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| Author | Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro | ||||
| Title | Residual Stacked RNNs for Action Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 9th International Workshop on Human Behavior Understanding | Abbreviated Journal | |
| Volume | Issue | Pages | 534-548 | ||
Keywords  |
Action recognition; Deep residual learning; Two-stream RNN | ||||
| Abstract | Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. | ||||
| Address | Munich; September 2018 | ||||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LCE2018b | Serial | 3206 | ||
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| Author | Thomas B. Moeslund; Sergio Escalera; Gholamreza Anbarjafari; Kamal Nasrollahi; Jun Wan | ||||
| Title | Statistical Machine Learning for Human Behaviour Analysis | Type | Journal Article | ||
| Year | 2020 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 25 | Issue | 5 | Pages | 530 |
| Keywords |
action recognition; emotion recognition; privacy-aware | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ MEA2020 | Serial | 3441 | ||
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| Author | Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey | Type | Book Chapter | ||
| Year | 2017 | Publication | Gesture Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 539-578 | ||
| Keywords |
Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies | ||||
| Abstract | Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ ACB2017a | Serial | 2981 | ||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen | ||||
| Title | Deep semantic pyramids for human attributes and action recognition | Type | Conference Article | ||
| Year | 2015 | Publication | Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 | Abbreviated Journal | |
| Volume | 9127 | Issue | Pages | 341-353 | |
| Keywords |
Action recognition; Human attributes; Semantic pyramids | ||||
| 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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| Address | Denmark; Copenhagen; June 2015 | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| ISSN | 0302-9743 | ISBN | 978-3-319-19664-0 | Medium | |
| Area | Expedition | Conference | SCIA | ||
| Notes | LAMP; 600.068; 600.079;ADAS | Approved | no | ||
| Call Number | Admin @ si @ KRW2015b | Serial | 2672 | ||
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| Author | Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz | ||||
| Title | Gate-Shift-Fuse for Video Action Recognition | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 45 | Issue | 9 | Pages | 10913-10928 |
| Keywords |
Action Recognition; Video Classification; Spatial Gating; Channel Fusion | ||||
| Abstract | Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks. | ||||
| Address | 1 Sept. 2023 | ||||
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ SEL2023 | Serial | 3814 | ||
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| Author | Albert Andaluz | ||||
| Title | LV Contour Segmentation in TMR images using Semantic Description of Tissue and Prior Knowledge Correction | Type | Report | ||
| Year | 2009 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 142 | Issue | Pages | ||
| Keywords |
Active Contour Models; Snakes; Active Shape Models; Deformable Templates; Left Ventricle Segmentation; Generalized Orthogonal Procrustes Analysis; Harmonic Phase Flow; Principal Component Analysis; Tagged Magnetic Resonance | ||||
| Abstract | The Diagnosis of Left Ventricle (LV) pathologies is related to regional wall motion analysis. Health indicator scores such as the rotation and the torsion are useful for the diagnose of the Left Ventricle (LV) function. However, this requires proper identification of LV segments. On one hand, manual segmentation is robust, but it is slow and requires medical expertise. On the other hand, the tag pattern in Tagged Magnetic Resonance (TMR) sequences is a problem for the automatic segmentation of the LV boundaries. Consequently, we propose a method based in the classical formulation of parametric Snakes, combined with Active Shape models. Our semantic definition of the LV is tagged tissue that experiences motion in the systolic cycle. This defines two energy potentials for the Snake convergence. Additionally, the mean shape corrects excessive deviation from the anatomical shape. We have validated our approach in 15 healthy volunteers and two short axis cuts. In this way, we have compared the automatic segmentations to manual shapes outlined by medical experts. Also, we have explored the accuracy of clinical scores computed using automatic contours. The results show minor divergence in the approximation and the manual segmentations as well as robust computation of clinical scores in all cases. From this we conclude that the proposed method is a promising support tool for clinical analysis. | ||||
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| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Bellaterra 08193, Barcelona, Spain | Editor | ||
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| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ And2009 | Serial | 1667 | ||
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| Author | Q. Xue; Laura Igual; A. Berenguel; M. Guerrieri; L. Garrido | ||||
| Title | Active Contour Segmentation with Affine Coordinate-Based Parametrization | Type | Conference Article | ||
| Year | 2014 | Publication | 9th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 5-14 | |
| Keywords |
Active Contours; Affine Coordinates; Mean Value Coordinates | ||||
| Abstract | In this paper, we present a new framework for image segmentation based on parametrized active contours. The contour and the points of the image space are parametrized using a set of reduced control points that have to form a closed polygon in two dimensional problems and a closed surface in three dimensional problems. By moving the control points, the active contour evolves. We use mean value coordinates as the parametrization tool for the interface, which allows to parametrize any point of the space, inside or outside the closed polygon
or surface. Region-based energies such as the one proposed by Chan and Vese can be easily implemented in both two and three dimensional segmentation problems. We show the usefulness of our approach with several experiments. |
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| Address | Lisboa; January 2014 | ||||
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| Area | Expedition | Conference | VISAPP | ||
| Notes | OR;MILAB | Approved | no | ||
| Call Number | Admin @ si @ XIB2014 | Serial | 2452 | ||
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| Author | David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa | ||||
| Title | Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation | Type | Miscellaneous | ||
| Year | 2006 | Publication | 6th IASTED International Conference on Visualization, Imaging and Image Processing | Abbreviated Journal | VIIP |
| Volume | Issue | Pages | 400–405 | ||
| Keywords |
ADAS, pedestrian detection, adaboost learning, pitch estimation, haar wavelets, edge orientation histograms. | ||||
