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  • Monocular Depth Estimation in Images and Sequences Using Occlusion Cues  Open access

     Palou Visa, Guillem
    Defense's date: 2014-02-21
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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    Quan els humans observen una escena, son capaços de distingir perfectament les parts que la composen i organitzar-lesespacialment per tal de poder-se orientar. Els mecanismes que governen la percepció visual han estat estudiats des delsprincipis de la neurociència, però encara no es coneixen tots els processos biològic que hi prenen part. En situacions normals,els humans poden fer servir tres eines per estimar l'estructura de l'escena. La primera és l'anomenada divergència. Aprofita l'úsde dos punts de vista (els dos ulls) i és capaç de determinar molt acuradament la posició dels objectes ,que a una distància defins a cent metres, romanen enfront de l'observador. A mesura que augmenta la distància o els objectes no es troben en el campde visió dels dos ulls, altres mecanismes s'han d'utilitzar. Tant l'experiència anterior com certs indicis visuals s'utilitzen enaquests casos i, encara que la seva precisió és menor, els humans aconsegueixen quasibé sempre interpretar bé el seu entorn.Els indicis visuals que aporten informació de profunditat més coneguts i utilitzats són, per exemple, la perspectiva, les oclusionso el tamany de certs objectes. L'experiència anterior permet resoldre situacions vistes anteriorment com ara saber quins regionscorresponen al terra, al cel o a objectes.Durant els últim anys, quan la tecnologia ho ha permès, s'han intentat dissenyar sistemes que interpretessin automàticamentdiferents tipus d'escena. En aquesta tesi s'aborda el tema de l'estimació de la profunditat utilitzant només un punt de vista iindicis visuals d'oclusió. L'objectiu del treball es la detecció d'aquests indicis i combinar-los amb un sistema de segmentació pertal de generar automàticament els diferents plans de profunditat presents a una escena. La tesi explora tant situacionsestàtiques (imatges fixes) com situacions dinàmiques, com ara trames dins de seqüències de vídeo o seqüències completes. Enel cas de seqüències completes, també es proposa un sistema automàtic per reconstruir l'estructura de l'escena només ambinformació de moviment. Els resultats del treball son prometedors i competitius amb la literatura del moment, però mostrenencara que la visió per computador té molt marge de millora respecte la presició dels humans.

    When humans observe a scene, they are able to perfectly distinguish the different parts composing it. Moreover, humans can easily reconstruct the spatial position of these parts and conceive a consistent structure. The mechanisms involving visual perception have been studied since the beginning of neuroscience but, still today, not all the processes composing it are known. In usual situations, humans can make use of three different methods to estimate the scene structure. The first one is the so called divergence and it makes use of both eyes. When objects lie in front of the observed at a distance up to hundred meters, subtle differences in the image formation in each eye can be used to determine depth. When objects are not in the field of view of both eyes, other mechanisms should be used. In these cases, both visual cues and prior learned information can be used to determine depth. Even if these mechanisms are less accurate than divergence, humans can almost always infer the correct depth structure when using them. As an example of visual cues, occlusion, perspective or object size provide a lot of information about the structure of the scene. A priori information depends on each observer, but it is normally used subconsciously by humans to detect commonly known regions such as the sky, the ground or different types of objects. In the last years, since technology has been able to handle the processing burden of vision systems, there has been lots of efforts devoted to design automated scene interpreting systems. In this thesis we address the problem of depth estimation using only one point of view and using only occlusion depth cues. The thesis objective is to detect occlusions present in the scene and combine them with a segmentation system so as to generate a relative depth order depth map for a scene. We explore both static and dynamic situations such as single images, frame inside sequences or full video sequences. In the case where a full image sequence is available, a system exploiting motion information to recover depth structure is also designed. Results are promising and competitive with respect to the state of the art literature, but there is still much room for improvement when compared to human depth perception performance.

