Moreno Noguer, Francesc d'Assis
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  • Fast online learning and detection of natural landmarks for autonomous aerial robots

     Villamizar Vergel, Michael Alejandro; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    IEEE International Conference on Robotics and Automation
    p. 4996-5003
    DOI: 10.1109/ICRA.2014.6907591
    Presentation's date: 2014
    Presentation of work at congresses

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    We present a method for efficiently detecting natural landmarks that can handle scenes with highly repetitive patterns and targets progressively changing its appearance. At the core of our approach lies a Random Ferns classifier, that models the posterior probabilities of different views of the target using multiple and independent Ferns, each containing features at particular positions of the target. A Shannon entropy measure is used to pick the most informative locations of these features. This minimizes the number of Ferns while maximizing its discriminative power, allowing thus, for robust detections at low computational costs. In addition, after offline initialization, the new incoming detections are used to update the posterior probabilities on the fly, and adapt to changing appearances that can occur due to the presence of shadows or occluding objects. All these virtues, make the proposed detector appropriate for UAV navigation. Besides the synthetic experiments that will demonstrate the theoretical benefits of our formulation, we will show applications for detecting landing areas in regions with highly repetitive patterns, and specific objects under the presence of cast shadows or sudden camera motions.

  • On-board real-time pose estimation for UAVs using deformable visual contour registration

     Amor Martinez, Adrian; Ruiz, Alberto; Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto
    IEEE International Conference on Robotics and Automation
    p. 2595-2601
    DOI: 10.1109/ICRA.2014.6907231
    Presentation's date: 2014
    Presentation of work at congresses

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    We present a real time method for pose estimation of objects from an UAV, using visual marks placed on non planar surfaces. It is designed to overcome constraints in small aerial robots, such as slow CPUs, low resolution cameras and image deformations due to distortions introduced by the lens or by the viewpoint changes produced during the flight navigation. The method consists of shape registration from extracted contours in an image. Instead of working with dense image patches or corresponding image features, we optimize a geometric alignment cost computed directly from the raw polygonal representations of the observed regions using efficient clipping algorithms. Moreover, instead of doing 2D image processing operations, the optimization is performed in the polygon representation space, allowing real-time projective matching. Deformation modes are easily included in the optimization scheme, allowing an accurate registration of different markers attached to curved surfaces using a single deformable prototype. As a result, the method achieves accurate object pose estimation precision in real-time, which is very important for interactive UAV tasks, for example for short distance surveillance or bar assembly. We describe the main algorithmic components of the method and present experiments where our method yields an average error of less than 5mm in position at a distance of 0.7m, using a visual mark of 19mm x 19mm. Finally, we compare these results with current computer vision state-of-the-art systems.

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    Segmentation-aware deformable part models  Open access

     Tsogkas, Stavros; Kokkinos, Iasonas; Trulls Fortuny, Eduard; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 168-175
    DOI: 10.1109/CVPR.2014.29
    Presentation's date: 2014
    Presentation of work at congresses

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    In this work we propose a technique to combine bottom- up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs). The merit of our approach lies in `cleaning up¿ the low-level HOG features by exploiting the spatial support of SLIC superpixels; this can be understood as using segmentation to split the feature variation into object-specific and background changes. Rather than committing to a single segmentation we use a large pool of SLIC superpixels and combine them in a scale-, position- and object-dependent manner to build soft segmentation masks. The segmentation masks can be computed fast enough to repeat this process over every candidate window, during training and detection, for both the root and part filters of DPMs. We use these masks to construct enhanced, background-invariant features to train DPMs. We test our approach on the PASCAL VOC 2007, outperforming the standard DPM in 17 out of 20 classes, yielding an average increase of 1.7% AP. Additionally, we demonstrate the robustness of this approach, extending it to dense SIFT descriptors for large displacement optical flow.

    In this work we propose a technique to combine bottom- up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs). The merit of our approach lies in ‘cleaning up’ the low-level HOG features by exploiting the spatial support of SLIC superpixels; this can be understood as using segmentation to split the feature variation into object-specific and background changes. Rather than committing to a single segmentation we use a large pool of SLIC superpixels and combine them in a scale-, position- and object-dependent manner to build soft segmentation masks. The segmentation masks can be computed fast enough to repeat this process over every candidate window, during training and detection, for both the root and part filters of DPMs. We use these masks to construct enhanced, background-invariant features to train DPMs. We test our approach on the PASCAL VOC 2007, outperforming the standard DPM in 17 out of 20 classes, yielding an average increase of 1.7% AP. Additionally, we demonstrate the robustness of this approach, extending it to dense SIFT descriptors for large displacement optical flow.

    Postprint (author’s final draft)

  • A joint model for 2D and 3D pose estimation from a single image

     Simo Serra, Edgar; Quattoni, Ariadna Julieta; Torras, Carme; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 3634-3641
    DOI: 10.1109/CVPR.2013.466
    Presentation's date: 2013-06-23
    Presentation of work at congresses

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    We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate. © 2013 IEEE.