| Abstract | In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform
the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows. With this approach, the system can work in urban roads, as will be illustrated by current results. |
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| Address | Palma de Mallorca (Spain) | ||||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSL2006 | Serial | 672 | ||
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| Author | David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf | ||||
| Title | Survey on Pedestrian Detection for Advanced Driver Assistance Systems | Type | Journal Article | ||
| Year | 2010 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 32 | Issue | 7 | Pages | 1239–1258 |
| Keywords |
ADAS, pedestrian detection, on-board vision, survey | ||||
| Abstract | Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GLS2010 | Serial | 1340 | ||
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| Author | David Geronimo; David Vazquez; Arturo de la Escalera | ||||
| Title | Vision-Based Advanced Driver Assistance Systems | Type | Book Chapter | ||
| Year | 2017 | Publication | Computer Vision in Vehicle Technology: Land, Sea, and Air | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords |
ADAS; Autonomous Driving | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | ADAS @ adas @ GVE2017 | Serial | 2881 | ||
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| Author | Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe | ||||
| Title | Random Forests of Local Experts for Pedestrian Detection | Type | Conference Article | ||
| Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 2592 - 2599 | ||
| Keywords |
ADAS; Random Forest; Pedestrian Detection | ||||
| Abstract | Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | Medium | ||
| Area | Expedition | Conference | ICCV | ||
| Notes | ADAS; 600.057; 600.054 | Approved | no | ||
| Call Number | ADAS @ adas @ MVL2013 | Serial | 2333 | ||
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| Author | Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados | ||||
| Title | Classification of Administrative Document Images by Logo Identification | Type | Book Chapter | ||
| Year | 2014 | Publication | Graphics Recognition. Current Trends and Challenges | Abbreviated Journal | |
| Volume | 8746 | Issue | Pages | 49-58 | |
| Keywords |
Administrative Document Classification; Logo Recognition; Logo Spotting | ||||
| Abstract | This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | Bart Lamiroy; Jean-Marc Ogier | |
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| ISSN | 0302-9743 | ISBN | 978-3-662-44853-3 | Medium | |
| Area | Expedition | Conference | |||
| Notes | DAG; 600.056; 600.045; 605.203; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ RPK2014 | Serial | 2701 | ||
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| Author | Domicele Jonauskaite; Lucia Camenzind; C. Alejandro Parraga; Cecile N Diouf; Mathieu Mercapide Ducommun; Lauriane Müller; Melanie Norberg; Christine Mohr | ||||
| Title | Colour-emotion associations in individuals with red-green colour blindness | Type | Journal Article | ||
| Year | 2021 | Publication | PeerJ | Abbreviated Journal | |
| Volume | 9 | Issue | Pages | e11180 | |
| Keywords |
Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia. | ||||
| Abstract | Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient. | ||||
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| Notes | CIC; LAMP; 600.120; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ JCP2021 | Serial | 3564 | ||
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| Author | Pau Rodriguez; Guillem Cucurull; Josep M. Gonfaus; Xavier Roca; Jordi Gonzalez | ||||
| Title | Age and gender recognition in the wild with deep attention | Type | Journal Article | ||
| Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 72 | Issue | Pages | 563-571 | |
| Keywords |
Age recognition; Gender recognition; Deep neural networks; Attention mechanisms | ||||
| Abstract | Face analysis in images in the wild still pose a challenge for automatic age and gender recognition tasks, mainly due to their high variability in resolution, deformation, and occlusion. Although the performance has highly increased thanks to Convolutional Neural Networks (CNNs), it is still far from optimal when compared to other image recognition tasks, mainly because of the high sensitiveness of CNNs to facial variations. In this paper, inspired by biology and the recent success of attention mechanisms on visual question answering and fine-grained recognition, we propose a novel feedforward attention mechanism that is able to discover the most informative and reliable parts of a given face for improving age and gender classification. In particular, given a downsampled facial image, the proposed model is trained based on a novel end-to-end learning framework to extract the most discriminative patches from the original high-resolution image. Experimental validation on the standard Adience, Images of Groups, and MORPH II benchmarks show that including attention mechanisms enhances the performance of CNNs in terms of robustness and accuracy. | ||||
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| Notes | ISE; 600.098; 602.133; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ RCG2017b | Serial | 2962 | ||
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| Author | Joel Barajas; Jaume Garcia; Francesc Carreras; Sandra Pujades; Petia Radeva | ||||
| Title | Angle Images Using Gabor Filters in Cardiac Tagged MRI | Type | Conference Article | ||
| Year | 2005 | Publication | Proceeding of the 2005 conference on Artificial Intelligence Research and Development | Abbreviated Journal | |
| Volume | Issue | Pages | 107-114 | ||
| Keywords |
Angle Images, Gabor Filters, Harp, Tagged Mri | ||||
| Abstract | Tagged Magnetic Resonance Imaging (MRI) is a non-invasive technique used to examine cardiac deformation in vivo. An Angle Image is a representation of a Tagged MRI which recovers the relative position of the tissue respect to the distorted tags. Thus cardiac deformation can be estimated. This paper describes a new approach to generate Angle Images using a bank of Gabor filters in short axis cardiac Tagged MRI. Our method improves the Angle Images obtained by global techniques, like HARP, with a local frequency analysis. We propose to use the phase response of a combination of a Gabor filters bank, and use it to find a more precise deformation of the left ventricle. We demonstrate the accuracy of our method over HARP by several experimental results. | ||||
| Address | Amsterdam; The Netherlands | ||||
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| Publisher | IOS Press | Place of Publication | Amsterdam, The Netherlands | Editor | |
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| ISSN | ISBN | 1-58603-560-6 | Medium | ||
| Area | Expedition | Conference | CAIRD | ||
| Notes | IAM;MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BGC2005; IAM @ iam | Serial | 595 | ||
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