    Quan els humans observen una escena, son capaços de distingir perfectament les parts que la composen i organitzar-les espacialment per tal de poder-se orientar. Els mecanismes que governen la percepció visual han estat estudiats des dels principis de la neurociència, però encara no es coneixen tots els processos biològic que hi prenen part. En situacions normals, els humans poden fer servir tres eines per estimar l’estructura de l’escena. La primera és l’anomenada divergència. Aprofita l’ús de dos punts de vista (els dos ulls) i és capaç¸ de determinar molt acuradament la posició dels objectes ,que a una distància de fins a cent metres, romanen enfront de l’observador. A mesura que augmenta la distància o els objectes no es troben en el camp de visió dels dos ulls, altres mecanismes s’han d’utilitzar. Tant l’experiència anterior com certs indicis visuals s’utilitzen en aquests casos i, encara que la seva precisió és menor, els humans aconsegueixen quasi bé sempre interpretar bé el seu entorn. Els indicis visuals que aporten informació de profunditat més coneguts i utilitzats són per exemple, la perspectiva, les oclusions o el tamany de certs objectes. L’experiència anterior permet resoldre situacions vistes anteriorment com ara saber quins regions corresponen al terra, al cel o a objectes. Durant els últims anys, quan la tecnologia ho ha permès, s’han intentat dissenyar sistemes que interpretessin automàticament diferents tipus d’escena. En aquesta tesi s’aborda el tema de l’estimació de la profunditat utilitzant només un punt de vista i indicis visuals d’oclusió. L’objectiu del treball es la detecció d’aquests indicis i combinar-los amb un sistema de segmentació per tal de generar automàticament els diferents plans de profunditat presents a una escena. La tesi explora tant situacions estàtiques (imatges fixes) com situacions dinàmiques, com ara trames dins de seqüències de vídeo o seqüències completes. En el cas de seqüències completes, també es proposa un sistema automàtic per reconstruir l’estructura de l’escena només amb informació de moviment. Els resultats del treball son prometedors i competitius amb la literatura del moment, però mostren encara que la visió per computador té molt marge de millora respecte la precisió dels humans.

  • Hierarchical information representation and efficient classification of gene expression microarray data  Open access

     Bosio, Mattia
    Defense's date: 2014-06-27
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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    En el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras.La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filtrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula.El objetivo de esta tesis es mejorar el estado del arte en la clasificación de microarrays y contribuir a entender como se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasificación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmos desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables.La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con fi ensembles". El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza ¿ensembles" aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays.El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados.

    In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and eficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet. Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process. This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk. The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers. In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions. All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art. Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.

    En el campo de la biología computacional, los microarrays son utilizados para medir la actividad de miles de genes a la vez y producir una representación global de la función celular. Los microarrays permiten analizar la expresión de muchos genes en un solo experimento, rápidamente y eficazmente. Aunque los microarrays sean una tecnología de investigación consolidada hoy en día y la tendencia es en utilizar nuevas tecnologías como Next Generation Sequencing (NGS), aun no se ha encontrado un método óptimo para la clasificación de muestras. La clasificación de muestras de microarray es una tarea complicada, debido al alto número de variables y a la falta de estructura entre los datos. Esta característica impide la aplicación de técnicas de procesado que se basan en relaciones estructurales, como el filtrado con wavelet u otras técnicas de filltrado. Por otro lado, los genes no se expresen independientemente unos de otros: los genes están inter-relacionados según el proceso biológico que les regula. El objetivo de esta tesis es mejorar el estado del arte en la clasi cación de microarrays y contribuir a entender cómo se pueden diseñar y aplicar técnicas de procesado de señal para analizar microarrays. El objetivo de construir un algoritmo de clasi cación, necesita un estudio de comprobaciones y adaptaciones de algoritmos existentes a los datos analizados. Los algoritmo desarrollados en esta tesis encaran el problema con dos bloques esenciales. El primero ataca la falta de estructura, derivando un árbol binario usando herramientas de clustering no supervisado. El segundo elemento fundamental para obtener clasificadores precisos reduciendo el riesgo de overfitting es un elemento de selección de variables. La principal tarea en esta tesis es la clasificación de datos binarios en la cual hemos obtenido mejoras relevantes al estado del arte. El primer paso es la generación de una estructura, para eso se ha utilizado el algoritmo Treelets disponible en la literatura. Múltiples alternativas a este algoritmo original han sido propuestas y evaluadas, cambiando las métricas de similitud o las reglas de fusión durante el proceso. Además, se ha estudiado la posibilidad de usar fuentes de información externas, como ontologías de información biológica, para mejorar la inferencia de la estructura. Se han estudiado dos enfoques diferentes para la selección de variables: el primero es una modificación del algoritmo IFFS y el segundo utiliza un esquema de aprendizaje con “ensembles”. El algoritmo IFFS ha sido adaptado a las características de microarrays para obtener mejores resultados, añadiendo elementos como la medida de fiabilidad y un sistema de evaluación para seleccionar la mejor variable en cada iteración. El método que utiliza “ensembles” aprovecha la abundancia de features de los microarrays para implementar una selección diferente. En este campo se han estudiado diferentes algoritmos, mejorando alternativas ya existentes al escaso número de muestras y al alto número de variables, típicos de los microarrays. El problema de clasificación con más de dos clases ha sido también tratado al estudiar un nuevo algoritmo que combina múltiples clasificadores binarios. El algoritmo propuesto aprovecha la redundancia ofrecida por múltiples clasificadores para obtener predicciones más fiables. Todos los algoritmos propuestos en esta tesis han sido evaluados con datos públicos y de alta calidad, siguiendo protocolos establecidos en la literatura para poder ofrecer una comparación fiable con el estado del arte. Cuando ha sido posible, se han aplicado simulaciones Monte Carlo para mejorar la robustez de los resultados.