    We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D poses when the feature detector has performed poorly. In this paper, we address this issue by jointly solving both the 2D detection and the 3D inference problems. For this purpose, we propose a Bayesian framework that integrates a generative model based on latent variables and discriminative 2D part detectors based on HOGs, and perform inference using evolutionary algorithms. Real experimentation demonstrates competitive results, and the ability of our methodology to provide accurate 2D and 3D pose estimations even when the 2D detectors are inaccurate.

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    Simultaneous pose, focal length and 2D-to-3D correspondences from noisy observations  Open access

     Peñate Sánchez, Adrián; Serradell, Eduard; Andrade Cetto, Juan; Moreno Noguer, Francesc d'Assis
    British Machine Vision Conference
    p. 82.1-82.11
    DOI: 10.5244/C.27.82
    Presentation's date: 2013
    Presentation of work at congresses

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    Simultaneously recovering the camera pose and correspondences between a set of 2D-image and 3D-model points is a difficult problem, especially when the 2D-3D matches cannot be established based on appearance only. The problem becomes even more challenging when input images are acquired with an uncalibrated camera with varying zoom, which yields strong ambiguities between translation and focal length. We present a solution to this problem using only geometrical information. Our approach owes its robustness to an initial stage in which the joint pose and focal length solution space is split into several Gaussian regions. At runtime, each of these regions is explored using an hypothesize-and-test approach, in which the potential number of 2D-3D matches is progressively reduced using informed search through Kalman updates, iteratively refining the pose and focal length parameters. The technique is exhaustive but efficient, significantly improving previous methods in terms of robustness to outliers and noise.

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    FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation  Open access

     Ramisa Ayats, Arnau; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Torras, Carme
    IEEE/RSJ International Conference on Intelligent Robots and Systems
    p. 824-830
    DOI: 10.1109/IROS.2013.6696446
    Presentation's date: 2013
    Presentation of work at congresses

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    Most current depth sensors provide 2.5D range images in which depth values are assigned to a rectangular 2D array. In this paper we take advantage of this structured information to build an efficient shape descriptor which is about two orders of magnitude faster than competing approaches, while showing similar performance in several tasks involving deformable object recognition. Given a 2D patch surrounding a point and its associated depth values, we build the descriptor for that point, based on the cumulative distances between their normals and a discrete set of normal directions. This processing is made very efficient using integral images, even allowing to compute descriptors for every range image pixel in a few seconds. The discriminative power of our descriptor, dubbed FINDDD, is evaluated in three different scenarios: recognition of specific cloth wrinkles, instance recognition from geometry alone, and detection of reliable and informed grasping points.

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    Proactive behavior of an autonomous mobile robot for human-assisted learning  Open access

     Garrell Zulueta, Anais; Villamizar Vergel, Michael Alejandro; Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto
    IEEE International Symposium on Robot and Human Interactive Communication
    p. 107-113
    DOI: 10.1109/ROMAN.2013.6628463
    Presentation's date: 2013
    Presentation of work at congresses

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    During the last decade, there has been a growing interest in making autonomous social robots able to interact with people. However, there are still many open issues regarding the social capabilities that robots should have in order to perform these interactions more naturally. In this paper we present the results of several experiments conducted at the Barcelona Robot Lab in the campus of the ¿Universitat Politècnica de Catalunya¿ in which we have analyzed different important aspects of the interaction between a mobile robot and non-trained human volunteers. First, we have proposed different robot behaviors to approach a person and create an engagement with him/her. In order to perform this task we have provided the robot with several perception and action capabilities, such as that of detecting people, planning an approach and verbally communicating its intention to initiate a conversation. Once the initial engagement has been created, we have developed further communication skills in order to let people assist the robot and improve its face recognition system. After this assisted and online learning stage, the robot becomes able to detect people under severe changing conditions, which, in turn enhances the number and the manner that subsequent human-robot interactions are performed.

    During the last decade, there has been a growing interest in making autonomous social robots able to interact with people. However, there are still many open issues regarding the social capabilities that robots should have in order to perform these interactions more naturally. In this paper we present the results of several experiments conducted at the Barcelona Robot Lab in the campus of the “Universitat Politècnica de Catalunya” in which we have analyzed different important aspects of the interaction between a mobile robot and non-trained human volunteers. First, we have proposed different robot behaviors to approach a person and create an engagement with him/her. In order to perform this task we have provided the robot with several perception and action capabilities, such as that of detecting people, planning an approach and verbally communicating its intention to initiate a conversation. Once the initial engagement has been created, we have developed further communication skills in order to let people assist the robot and improve its face recognition system. After this assisted and online learning stage, the robot becomes able to detect people under severe changing conditions, which, in turn enhances the number and the manner that subsequent human-robot interactions are performed.

