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Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts |
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2017 |
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IEEE Transactions on cybernetics |
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Cyber |
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47 |
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11 |
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3980 - 3990 |
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Multicue; multimodal; multiview; object detection |
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Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. |
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2168-2267 |
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ADAS; 600.085; 600.082; 600.076; 600.118 |
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Admin @ si @ |
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2810 |
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Jean-Pascal Jacob; Mariella Dimiccoli; L. Moisan |
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Title |
Active skeleton for bacteria modelling |
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2017 |
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Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization |
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CMBBE |
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5 |
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4 |
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274-286 |
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The investigation of spatio-temporal dynamics of bacterial cells and their molecular components requires automated image analysis tools to track cell shape properties and molecular component locations inside the cells. In the study of bacteria aging, the molecular components of interest are protein aggregates accumulated near bacteria boundaries. This particular location makes very ambiguous the correspondence between aggregates and cells, since computing accurately bacteria boundaries in phase-contrast time-lapse imaging is a challenging task. This paper proposes an active skeleton formulation for bacteria modelling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness and orientation), an improved boundary accuracy in noisy images and a natural bacteria-centred coordinate system that permits the intrinsic location of molecular components inside the cell. Starting from an initial skeleton estimate, the medial axis of the bacterium is obtained by minimising an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modelling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at http://fluobactracker.inrialpes.fr. |
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Taylor & Francis Group |
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MILAB; |
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Admin @ si @JDM2017 |
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2784 |
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Debora Gil; Sergio Vera; Agnes Borras; Albert Andaluz; Miguel Angel Gonzalez Ballester |
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Title |
Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities |
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2017 |
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Medical Image Analysis |
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MIA |
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35 |
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390-402 |
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Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points |
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Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a con dent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies. |
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Elsevier B.V. |
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IAM; 600.060; 600.096; 600.075; 600.145 |
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Admin @ si @ GVB2017 |
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2775 |
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Author |
Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera |
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Title |
Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis |
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Year |
2017 |
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International Journal of Computer Vision |
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IJCV |
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122 |
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2 |
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212–227 |
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Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation |
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This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures. |
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HuPBA;MILAB; 303.100 |
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Admin @ si @ PEB2017 |
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2765 |
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Author |
Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza |
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Title |
Evolving weighting schemes for the Bag of Visual Words |
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Journal Article |
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Year |
2017 |
Publication |
Neural Computing and Applications |
Abbreviated Journal |
Neural Computing and Applications |
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28 |
Issue |
5 |
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925–939 |
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Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision |
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The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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Springer |
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HUPBA;MV; no menciona |
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Admin @ si @ EPE2017 |
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2743 |
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Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva |
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Title |
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation |
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Journal Article |
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2017 |
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Computer Vision and Image Understanding |
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CVIU |
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155 |
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55-69 |
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While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. |
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MILAB; 601.235 |
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Admin @ si @ DBT2017 |
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2714 |
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Marc Bolaños; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards Storytelling from Visual Lifelogging: An Overview |
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2017 |
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IEEE Transactions on Human-Machine Systems |
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THMS |
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47 |
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1 |
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77 - 90 |
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Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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MILAB; 601.235 |
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Admin @ si @ BDR2017 |
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2712 |
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