  • Depth order estimation for video frames using motion occlusions

     Palou, Guillem; Salembier Clairon, Philippe Jean
    IET computer vision
    Date of publication: 2014-04-01
    Journal article

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    This study proposes a system to estimate the depth order of regions belonging to a monocular image sequence. For each frame, the regions are ordered according to their relative depth using information from the previous and following frames. The algorithm estimates occlusions relying on a hierarchical region-based representation of the image by means of a binary tree. This representation is used to define the final depth order partition which is obtained through an energy minimisation process. Finally, to achieve a global and consistent depth ordering, a depth order graph is constructed and used to eliminate contradictory local cues. The system is evaluated and compared with the state-of-the-art figure/ground labelling systems showing very good results.

    This study proposes a system to estimate the depth order of regions belonging to a monocular image sequence. For each frame, the regions are ordered according to their relative depth using information from the previous and following frames. The algorithm estimates occlusions relying on a hierarchical region-based representation of the image by means of a binary tree. This representation is used to define the final depth order partition which is obtained through an energy minimisation process. Finally, to achieve a global and consistent depth ordering, a depth order graph is constructed and used to eliminate contradictory local cues. The system is evaluated and compared with the state-of-the-art figure/ground labelling systems showing very good results.

  • PolSAR time series processing with Binary Partition Trees

     Alonso González, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    IEEE transactions on geoscience and remote sensing
    Date of publication: 2014-06-01
    Journal article

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    This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatio-temporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

    This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatio-temporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

  • Bilateral distance based filtering for polarimetric SAR data

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean; Deng, Xinping
    Remote Sensing
    Date of publication: 2013-10-30
    Journal article

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    This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset.

  • Monocular depth ordering using T-Junctions and convexity occlusion cues

     Palou Visa, Guillem; Salembier Clairon, Philippe Jean
    IEEE transactions on image processing
    Date of publication: 2013
    Journal article

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  • PolSAR time series processing and analysis based on Binary Partition Trees

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry
    Presentation's date: 2013-02-12
    Presentation of work at congresses

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  • Hierarchical video representation with trajectory binary partition tree

     Palou Visa, Guillem; Salembier Clairon, Philippe Jean
    IEEE Conference on Computer Vision and Pattern Recognition
    Presentation's date: 2013-06-20
    Presentation of work at congresses

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    As early stage of video processing, we introduce an iter- ative trajectory merging algorithm that produces a region- based and hierarchical representation of the video se- quence, called the Trajectory Binary Partition Tree (BPT). From this representation, many analysis and graph cut tech- niques can be used to extract partitions or objects that are useful in the context of specific applications. In order to define trajectories and to create a precise merging algorithm, color and motion cues have to be used. Both types of informations are very useful to characterize objects but present strong differences of behavior in the spa- tial and the temporal dimensions. On the one hand, scenes and objects are rich in their spatial color distributions, but these distributions are rather stable over time. Object mo- tion, on the other hand, presents simple structures and low spatial variability but may change from frame to frame. The proposed algorithm takes into account this key difference and relies on different models and associated metrics to deal with color and motion information. We show that the proposed algorithm outperforms existing hierarchical video segmentation algorithms and provides more stable and pre- cise regions

  • Segmentation hyperspectrale de forets tropicales par arbres de partition binaires

     Tochon, Guillaume; Feret, J.B.; Valero Valbuena, Silvia; Martin, R.E.; Tupayachi, R.; Chanussot, Jocelyn; Salembier Clairon, Philippe Jean; Asner, G.P.
    Revue Francaise de Photogrammetrie et de Teledetection
    Date of publication: 2013-05-01
    Journal article