    Postprint (author’s final draft)

  • Dense segmentation-aware descriptors

     Kokkinos, Iasonas; Trulls Fortuny, Eduard; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 2890-2897
    DOI: 10.1109/CVPR.2013.372
    Presentation's date: 2013
    Presentation of work at congresses

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    In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes. For this, we downplay measurements coming from areas that are unlikely to belong to the same region as the descriptor¿s center, as suggested by soft segmentation masks. Our treatment is applicable to any image point, i.e. dense, and its computational overhead is in the order of a few seconds. We integrate this idea with Dense SIFT, and also with Dense Scale and Rotation Invariant Descriptors (SID), delivering descriptors that are densely computable, invariant to scaling and rotation, and robust to background changes. We apply our approach to standard benchmarks on large displacement motion estimation using SIFT-flow and widebaseline stereo, systematically demonstrating that the introduction of segmentation yields clear improvements.

    In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes. For this, we downplay measurements coming from areas that are unlikely to belong to the same region as the descriptor’s center, as suggested by soft segmentation masks. Our treatment is applicable to any image point, i.e. dense, and its computational overhead is in the order of a few seconds. We integrate this idea with Dense SIFT, and also with Dense Scale and Rotation Invariant Descriptors (SID), delivering descriptors that are densely computable, invariant to scaling and rotation, and robust to background changes. We apply our approach to standard benchmarks on large displacement motion estimation using SIFT-flow and widebaseline stereo, systematically demonstrating that the introduction of segmentation yields clear improvements.

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    Active testing search for point cloud matching  Open access

     Amável Pinheiro, Miguel; Sznitman, Raphael; Serradell, Eduard; Kybic, Jan; Moreno Noguer, Francesc d'Assis; Fua, Pascal
    International Conference on Information Processing in Medical Imaging
    p. 572-583
    DOI: 10.1007/978-3-642-38868-2_48
    Presentation's date: 2013
    Presentation of work at congresses

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    We present a general approach for solving the point-cloud matching problem for the case of mildly nonlinear transformations. Our method quickly finds a coarse approximation of the solution by exploring a reduced set of partial matches using an approach to which we refer to as Active Testing Search (ATS). We apply the method to registration of graph structures by branching point matching. It is based solely on the geometric position of the points, no additional information is used nor the knowledge of an initial alignment. In the second stage, we use dynamic programming to rene the solution. We tested our algorithm on angiography, retinal fundus, and neuronal data gathered using electron and light microscopy. We show that our method solves cases not solved by most approaches, and is faster than the remaining ones.

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    Online human-assisted learning using random ferns  Open access

     Villamizar Vergel, Michael Alejandro; Garrell Zulueta, Anais; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    International Conference on Pattern Recognition
    p. 2821-2824
    Presentation's date: 2012
    Presentation of work at congresses

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    We present an Online Random Ferns (ORFs) classifier that progressively learns and builds enhanced models of object appearances. During the learning process, we allow the human intervention to assist the classifier and discard false positive training samples. The amount of human intervention is minimized and integrated within the online learning, such that in a few seconds, complex object appearances can be learned. After the assisted learning stage, the classifier is able to detect the object under severe changing conditions. The system runs at a few frames per second, and has been validated for face and object detection tasks on a mobile robot platform. We show that with minimal human assistance we are able to build a detector robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. 2012 ICPR Org Committee.

    We present an Online Random Ferns (ORFs) classifier that progressively learns and builds enhanced models of object appearances. During the learning process, we allow the human intervention to assist the classifier and discard false positive training samples. The amount of human intervention is minimized and integrated within the online learning, such that in a few seconds, complex object appearances can be learned. After the assisted learning stage, the classifier is able to detect the object under severe changing conditions. The system runs at a few frames per second, and has been validated for face and object detection tasks on a mobile robot platform. We show that with minimal human assistance we are able to build a detector robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds.

    Postprint (author’s final draft)

  • Spatiotemporal descriptor for wide-baseline stereo reconstruction of non-rigid and ambiguous scenes

     Trulls Fortuny, Eduard; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    European Conference on Computer Vision
    p. 441-454
    DOI: 10.1007/978-3-642-33712-3_32
    Presentation's date: 2012
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    This paper studies the use of temporal consistency to match appearance descriptors and handle complex ambiguities when computing dynamic depth maps from stereo. Previous attempts have designed 3D descriptors over the spacetime volume and have been mostly used for monocular action recognition, as they cannot deal with perspective changes. Our approach is based on a state-of-the-art 2D dense appearance descriptor which we extend in time by means of optical flow priors, and can be applied to wide-baseline stereo setups. The basic idea behind our approach is to capture the changes around a feature point in time instead of trying to describe the spatiotemporal volume. We demonstrate its effectiveness on very ambiguous synthetic video sequences with ground truth data, as well as real sequences.

