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Author |
Onur Ferhat; Arcadi Llanza; Fernando Vilariño |
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Title |
Gaze interaction for multi-display systems using natural light eye-tracker |
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Conference Article |
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2015 |
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2nd International Workshop on Solutions for Automatic Gaze Data Analysis |
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Bielefeld; Germany; September 2015 |
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SAGA |
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MV;SIAI |
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Admin @ si @ FLV2015b |
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2676 |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
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Title |
Deep semantic pyramids for human attributes and action recognition |
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Conference Article |
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Year |
2015 |
Publication |
Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 |
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9127 |
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341-353 |
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Keywords |
Action recognition; Human attributes; Semantic pyramids |
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Abstract |
Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature. |
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Denmark; Copenhagen; June 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-19664-0 |
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SCIA |
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LAMP; 600.068; 600.079;ADAS |
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Admin @ si @ KRW2015b |
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2672 |
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Author |
Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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Title |
Brightness and colour induction through contextual influences in V1 |
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Conference Article |
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2015 |
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Scottish Vision Group 2015 SGV2015 |
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12 |
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9 |
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1208-2012 |
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Carnoustie; Scotland; March 2015 |
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SGV |
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NEUROBIT; |
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no |
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Admin @ si @ OPC2015a |
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2632 |
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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
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Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
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Book Chapter |
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Year |
2015 |
Publication |
Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 |
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9534 |
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69-79 |
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Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. |
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Munich; Germany; January 2015 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-28711-9 |
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STACOM |
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ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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Admin @ si @ KHM2015 |
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2734 |
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Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; I.Araujo ; Maria Lopez Ceron; J.Llach; F. Javier Sanchez |
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Title |
Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps |
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Conference Article |
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2015 |
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Proceedings of 23 United European- UEG Week 2015 |
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UEG |
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MV; IAM; 600.075;SIAI |
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Admin @ si @ FBR2015 |
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2732 |
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Author |
Antoni Gurgui; Debora Gil; Enric Marti |
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Title |
Laplacian Unitary Domain for Texture Morphing |
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Conference Article |
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2015 |
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Proceedings of the 10th International Conference on Computer Vision Theory and Applications VISIGRAPP2015 |
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1 |
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693-699 |
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Facial; metamorphosis;LaplacianMorphing |
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Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them. |
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Munich; Germany; February 2015 |
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SciTePress |
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978-989-758-089-5 |
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VISAPP |
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IAM; 600.075 |
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Admin @ si @ GGM2015 |
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2614 |
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Author |
Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados |
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Title |
Document Analysis Techniques for Automatic Electoral Document Processing: A Survey |
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Conference Article |
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2015 |
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E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 |
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139-141 |
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Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally |
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In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. |
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Bern; Switzerland; September 2015 |
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VoteID |
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DAG; 600.061; 602.006; 600.077 |
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Admin @ si @ TCP2015 |
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2641 |
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Author |
German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
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Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
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2015 |
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IEEE Winter Conference on Applications of Computer Vision |
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231 - 238 |
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Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
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Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
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Hawaii; January 2015 |
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ACDC |
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WACV |
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ADAS; 600.076 |
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ADAS @ adas @ RRG2015 |
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2499 |
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