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    La segmentation d¿images de forêts tropicales est un outil important pour faciliter le travail des écologues. Dans ce papier, nous proposons une nouvelle méthode de segmentation pour les images hyperspectrales, basée sur la construction d¿un arbre de partition binaire (APB). Nous introduisons tout d¿abord une étape de prétraitement combinant une analyse en composantes principales et la définition de cartes de pré-segmentation, afin de réduire spatialement et spectralement le volume de données à traiter. La construction de l¿APB nécessite la définition d¿un modèle de région statistique non-paramétrique s¿appuyant sur des histogrammes, ainsi qu¿un critère de fusion fondé sur la distance de diffusion. Nous introduisons également une stratégie d¿élagage de l¿APB, adaptée spécifiquement à la segmentation de couronnes d¿arbres en forêts tropicales. Pour finir, nous présentons certains critères permettant d¿évaluer la qualité de la segmentation finale, basés sur le décompte du nombre de couronnes de référence correctement segmentées. La méthode proposée est validée sur deux jeux de données issues de campagnes aéroportées à Hawaii et Panama, respectivement, avec des résolutions spectrales et spatiales différentes.

    La segmentation d’images de forêts tropicales est un outil important pour faciliter le travail des écologues. Dans ce papier, nous proposons une nouvelle méthode de segmentation pour les images hyperspectrales, basée sur la construction d’un arbre de partition binaire (APB). Nous introduisons tout d’abord une étape de prétraitement combinant une analyse en composantes principales et la définition de cartes de pré-segmentation, afin de réduire spatialement et spectralement le volume de données à traiter. La construction de l’APB nécessite la définition d’un modèle de région statistique non-paramétrique s’appuyant sur des histogrammes, ainsi qu’un critère de fusion fondé sur la distance de diffusion. Nous introduisons également une stratégie d’élagage de l’APB, adaptée spécifiquement à la segmentation de couronnes d’arbres en forêts tropicales. Pour finir, nous présentons certains critères permettant d’évaluer la qualité de la segmentation finale, basés sur le décompte du nombre de couronnes de référence correctement segmentées. La méthode proposée est validée sur deux jeux de données issues de campagnes aéroportées à Hawaii et Panama, respectivement, avec des résolutions spectrales et spatiales différentes.

  • Hierarchical clustering combining numerical and biological similarities for gene expression data classification

     Bosio, Mattia; Salembier Clairon, Philippe Jean; Bellot Pujalte, Pau; Oliveras Verges, Albert
    IEEE Engineering in Medicine and Biology Society
    Presentation's date: 2013-07
    Presentation of work at congresses

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    High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.

  • Object recognition in urban hyperspectral images using binary partition tree representation

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    IEEE International Geoscience and Remote Sensing Symposium
    Presentation's date: 2013-07-18
    Presentation of work at congresses

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    In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

    In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. The BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. Experimental results demonstrate the good performances of this BPT-based approach.

  • Ensemble learning and hierarchical data representation for microarray classification

     Bosio, Mattia; Bellot, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Conference on Bioinformatics and Bioengineering
    Presentation's date: 2013-11-11
    Presentation of work at congresses

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    The microarray data classification is an open and active research field. The development of more accurate algorithms is of great interest and many of the developed techniques can be straightforwardly applied in analyzing different kinds of omics data. In this work, an ensemble learning algorithm is applied within a classification framework that already got good predictive results. Ensemble techniques take individual experts, (i.e. classifiers), to combine them to improve the individual expert results with a voting scheme. In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote. Two versions of a state of the art ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC. The proposed key elements have shown to be useful for the prediction performance and the studied ensemble technique shown to improve the state of the art results by producing classifiers with better predictions.

    The microarray data classification is an open and active research field. The development of more accurate algorithms is of great interest and many of the developed techniques can be straightforwardly applied in analyzing different kinds of omics data. In this work, an ensemble learning algorithm is applied within a classification framework that already got good predictive results. Ensemble techniques take individual experts, (i.e. classifiers), to combine them to improve the individual expert results with a voting scheme. In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote. Two versions of a state of the art ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC. The proposed key elements have shown to be useful for the prediction performance and the studied ensemble technique shown to improve the state of the art results by producing classifiers with better predictions.

  • Processing multidimensional SAR and hyperspectral images with binary partition tree

     Alonso Gonzalez, Alberto; Valero Valbuena, Silvia; Chanussot, Jocelyn; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    Proceedings of the IEEE
    Date of publication: 2012-08-13
    Journal article

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    The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many prac tical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations. In this paper, we discuss the inter est of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classifica- tion will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representa- tion will be highlighted

  • Hyperspectral image representation and processing with binary partition trees

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    IEEE transactions on image processing
    Date of publication: 2012-12-04
    Journal article

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    The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

  • Gene expression data classification combining hierarchical representation and efficient feature selection

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    Journal of biological systems
    Date of publication: 2012-12
    Journal article

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    A general framework for microarray data classification is proposed in this paper. It pro- duces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the com- mon behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of func- tionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process. The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). The comparative results showed that the proposed framework can compete with a wide variety of state of the art alternatives and it can obtain the best mean performance if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed framework obtains stable and repeatable results.

