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.

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    Exhaustive linearization for robust camera pose and focal length estimation  Open access

     Peñate Sánchez, Adrián; Andrade Cetto, Juan; Moreno Noguer, Francesc d'Assis
    IEEE transactions on pattern analysis and machine intelligence
    Vol. 35, num. 10, p. 2387-2400
    DOI: 10.1109/TPAMI.2013.36
    Date of publication: 2013
    Journal article

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    We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.

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    Stochastic exploration of ambiguities for nonrigid shape recovery  Open access

     Moreno Noguer, Francesc d'Assis; Fua, Pascal
    IEEE transactions on pattern analysis and machine intelligence
    Vol. 35, num. 2, p. 463-475
    DOI: 0.1109/TPAMI.2012.102
    Date of publication: 2013
    Journal article

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    Recovering the 3D shape of deformable surfaces from single images is known to be a highly ambiguous problem because many different shapes may have very similar projections. This is commonly addressed by restricting the set of possible shapes to linear combinations of deformation modes and by imposing additional geometric constraints. Unfortunately, because image measurements are noisy, such constraints do not always guarantee that the correct shape will be recovered. To overcome this limitation, we introduce a stochastic sampling approach to efficiently explore the set of solutions of an objective function based on point correspondences. This allows to propose a small set of ambiguous candidate 3D shapes and then use additional image information to choose the best one. As a proof of concept, we use either motion or shading cues to this end and show that we can handle a complex objective function without having to solve a difficult non-linear minimization problem. The advantages of our method are demonstrated on a variety of problems including both real and synthetic data.

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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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    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.

  • 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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    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.

    Postprint (author’s final draft)

  • Robust non-rigid registration of 2D and 3D graphs

     Serradell Domingo, Eduard; Glowacki, Przemyslaw; Kybic, J.; Moreno Noguer, Francesc d'Assis; Fua, Pascal
    IEEE Computer Society conference on computer vision and pattern recognition. Proceedings
    Vol. 2012, p. 996-1003
    DOI: 10.1109/CVPR.2012.6247776
    Date of publication: 2012
    Journal article

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    We present a new approach to matching graphs embedded in R2 or R3. Unlike earlier methods, our approach does not rely on the similarity of local appearance features, does not require an initial alignment, can handle partial matches, and can cope with non-linear deformations and topological differences. To handle arbitrary non-linear deformations, we represent them as Gaussian Processes. In the absence of appearance information, we iteratively establish correspondences between graph nodes, update the structure accordingly, and use the current mapping estimate to find the most likely correspondences that will be used in the next iteration. This makes the computation tractable. We demonstrate the effectiveness of our approach first on synthetic cases and then on angiography data, retinal fundus images, and microscopy image stacks acquired at very different resolutions.

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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)

  • 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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  • Bootstrapping Boosted Random Ferns for discriminative and efficient object classification

     Villamizar Vergel, Michael Alejandro; Andrade Cetto, Juan; Sanfeliu Cortes, Alberto; Moreno Noguer, Francesc d'Assis
    Pattern recognition
    Vol. 45, num. 9, p. 3141-3153
    DOI: 10.1016/j.patcog.2012.03.025
    Date of publication: 2012-09
    Journal article

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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
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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.

  • 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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    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
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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; Sanfeliu Cortes, Alberto
    Competitive project

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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.

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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.

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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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  • 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
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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.

    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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    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.

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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.

  • 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
    Presentation of work at congresses

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  • 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.

  • 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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  • EPnP: an accurate o(n) solution to the PnP problem

     Lepetit, V; Moreno Noguer, Francesc d'Assis; Fua, P
    International journal of computer vision
    Vol. 81, num. 2, p. 155-166
    DOI: 10.1007/s11263-008-0152-6
    Date of publication: 2009-02
    Journal article

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  • 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
    Competitive project

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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.

  • 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
    Presentation of work at congresses

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  • Dependent multiple cue integration for robust tracking

     Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto; Samaras, D
    IEEE transactions on pattern analysis and machine intelligence
    Vol. 30, num. 4, p. 670-685
    DOI: 10.1109/TPAMI.2007.70727
    Date of publication: 2008
    Journal article

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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
    Presentation of work at congresses

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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
    Presentation of work at congresses

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  • PAU - Perception and action under uncertainty.

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

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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
    ACM transactions on graphics
    Vol. 26, num. 3, p. 1-67
    Date of publication: 2007-07
    Journal article

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    Integration of deformable contours and a multiple hypotheses Fisher color model for robust tracking in varying illuminant environments  Open access

     Moreno Noguer, Francesc d'Assis; Sanfeliu Cortes, Alberto; Samaras, D
    Image and vision computing
    Vol. 25, num. 3, p. 285-296
    Date of publication: 2007-03
    Journal article

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    In this paper we propose a new technique to perform figure-ground segmentation in image sequences of moving objects under varying illumination conditions. Unlike most of the algorithms that adapt color, there is not the assumption of smooth change of the viewing conditions. To cope with this, we propose the use of a new colorspace that maximizes the foreground/background class separability based on the 'Linear Discriminant Analysis' method. Moreover, we introduce a technique that formulates multiple hypotheses about the next state of the color distribution (some of these hypotheses take into account small and gradual changes in the color model and others consider more abrupt and unexpected variations) and the hypothesis that generates the best object segmentation is used to remove noisy edges from the image. This simplifies considerably the final step of fitting a deformable contour to the object boundary, thus allowing a standard snake formulation to successfully track nonrigid contours. In the same manner, the contour estimate is used to correct the color model. The integration of color and shape is done in a stage called 'sample concentration', introduced as a final step to the well-known condensation algorithm.

  • 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
    Presentation of work at congresses

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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
    Presentation of work at congresses

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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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  • 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
    Presentation of work at congresses

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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
    Presentation of work at congresses

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  • DYVINE Dynamic Visual Networks.

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

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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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  • 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
    Presentation of work at congresses

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