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    Robust elastic 2D/3D geometric graph matching  Open access

     Serradell Domingo, Eduard; Kybic, J.; Moreno Noguer, Francesc d'Assis; Fua, Pascal
    Medical Imaging Conference
    p. 1-8
    DOI: 10.1117/12.910573
    Presentation's date: 2012
    Presentation of work at congresses

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    We present an algorithm for geometric matching of graphs embedded in 2D or 3D space. It is applicable for registering any graph-like structures appearing in biomedical images, such as blood vessels, pulmonary bronchi, nerve fibers, or dendritic arbors. Our approach does not rely on the similarity of local appearance features, so it is suitable for multimodal registration with a large difference in appearance. Unlike earlier methods, the algorithm uses edge shape, does not require an initial pose estimate, can handle partial matches, and can cope with nonlinear deformations and topological differences. The matching consists of two steps. First, we find an affine transform that roughly aligns the graphs by exploring the set of all consistent correspondences between the nodes. This can be done at an acceptably low computational expense by using parameter uncertainties for pruning, backtracking as needed. Parameter uncertainties are updated in a Kalman-like scheme with each match. In the second step we allow for a nonlinear part of the deformation, modeled as a Gaussian Process. Short sequences of edges are grouped into superedges, which are then matched between graphs. This allows for topological differences. A maximum consistent set of superedge matches is found using a dedicated branch-and-bound solver, which is over 100 times faster than a standard linear programming approach. Geometrical and topological consistency of candidate matches is determined in a fast hierarchical manner. We demonstrate the effectiveness of our technique at registering angiography and retinal fundus images, as well as neural image stacks.

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    Using depth and appearance features for informed robot grasping of highly wrinkled clothes  Open access

     Ramisa Ayats, Arnau; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Torras, Carme
    IEEE International Conference on Robotics and Automation
    p. 1703-1708
    DOI: 10.1109/ICRA.2012.6225045
    Presentation's date: 2012
    Presentation of work at congresses

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    Detecting grasping points is a key problem in cloth manipulation. Most current approaches follow a multiple regrasp strategy for this purpose, in which clothes are sequentially grasped from different points until one of them yields to a desired configuration. In this paper, by contrast, we circumvent the need for multiple re-graspings by building a robust detector that identifies the grasping points, generally in one single step, even when clothes are highly wrinkled. In order to handle the large variability a deformed cloth may have, we build a Bag of Features based detector that combines appearance and 3D geometry features. An image is scanned using a sliding window with a linear classifier, and the candidate windows are refined using a non-linear SVM and a “grasp goodness” criterion to select the best grasping point. We demonstrate our approach detecting collars in deformed polo shirts, using a Kinect camera. Experimental results show a good performance of the proposed method not only in identifying the same trained textile object part under severe deformations and occlusions, but also the corresponding part in other clothes, exhibiting a degree of generalization.

  • Characterization of textile grasping experiments

     Alenyà Ribas, Guillem; Ramisa Ayats, Arnau; Moreno Noguer, Francesc d'Assis; Torras, Carme
    IEEE International Conference on Robotics and Automation
    p. 1-6
    Presentation's date: 2012
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    Single image 3D human pose estimation from noisy observations  Open access

     Simo Serra, Edgar; Ramisa Ayats, Arnau; Torras, Carme; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 2673-2680
    DOI: /10.1109/CVPR.2012.6247988
    Presentation's date: 2012
    Presentation of work at congresses

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    Markerless 3D human pose detection from a single image is a severely underconstrained problem because different 3D poses can have similar image projections. In order to handle this ambiguity, current approaches rely on prior shape models that can only be correctly adjusted if 2D image features are accurately detected. Unfortunately, although current 2D part detector algorithms have shown promising results, they are not yet accurate enough to guarantee a complete disambiguation of the 3D inferred shape. In this paper, we introduce a novel approach for estimating 3D human pose even when observations are noisy. We propose a stochastic sampling strategy to propagate the noise from the image plane to the shape space. This provides a set of ambiguous 3D shapes, which are virtually undistinguishable from their image projections. Disambiguation is then achieved by imposing kinematic constraints that guarantee the resulting pose resembles a 3D human shape. We validate the method on a variety of situations in which state-of-the-art 2D detectors yield either inaccurate estimations or partly miss some of the body parts.