  • Distance-based measures of association with applications in relating hyperspectral images

     Cuadras Avellana, Carles M.; Valero Valbuena, Silvia; Cuadras, Daniel; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    Communications in statistics. Simulation and computation
    Date of publication: 2012-07-01
    Journal article

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  • Filtering and segmentation of polarimetric SAR data based on binary partition trees

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    IEEE transactions on geoscience and remote sensing
    Date of publication: 2012-02
    Journal article

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    In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed.When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system.

  • From local occlusion cues to global monocular depth estimation

     Palou Visa, Guillem; Salembier Clairon, Philippe Jean
    IEEE International Conference on Acoustics, Speech, and Signal Processing
    Presentation's date: 2012-03-14
    Presentation of work at congresses

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    In this paper, we propose a system to obtain a depth ordered seg- mentation of a single image based on low level cues. The algorithm first constructs a hierarchical, region-based image representation of the image using a Binary Partition Tree (BPT). During the building process, T-junction depth cues are detected, along with high convex boundaries. When the BPT is built, a suitable segmentation is found and a global depth ordering is found using a probabilistic framework. Results are compared with state of the art depth ordering and figure/ground labeling systems. The advantage of the proposed ap- proach compared to systems based on a training procedure is the lack of assumptions about the scene content. Moreover, it is shown that the system outperforms previously low-level cue based systems, while offering similar results to a priori trained figure/ground label- ing algorithms

  • Microarray classification with hierarchical data representation and novel feature selection criteria

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Conference on BioInformatics and BioEngineering
    Presentation's date: 2012-11-12
    Presentation of work at congresses

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    Microarray data classification is a challenging problem due to the high number of variables compared to the small number of available samples. An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1]. It considers the sample scarcity problem and the lack of data structure typical of microarrays. Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes and introducing a novel feature ranking criterion, inside the wrapper feature selection task. The classification ability has been evaluated on 4 publicly available datasets from Micro Array Quality Control study phase II (MAQC) classified by 7 different endpoints. The global results have showed how the proposed approach obtains better prediction accuracy than a wide variety of state of the art alternatives.

    Microarray data classification is a challenging prob- lem due to the high number of variables compared to the small number of available samples. An effective methodology to output a precise and reliable classifier is proposed in this work as an improvement of the algorithm in [1]. It considers the sample scarcity problem and the lack of data structure typical of microarrays. Both problem are assessed by a two-step approach applying hierarchical clustering to create new features called metagenes and introducing a novel feature ranking criterion, inside the wrapper feature selection task. The classification ability has been evaluated on 4 publicly available datasets from Micro Array Quality Control study phase II (MAQC) classified by 7 different endpoints. The global results have showed how the proposed approach obtains better prediction accuracy than a wide variety of state of the art alternatives

  • Depth estimation of frames in image sequences using motion occlusions

     Palou Visa, Guillem; Salembier Clairon, Philippe Jean
    European Conference on Computer Vision
    Presentation's date: 2012
    Presentation of work at congresses

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    This paper proposes a system to depth order regions of a frame belonging to a monocular image sequence. For a given frame, re- gions are ordered according to their relative depth using the previous and following frames. The algorithm estimates occluded and disoccluded pixels belonging to the central frame. Afterwards, a Binary Partition Tree (BPT) is constructed to obtain a hierarchical, region based repre- sentation of the image. The nal depth partition is obtained by means of energy minimization on the BPT. To achieve a global depth ordering from local occlusion cues, a depth order graph is constructed and used to eliminate contradictory local cues. Results of the system are evaluated and compared with state of the art gure/ground labeling systems on several datasets, showing promising results.

  • Registration of multi-modal neuroimaging datasets by considering the non-overlapping field of view into the NMI calculation

     Jiménez, Xavi; Figueiras, Francisca; Marques Acosta, Fernando; Salembier Clairon, Philippe Jean; Herance, Raúl; Rojas, Santi; Millan, Olga; Pareto, Deborah; Domingo Gispert, Juan
    IEEE International Symposium on Biomedical Imaging
    Presentation's date: 2012-05-02
    Presentation of work at congresses

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  • Variable local weight filtering for PolSAR data speckle noise reduction

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    IEEE International Geoscience and Remote Sensing Symposium
    Presentation's date: 2012-07
    Presentation of work at congresses

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    This paper presents a Polarimetric SAR data speckle filtering technique, based on a combined filtering in the spatial and polarimetric domains. It is based on a bilateral filtering employing distance measures over these domains. These measures concentrate all the information related to the domain structure that is needed for an adaptation to the scene morphology. A weighted average is performed over a given window favoring closer and similar pixels. As a consequence, an adaptive filtering is achieved, attaining higher filtering over homogeneous areas whereas point scatters remain almost unchanged. Results will be shown over a real RADARSAT-2 data.

  • Temporal PolSAR image series exploitation with binary partition trees

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    IEEE International Geoscience and Remote Sensing Symposium
    Presentation's date: 2012-07
    Presentation of work at congresses

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    In this paper, the processing of temporal PolSAR image series is addressed through a region-based and multi-scale data representation, the Binary Partition Tree (BPT). This structure contains useful information related to the data structure at different detail levels that may be employed for different applications. The construction of this structure ans its exploitation is addressed in this work in the context of the speckle filtering and data segmentation applications. A new region model and processing strategy are defined to tackle with the temporal dimension of the data. Finally, to illustrate the capabilities of the proposed technique, results are shown with a real RADARSAT-2 dataset.

  • Depth ordering on image sequences using motion occlusions

     Palou Visa, Guillem; Salembier Clairon, Philippe Jean
    IEEE International Conference on Image Processing
    Presentation's date: 2012-10-01
    Presentation of work at congresses

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    This paper proposes a system to obtain the depth order of frames in image sequences using motion occlusion cues. The system first computes the forward and backward flows with the previous and next frames and estimates the occluded points. To obtain a region representation of the image, a Binary Partition Tree (BPT) is created for each frame. To estimate occlusion relations in the image, projective flow models are fitted to all regions in the image. The depth order solution is obtained by minimizing over the tree structure a cost function based on occlusion relations and the number of regions. Results show that optical flow algorithms can be used directly to estimate occlusion points. Promising results are obtained combining motion occlusions and region information by means of a BPT. Evaluation is performed comparing current state-of-the-art algorithms on figure/ground assignments, showing that the performance of the proposed system is comparable to current algorithms.

  • Multiclass cancer-microarray classification algorithm with Pair-Against-All redundancy

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Workshop on Genomic Signal Processing and Statistic
    Presentation's date: 2012-12-03
    Presentation of work at congresses

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    Multiclass cancer classification is still a challenging task in the field of machine learning. A novel multiclass approach is proposed in this work as a combination of multiple binary classifiers. It is an example of Error Correcting Output Codes algorithms, applying data transmission coding techniques to improve the classification as a combination of binary classifiers. The proposed method combines the One Against All, OAA, approach with a set of classifiers separating each class-pair from the rest, called Pair Against All, PAA. The OAA+PAA approach has been tested on seven publicly available datasets. It has been compared with the common OAA approach and with state of the art alternatives. The obtained results showed how the OAA+PAA algorithm consistently improves the OAA results, unlike other ECOC algorithms presented in the literature.

  • Best Paper award of the IEEE International Geosciencie and Remote Sensing Symposium

     Salembier Clairon, Philippe Jean; Valero Valbuena, Silvia
    Award or recognition

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  • Neighborhood filters and the recovery of 3D information

     Digne, Julie; Dimiccoli, Mariella; Salembier Clairon, Philippe Jean; Sabater, Neus
    Date of publication: 2011
    Book chapter

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  • Access to the full text
    Edge enhancement algorithm based on the wavelet transform for automatic edge detection in SAR images  Open access

     Tello Alonso, Marivi; López Martínez, Carlos; Mallorqui Franquet, Jordi Joan; Salembier Clairon, Philippe Jean
    IEEE transactions on geoscience and remote sensing
    Date of publication: 2011-01-01
    Journal article

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    This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed.