  • AERIAL ROBOTICS COOPERATIVE ASSEMBLY SYSTEM

     Andrade Cetto, Juan; Grau Saldes, Antoni; Alquezar Mancho, Renato; Moreno Noguer, Francesc d'Assis; Villamizar Vergel, Michael Alejandro; Bolea Monte, Yolanda; Sanfeliu Cortes, Alberto
    Competitive project

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  • Efficient 3D object detection using multiple pose-specific classifiers

     Villamizar Vergel, Michael Alejandro; Grabner, Helmut; Andrade Cetto, Juan; Sanfeliu Cortes, Alberto; Van Gool, Luc; Moreno Noguer, Francesc d'Assis
    British Machine Vision Conference
    p. 20.1
    DOI: 10.5244/C.25.20
    Presentation's date: 2011
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    Determining where to grasp cloth using depth information  Open access

     Ramisa Ayats, Arnau; Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Torras, Carme
    International Conference of the Catalan Association for Artificial Intelligence
    p. 199-207
    DOI: 10.3233/978-1-60750-842-7-199
    Presentation's date: 2011
    Presentation of work at congresses

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    In this paper we address the problem of finding an initial good grasping point for the robotic manipulation of textile objects lying on a flat surface. Given as input a point cloud of the cloth acquired with a 3D camera, we propose choosing as grasping points those that maximize a new measure of wrinkledness, computed from the distribution of normal directions over local neighborhoods. Real grasping experiments using a robotic arm are performed, showing that the proposed measure leads to promising results.

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    Simultaneous correspondence and non-rigid 3D reconstruction of the coronary tree from single X-ray images  Open access

     Serradell Domingo, Eduard; Romero, Adriana; Leta, Ruben; Gatta, Carlo; Moreno Noguer, Francesc d'Assis
    International Conference on Computer Vision
    p. 850-857
    DOI: 10.1109/ICCV.2011.6126325
    Presentation's date: 2011
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    We present a novel approach to simultaneously reconstruct the 3D structure of a non-rigid coronary tree and estimate point correspondences between an input X-ray image and a reference 3D shape. At the core of our approach lies an optimization scheme that iteratively fits a generative 3D model of increasing complexity and guides the matching process. As a result, and in contrast to existing approaches that assume rigidity or quasi-rigidity of the structure, our method is able to retrieve large non-linear deformations even when the input data is corrupted by the presence of noise and partial occlusions. We extensively evaluate our approach under synthetic and real data and demonstrate a remarkable improvement compared to state-of-the-art.

    Postprint (author’s final draft)

  • Deformation and illumination invariant feature point descriptor

     Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 1593-1600
    DOI: 10.1109/CVPR.2011.5995529
    Presentation's date: 2011
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  • Detection performance evaluation of boosted random Ferns

     Villamizar Vergel, Michael Alejandro; Moreno Noguer, Francesc d'Assis; Andrade Cetto, Juan; Sanfeliu Cortes, Alberto
    Iberian conference on pattern recognition and image analysis
    p. 67-75
    DOI: 10.1007/978-3-642-21257-4_9
    Presentation's date: 2011
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  • Probabilistic simultaneous pose and non-rigid shape recovery

     Moreno Noguer, Francesc d'Assis; Porta Pleite, Josep M.
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 1289-1296
    DOI: 10.1109/CVPR.2011.5995532
    Presentation's date: 2011
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    Active perception of deformable objects using 3D cameras  Open access

     Alenyà Ribas, Guillem; Moreno Noguer, Francesc d'Assis; Ramisa Ayats, Arnau; Torras, Carme
    Workshop Español de Robótica
    p. 434-440
    Presentation's date: 2011
    Presentation of work at congresses

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    Perception and manipulation of rigid objects has received a lot of attention, and several solutions have been proposed. In contrast, dealing with deformable objects is a relatively new and challenging task because they are more complex to model, their state is difficult to determine, and self-occlusions are common and hard to estimate. In this paper we present our progress/results in the perception of deformable objects both using conventional RGB cameras and active sensing strategies by means of depth cameras.We provide insights in two different areas of application: grasping of textiles and plant leaf modelling.

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    Design of non-anthropomorphic robotic hands for anthropomorphic tasks  Open access

     Simo Serra, Edgar; Moreno Noguer, Francesc d'Assis; Pérez Gracia, María Alba
    ASME International Design Engineering Technical Conferences
    p. 377-386
    DOI: 10.1115/DETC2011-47818
    Presentation's date: 2011
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    In this paper, we explore the idea of designing non- anthropomorphic multi-fingered robotic hands for tasks tha t replicate the motion of the human hand. Taking as input data a finite set of rigid-body positions for the five fingertips, we de- velop a method to perform dimensional synthesis for a kinema tic chain with a tree structure, with five branches that share thr ee common joints. We state the forward kinematics equations of relative dis- placements for each serial chain expressed as dual quaterni ons, and solve for up to five chains simultaneously to reach a numbe r of positions along the hand trajectory. This is done using a h y- brid global numerical solver that integrates a genetic algo rithm and a Levenberg-Marquardt local optimizer. Although the number of candidate solutions in this problem is very high, the use of the genetic algorithm allows us to per form an exhaustive exploration of the solution space to obtain a s et of solutions. We can then choose some of the solutions based on t he specific task to perform. Note that these designs match the ta sk exactly while generally having a finger design radically dif ferent from that of the human hand.