  • Procesado de vídeo multicámara empleando información de la escena: aplicación a eventos deportivos, interacción visual y 3DTV

     Giro Nieto, Xavier; Oliveras Verges, Albert; Gasull Llampallas, Antoni; Salembier Clairon, Philippe Jean; Marques Acosta, Fernando; Sayrol Clols, Elisa; Pardas Feliu, Montserrat; Morros Rubió, Josep Ramon; Ruiz Hidalgo, Javier; Vilaplana Besler, Veronica; Casas Pla, Josep Ramon
    Participation in a competitive project

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  • Fellow of IEEE

     Salembier Clairon, Philippe Jean
    Award or recognition

     Share

  • Best paper award of IEEE International Geoscience and Remote Sensing Symposium

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    Award or recognition

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  • PolSAR speckle filtering and segmentation based on binary partition tree representation

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry
    Presentation's date: 2011-01
    Presentation of work at congresses

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  • HYPERSPECTRAL IMAGE REPRESENTATION AND PROCESSING USING BINARY PARTITION TREES  Open access

     Valero Valbuena, Silvia
    Defense's date: 2011-12-09
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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    The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, under the title Hyperspectral image representation and Processing with Binary Partition Trees, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the Binary Partition Tree succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. As a matter of fact, the very high dimensionality and the complexity of the data require the definition of specific region models and similarity measures. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation

  • Feature set enhancement via hierarchical clustering for microarray classification

     Bosio, Mattia; Bellot Pujalte, Pau; Salembier Clairon, Philippe Jean; Oliveras Verges, Albert
    IEEE International Workshop on Genomic Signal Processing and Statistics
    Presentation's date: 2011
    Presentation of work at congresses

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  • Occlusion-based depth ordering on monocular images with binary partition tree

     Palou Visa, Guillem; Salembier Clairon, Philippe Jean
    IEEE International Conference on Acoustics, Speech and Signal Processing
    Presentation's date: 2011-05
    Presentation of work at congresses

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    This paper proposes a system to relate objects in an image using occlusion cues and arrange them according to depth. The system does not rely on any a priori knowledge of the scene structure and focuses on detecting specific points, such as T-junctions, to infer the depth relationships between objects in the scene. The system makes extensive use of the Binary Partition Tree (BPT) as the segmentation tool jointly with a new approach for T-junction estimation. Following a bottom-up strategy, regions (initially individual pixels) are iteratively merged until only one region is left. At each merging step, the system estimates the probability of observing a T-junction which is a cue of occlusion when three regions meet. When the BPT is constructed and the pruning is performed, this information is used for depth ordering. Although the proposed system only relies on one low-level depth cue and does not involve any learning process, it shows similar performances than the state of the art.

  • Arbre de partition binaire: un nouvel outil pour la représentation hiérarchique et l¿analyse des images hyperspectrales

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    Colloque sur le Traitement du Signal et des Images
    Presentation's date: 2011-09-05
    Presentation of work at congresses

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    This problem discusses here is the hierarchical representation and processing of the hyperspectral imaging. In this framework, Binary Partition Trees (BPTs) are proposed as new hierarchical region-based representation. Based on region merging techniques, the work presented here proposes a strategy for merging hyperspectral regions using a new association measure depending on canonical correlations relating principal coordinates. Once is BPT constructed, this representation can be used for many applications including ltering, segmentation and classi cation.To demonstrate an example of BPT usefulness, a pruning strategy aiming at object detection is discussed. Experimental results demonstrate the good performances of BPT.

  • Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees

     Benediktsson, Joan A; Bruzzone, Lorenzo; Chanussot, Jocelyn; Dalla Mura, Mauro; Salembier Clairon, Philippe Jean; Valero Valbuena, Silvia
    International Symposium on Mathematical Morphology
    Presentation's date: 2011-07-07
    Presentation of work at congresses

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    The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrows challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.

  • Improved binary partition tree construction for hyperspectral images: application to object detection

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn; Cuadres, Carles
    IEEE International Geoscience and Remote Sensing Symposium
    Presentation's date: 2011-07-27
    Presentation of work at congresses

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    This paper discusses hierarchical region-based representation using Binary Partition Tree in the framework of hyperspectral data. Based on region merging techniques, this region-based representation reduces the number of elementary primitives compared to the pixel based representation and allows a more robust filtering, segmentation, classification or information retrieval. The work presented here proposes a strategy for merging hyperspectral regions using a new association measure depending on canonical correlations relating principal coordinates. To demonstrate an example of BPT usefulness, a pruning strategy aiming at object detection is discussed. Experimental results demonstrate the good performances of BPT.

  • Binary partition tree as a polarimetric SAR data representation in the space-time domain

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    IEEE International Geoscience and Remote Sensing Symposium
    Presentation's date: 2011-07
    Presentation of work at congresses

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    The aim of this paper is to present a Polarimetric Synthetic Aperture Radar data processing technique on the space-time domain. This approach is based on a Binary Partition Tree (BPT), which is a region-based and multi-scale data representation. Results with series of RADARSAT-2 real data are analyzed from the point of view of speckle filtering and change detection applications, to illustrate the capabilities to detect and preserve spatial and temporal contours.