    Postprint (author’s final draft)

  • GARNICS: Gardening with a Cognitive System (FP7-ICT-247947)

     Moreno Noguer, Francesc d'Assis; Torras, Carme; Agostini, Alejandro Gabriel; Husain, Syed Farzad; Dellen, Babette Karla Margarete; Alenyà Ribas, Guillem; Jimenez Schlegl, Pablo; Thomas Arroyo, Federico; Rozo Castañeda, Leonel; Foix Salmeron, Sergi
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    Efficient rotation invariant object detection using boosted random Ferns  Open access

     Villamizar Vergel, Michael Alejandro; Moreno Noguer, Francesc d'Assis; Andrade Cetto, Juan; Sanfeliu Cortes, Alberto
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 1038-1045
    DOI: 10.1109/CVPR.2010.5540104
    Presentation's date: 2010
    Presentation of work at congresses

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    We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs), which we compute during a preprocessing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions.

  • Simultaneous pose, correspondence and non-rigid shape

     Sánchez Riera, Jordi; Ostlund, Jonas; Fua, Pascal; Moreno Noguer, Francesc d'Assis
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 1189-1196
    DOI: 10.1109/CVPR.2010.5539831
    Presentation's date: 2010
    Presentation of work at congresses

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  • Exploring ambiguities for monocular non-rigid shape estimation

     Moreno Noguer, Francesc d'Assis; Porta Pleite, Josep M.; Fua, Pascal
    European Conference on Computer Vision
    p. 370-383
    DOI: 10.1007/978-3-642-15558-1_27
    Presentation's date: 2010
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    Shared random Ferns for efficient detection of multiple categories  Open access

     Villamizar Vergel, Michael Alejandro; Moreno Noguer, Francesc d'Assis; Andrade Cetto, Juan; Sanfeliu Cortes, Alberto
    International Conference on Pattern Recognition
    p. 388-391
    DOI: 10.1109/ICPR.2010.103
    Presentation's date: 2010
    Presentation of work at congresses

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    We propose a new algorithm for detecting multiple object categories that exploits the fact that different categories may share common features but with different geometric distributions. This yields an efficient detector which, in contrast to existing approaches, considerably reduces the computation cost at runtime, where the feature computation step is traditionally the most expensive. More specifically, at the learning stage we compute common features by applying the same Random Ferns over the Histograms of Oriented Gradients on the training images. We then apply a boosting step to build discriminative weak classifiers, and learn the specific geometric distribution of the Random Ferns for each class. At runtime, only a few Random Ferns have to be densely computed over each input image, and their geometric distribution allows performing the detection. The proposed method has been validated in public datasets achieving competitive detection results, which are comparable with state-of-the-art methods that use specific features per class.

  • Combining geometric and appearance priors for robust homography estimation

     Serradell Domingo, Eduard; Özuysa, Mustafa; Lepetit, Vincent; Fua, Pascal; Moreno Noguer, Francesc d'Assis
    European Conference on Computer Vision
    p. 58-72
    DOI: 10.1007/978-3-642-15558-1_5
    Presentation's date: 2010
    Presentation of work at congresses

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    Abstract. The homography between pairs of images are typically computed from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter, additional priors need to be considered. The Blind PnP algorithm makes use of geometric priors to guide the search for matches while computing camera pose. Inspired by this, we propose a novel approach for homography estimation that combines geometric priors with appearance priors of ambiguous descriptors. More specifically, for each point we retain its best candidates according to appearance. We then prune the set of potential matches by iteratively shrinking the regions of the image that are consistent with the geometric prior. We can then successfully compute homographies between pairs of images containing highly repetitive patterns and even under oblique viewing conditions.

  • VISIÓ ARTIFICIAL I SISTEMES INTEL·LIGENTS, 2009 SGR 00937

     Villamizar Vergel, Michael Alejandro; Gamiz Caro, Juan; Bolea Monte, Yolanda; Moreno Noguer, Francesc d'Assis; Celaya Llover, Enric; Grau Saldes, Antoni; Climent Vilaro, Juan; Andrade Cetto, Juan; Corominas Murtra, Andreu; Sanfeliu Cortes, Alberto
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  • Capturing 3D stretchable surfaces from single images in closed form

     Moreno Noguer, Francesc d'Assis; Salzmann, M; Lepetit, V; Fua, P
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 1-8
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    Discrete time motion model for guiding people in urban areas using multiple robots  Open access

     Garrell Zulueta, Anais; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    IEEE/RSJ International Conference on Intelligent Robots and Systems
    p. 486-491
    Presentation's date: 2009
    Presentation of work at congresses

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    We present a new model for people guidance in urban settings using several mobile robots, that overcomes the limitations of existing approaches, which are either tailored to tightly bounded environments, or based on unrealistic human behaviors. Although the robots motion is controlled by means of a standard particle filter formulation, the novelty of our approach resides in how the environment and human and robot motions are modeled. In particular we define a “Discrete-Time-Motion” model, which from one side represents the environment by means of a potential field, that makes it appropriate to deal with open areas, and on the other hand the motion models for people and robots respond to realistic situations, and for instance human behaviors such as “leaving the group” are considered.

  • PAU - Perception and action under uncertainty.

     Andrade Cetto, Juan; Moreno Noguer, Francesc d'Assis
    Competitive project

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  • Pose priors for simultaneously solving alignment and correspondence

     Moreno Noguer, Francesc d'Assis; Lepetit, V; Fua, P
    European Conference on Computer Vision
    p. 405-418
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  • Closed-form solution to son-rigid 3D surface detection

     Salzmann, M; Moreno Noguer, Francesc d'Assis; Lepetit, V; Fua, P
    European Conference on Computer Vision
    p. 581-594
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  • Accurate non-iterative O(n) solution to the PnP problem

     Moreno Noguer, Francesc d'Assis; Lepetit, V; Fua, P
    International Conference on Computer Vision
    p. 2252-2259
    Presentation's date: 2007-10-19
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  • CONSOLIDER-INGENIO 2010 Multimodal interaction in pattern recognition and computer vision

     Sanfeliu Cortes, Alberto; Alquezar Mancho, Renato; Moreno Noguer, Francesc d'Assis; Ferrer Sumsi, Miquel
    Competitive project

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  • Active refocusing of images and videos

     Moreno Noguer, Francesc d'Assis; Belhumeur, P N; Nayar, S K
    International Conference on Computer Graphics and Interactive Techniques
    p. 1-10
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  • Integration of dependent Bayesian filters for robust tracking

     Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto; Samaras, D
    IEEE International Conference on Robotics and Automation
    p. 4081-4087
    Presentation's date: 2006-05-16
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  • A target dependent colorspace for robust tracking

     Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto; Samaras, D
    International Conference on Pattern Recognition
    p. 43-46
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  • DYVINE Dynamic Visual Networks.

     Chrobocinski ., Philippe; Moreno Noguer, Francesc d'Assis
    Competitive project

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  • URUS Ubiquitous Networking Robotics in Urban Settings.

     Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    Competitive project

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  • Multiple Cue Integration for Robust Tracking in Dynamic Environments: Application to Video Relighting  Open access  awarded activity

     Moreno Noguer, Francesc d'Assis
    Department of Automatic Control, Universitat Politècnica de Catalunya
    Theses

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    L'anàlisi de moviment i seguiment d'objectes ha estat un dels pricipals focus d'atenció en la comunitat de visió per computador durant les dues darreres dècades. L'interès per aquesta àrea de recerca resideix en el seu ample ventall d'aplicabilitat, que s'extén des de tasques de navegació de vehicles autònoms i robots, fins a aplications en la indústria de l'entreteniment i realitat virtual.Tot i que s'han aconseguit resultats espectaculars en problemes específics, el seguiment d'objectes continua essent un problema obert, ja que els mètodes disponibles són propensos a ser sensibles a diversos factors i condicions no estacionàries de l'entorn, com ara moviments impredictibles de l'objecte a seguir, canvis suaus o abruptes de la il·luminació, proximitat d'objectes similars o fons confusos. Enfront aquests factors de confusió la integració de múltiples característiques ha demostrat que permet millorar la robustesa dels algoritmes de seguiment. En els darrers anys, degut a la creixent capacitat de càlcul dels ordinadors, hi ha hagut un significatiu increment en el disseny de complexes sistemes de seguiment que consideren simultàniament múltiples característiques de l'objecte. No obstant, la majoria d'aquests algoritmes estan basats enheurístiques i regles ad-hoc formulades per aplications específiques, fent-ne impossible l'extrapolació a noves condicions de l'entorn.En aquesta tesi proposem un marc probabilístic general per integrar el nombre de característiques de l'objecte que siguin necessàries, permetent que interactuin mútuament per tal d'estimar-ne el seu estat amb precisió, i per tant, estimar amb precisió la posició de l'objecte que s'està seguint. Aquest marc, s'utilitza posteriorment per dissenyar un algoritme de seguiment, que es valida en diverses seqüències de vídeo que contenen canvis abruptes de posició i il·luminació, camuflament de l'objecte i deformacions no rígides. Entre les característiques que s'han utilitzat per representar l'objecte, cal destacar la paramatrització robusta del color en un espai de color dependent de l'objecte, que permet distingir-lo del fons més clarament que altres espais de color típicament ulitzats al llarg de la literatura.En la darrera part de la tesi dissenyem una tècnica per re-il·luminar tant escenes estàtiques com en moviment, de les que s'en desconeix la geometria. La re-il·luminació es realitza amb un mètode 'basat en imatges', on la generació de les images de l'escena sota noves condicions d'il·luminació s'aconsegueix a partir de combinacions lineals d'un conjunt d'imatges de referència pre-capturades, i que han estat generades il·luminant l'escena amb patrons de llum coneguts. Com que la posició i intensitat de les fonts d'il.luminació que formen aquests patrons de llum es pot controlar, és natural preguntar-nos: quina és la manera més òptima d'il·luminar una escena per tal de reduir el nombre d'imatges de referència? Demostrem que la millor manera d'il·luminar l'escena (és a dir, la que minimitza el nombre d'imatges de referència) no és utilitzant una seqüència de fonts d'il·luminació puntuals, com es fa generalment, sinó a través d'una seqüència de patrons de llum d'una base d'il·luminació depenent de l'objecte. És important destacar que quan es re-il·luminen seqüències de vídeo, les imatges successives s'han d'alinear respecte a un sistema de coordenades comú. Com que cada imatge ha estat generada per un patró de llum diferent il·uminant l'escena, es produiran canvis d'il·luminació bruscos entre imatges de referència consecutives. Sota aquestes circumstàncies, el mètode de seguiment proposat en aquesta tesi juga un paper fonamental. Finalment, presentem diversos resultats on re-il·luminem seqüències de vídeo reals d'objectes i cares d'actors en moviment. En cada cas, tot i que s'adquireix un únic vídeo, som capaços de re-il·luminar una i altra vegada, controlant la direcció de la llum, la seva intensitat, i el color.