  • Hyperspectral image segmentation using binary partition trees

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    IEEE International Conference on Image Processing
    Presentation's date: 2011-09-12
    Presentation of work at congresses

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    The work presented here proposes a new Binary Partition Tree pruning strategy aimed at the segmentation of hyperspectral images. The BPT is a region-based representation of images that involves a reduced number of elementary primitives and therefore allows to design a robust and efficient segmentation algorithm. Here, the regions contained in the BPT branches are studied by recursive spectral graph partitioning. The goal is to remove subtrees composed of nodes which are considered to be similar. To this end, affinity matrices on the tree branches are computed using a new distance-based measure depending on canonical correlations relating principal coordinates. Experimental results have demonstrated the good performances of BPT construction and pruning.

  • Adquisición multicámara para Free Viewpoint Video (MC4FVV)

     Pardas Feliu, Montserrat; Giro Nieto, Xavier; Vilaplana Besler, Veronica; Ruiz Hidalgo, Javier; Morros Rubió, Josep Ramon; Salembier Clairon, Philippe Jean; Marques Acosta, Fernando; Gasull Llampallas, Antoni; Oliveras Verges, Albert; Sayrol Clols, Elisa; Casas Pla, Josep Ramon
    Participation in a competitive project

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  • ICT-248138- Fascinate - Format-Agnostic SCript-based INterAcTive Experience

     Salembier Clairon, Philippe Jean
    Participation in a competitive project

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  • Compression

     Marcotegui, Beatriz; Salembier Clairon, Philippe Jean
    Date of publication: 2010-09-01
    Book chapter

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  • Connected operators based on tree pruning strategies

     Salembier Clairon, Philippe Jean
    Date of publication: 2010-06-01
    Book chapter

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  • Compression

     Marcotegui, Beatriz; Salembier Clairon, Philippe Jean
    Date of publication: 2010-06-01
    Book chapter

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  • Generalized lifting for sparse image representation and coding

     Rolon Garrido, Julio Cesar
    Defense's date: 2010-01-25
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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  • Some measures of multivariate association relating two spectral data sets

     Cuadras, C.M.; Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    COMPSTAT International Conference on Computational Statistics
    Presentation's date: 2010-12-22
    Presentation of work at congresses

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  • Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data

     Valero Valbuena, Silvia; Salembier Clairon, Philippe Jean; Chanussot, Jocelyn
    IEEE International Conference on Image Processing
    Presentation's date: 2010-09-28
    Presentation of work at congresses

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  • Access to the full text
    Filtering and segmentation of polarimetric SAR images with binary partition trees  Open access

     Alonso Gonzalez, Alberto; López Martínez, Carlos; Salembier Clairon, Philippe Jean
    IEEE International Geoscience and Remote Sensing Symposium
    Presentation's date: 2010-07
    Presentation of work at congresses

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    A new multi-scale PolSAR data filtering technique, based on a Binary Partition Tree (BPT) representation of the data, is proposed. Different alternatives for the construction and the exploitation of the BPT for filtering and segmentation are presented. Results with simulated and experimental PolSAR data are presented to shown the capabilities of the BPT-filtering strategy to maintain both spatial details and the polarimetric information.

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    Improved local pdf estimation in the wavelet domain for generalized lifting  Open access

     Rolon Garrido, Julio Cesar; Salembier Clairon, Philippe Jean
    Picture Coding Symposium
    Presentation's date: 2010-12-08
    Presentation of work at congresses

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    Generalized Lifting (GL) has been studied for lossy image compression in [2,3]. It has been demonstrated that the method leads to a significant reduction of the wavelet coefficients energy and entropy. The definition of the GL relies on an estimation of the pdf of the pixel to encode conditioned to a surrounding context. The objective of this paper is to present an improved method for the estimation of the pdf at the local level. Rather than assuming that the local pdf is monomodal, symmetric, and centered at the central value of the best context match within a neighborhood, as in [3], we follow the idea of self similarity proposed in [1] for denoising, and propose to estimate the pdf using all the causal contexts within a window. Therefore, all the available knowledge about the neighborhood is incorporated. No assumptions about the characteristics of the pdf are made. A generalized lifting operator that minimizes the energy is applied to each context during the encoding process. Experimental results show an important increment in the energy and entropy gains when compared to previous strategies [2,3].