    Motion analysis and object tracking has been one of the principal focus of attention over the past two decades within the computer vision community. The interest of this research area lies in its wide range of applicability, extending from autonomous vehicle and robot navigation tasks, to entertainment and virtual reality applications.Even though impressive results have been obtained in specific problems, object tracking is still an open problem, since available methods are prone to be sensitive to several artifacts and non-stationary environment conditions, such as unpredictable target movements, gradual or abrupt changes of illumination, proximity of similar objects or cluttered backgrounds. Multiple cue integration has been proved to enhance the robustness of the tracking algorithms in front of such disturbances. In recent years, due to the increasing power of the computers, there has been a significant interest in building complex tracking systems which simultaneously consider multiple cues. However, most of these algorithms are based on heuristics and ad-hoc rules formulated for specific applications, making impossible to extrapolate them to new environment conditions.In this dissertation we propose a general probabilistic framework to integrate as many object features as necessary, permitting them to mutually interact in order to obtain a precise estimation of its state, and thus, a precise estimate of the target position. This framework is utilized to design a tracking algorithm, which is validated on several video sequences involving abrupt position and illumination changes, target camouflaging and non-rigid deformations. Among the utilized features to represent the target, it is important to point out the use of a robust parameterization of the target color in an object dependent colorspace which allows to distinguish the object from the background more clearly than other colorspaces commonly used in the literature.In the last part of the dissertation, we design an approach for relighting static and moving scenes with unknown geometry. The relighting is performed through an -image-based' methodology, where the rendering under new lighting conditions is achieved by linear combinations of a set of pre-acquired reference images of the scene illuminated by known light patterns. Since the placement and brightness of the light sources composing such light patterns can be controlled, it is natural to ask: what is the optimal way to illuminate the scene to reduce the number of reference images that are needed? We show that the best way to light the scene (i.e., the way that minimizes the number of reference images) is not using a sequence of single, compact light sources as is most commonly done, but rather to use a sequence of lighting patterns as given by an object-dependent lighting basis. It is important to note that when relighting video sequences, consecutive images need to be aligned with respect to a common coordinate frame. However, since each frame is generated by a different light pattern illuminating the scene, abrupt illumination changes between consecutive reference images are produced. Under these circumstances, the tracking framework designed in this dissertation plays a central role. Finally, we present several relighting results on real video sequences of moving objects, moving faces, and scenes containing both. In each case, although a single video clip was captured, we are able to relight again and again, controlling the lighting direction, extent, and color.

  • Integration of conditionally dependent object features for robust figure/background segmentation

     Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto; Samaras, D
    International Conference on Computer Vision
    p. 1713-1720
    Presentation's date: 2005-10-17
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  • Research at the learning and vision mobile robotics group 2004-2005

     Scandaliaris, Jorge; Alquezar Mancho, Renato; Andrade Cetto, Juan; Aranda López, Juan; Climent Vilaro, Juan; Grau Saldes, Antoni; Mirats Tur, Josep Maria; Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto; Vidal Calleja, Teresa; Serratosa Casanelles, Francesc; Vergés Llahí, Jaume
    Simposium de reconocimiento de formas y análisis de imágenes, I Congreso español de informática
    p. 175-181
    Presentation's date: 2005-09-13
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  • Optimal illumination for image and video relighting

     Moreno Noguer, Francesc d'Assis; Belhumeur, P N; Nayar, S K
    32nd International Conference on Computer Graphics and Interactive Techniques
    p. 1
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  • Optimal illumination for image and video relighting

     Moreno Noguer, Francesc d'Assis; Belhumeur, P N; Nayar, S K
    IEEE European Conference on Visual Media Production
    p. 201-210
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  • VIS Grupo de Visión Artificial y Sistemas Inteligentes.

     Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    Competitive project

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