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Author Marco Pedersoli; Jordi Gonzalez; Xu Hu; Xavier Roca
Title (down) Toward Real-Time Pedestrian Detection Based on a Deformable Template Model Type Journal Article
Year 2014 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 15 Issue 1 Pages 355-364
Keywords
Abstract Most advanced driving assistance systems already include pedestrian detection systems. Unfortunately, there is still a tradeoff between precision and real time. For a reliable detection, excellent precision-recall such a tradeoff is needed to detect as many pedestrians as possible while, at the same time, avoiding too many false alarms; in addition, a very fast computation is needed for fast reactions to dangerous situations. Recently, novel approaches based on deformable templates have been proposed since these show a reasonable detection performance although they are computationally too expensive for real-time performance. In this paper, we present a system for pedestrian detection based on a hierarchical multiresolution part-based model. The proposed system is able to achieve state-of-the-art detection accuracy due to the local deformations of the parts while exhibiting a speedup of more than one order of magnitude due to a fast coarse-to-fine inference technique. Moreover, our system explicitly infers the level of resolution available so that the detection of small examples is feasible with a very reduced computational cost. We conclude this contribution by presenting how a graphics processing unit-optimized implementation of our proposed system is suitable for real-time pedestrian detection in terms of both accuracy and speed.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1524-9050 ISBN Medium
Area Expedition Conference
Notes ISE; 601.213; 600.078 Approved no
Call Number PGH2014 Serial 2350
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Author X. Varona; Antoni Jaume-i-Capo; Jordi Gonzalez; Francisco Jose Perales
Title (down) Toward Natural Interaction through Visual Recognition of Body Gestures in Real-Time Type Journal
Year 2008 Publication Interacting with Computers, diu 10,1016/j.intcom.2008.10.001, available on line Abbreviated Journal
Volume Issue Pages
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Abstract
Address
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number ISE @ ise @ VJG2008 Serial 1022
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Author Patricia Suarez; Angel Sappa
Title (down) Toward a Thermal Image-Like Representation Type Conference Article
Year 2023 Publication Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal
Volume Issue Pages 133-140
Keywords
Abstract This paper proposes a novel model to obtain thermal image-like representations to be used as an input in any thermal image compressive sensing approach (e.g., thermal image: filtering, enhancing, super-resolution). Thermal images offer interesting information about the objects in the scene, in addition to their temperature. Unfortunately, in most of the cases thermal cameras acquire low resolution/quality images. Hence, in order to improve these images, there are several state-of-the-art approaches that exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). In these SOTA approaches visible images are fused at different levels without paying attention the images acquire information at different bands of the spectral. In this paper a novel approach is proposed to generate thermal image-like representations from a low cost visible images, by means of a contrastive cycled GAN network. Obtained representations (synthetic thermal image) can be later on used to improve the low quality thermal image of the same scene. Experimental results on different datasets are presented.
Address Lisboa; Portugal; February 2023
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference VISIGRAPP
Notes MSIAU Approved no
Call Number Admin @ si @ SuS2023b Serial 3927
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Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title (down) Touching Text Character Localization in Graphical Documents using SIFT Type Conference Article
Year 2009 Publication In proceedings 8th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.
Address La rochelle; July 2009
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number DAG @ dag @ RPL2009c Serial 1445
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Author Partha Pratim Roy; Umapada Pal; Josep Llados
Title (down) Touching Text Character Localization in Graphical Documents using SIFT Type Book Chapter
Year 2010 Publication Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers Abbreviated Journal
Volume 6020 Issue Pages 199-211
Keywords Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform
Abstract Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult.
Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-13727-3 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ RPL2010c Serial 2408
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Author Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane
Title (down) TopoX: A Suite of Python Packages for Machine Learning on Topological Domains Type Miscellaneous
Year 2024 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ HPF2024 Serial 4021
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez
Title (down) Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Conference Article
Year 2020 Publication Women in Geometry and Topology Abbreviated Journal
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Abstract
Address Barcelona; September 2019
Corporate Author Thesis
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Area Expedition Conference
Notes IAM; DAG; 600.139; 600.145; 600.121 Approved no
Call Number Admin @ si @ GRP2020 Serial 3473
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Author Debora Gil; Oriol Ramos Terrades; Raquel Perez
Title (down) Topological Radiomics (TOPiomics): Early Detection of Genetic Abnormalities in Cancer Treatment Evolution Type Book Chapter
Year 2021 Publication Extended Abstracts GEOMVAP 2019, Trends in Mathematics 15 Abbreviated Journal
Volume 15 Issue Pages 89–93
Keywords
Abstract Abnormalities in radiomic measures correlate to genomic alterations prone to alter the outcome of personalized anti-cancer treatments. TOPiomics is a new method for the early detection of variations in tumor imaging phenotype from a topological structure in multi-view radiomic spaces.
Address
Corporate Author Thesis
Publisher Springer Nature Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; DAG; 600.120; 600.145; 600.139 Approved no
Call Number Admin @ si @ GRP2021 Serial 3594
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Author A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva
Title (down) Topological principal component analysis for face encoding and recognition Type Journal Article
Year 2001 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 22 Issue 6-7 Pages 769–776
Keywords
Abstract IF: 0.552
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes ADAS;OR;MV Approved no
Call Number ADAS @ adas @ PVL2001 Serial 155
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Author Eduard Vazquez; Ramon Baldrich; Javier Vazquez; Maria Vanrell
Title (down) Topological histogram reduction towards colour segmentation Type Book Chapter
Year 2007 Publication 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:55–62 Abbreviated Journal
Volume Issue Pages
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Abstract
Address Gerona (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor
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Area Expedition Conference
Notes CIC Approved no
Call Number CAT @ cat @ VBV2007 Serial 809
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Author Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva
Title (down) Topic modelling for routine discovery from egocentric photo-streams Type Journal Article
Year 2020 Publication Pattern Recognition Abbreviated Journal PR
Volume 104 Issue Pages 107330
Keywords Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling
Abstract Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ TWP2020 Serial 3435
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Author Meysam Madadi; Sergio Escalera; Alex Carruesco Llorens; Carlos Andujar; Xavier Baro; Jordi Gonzalez
Title (down) Top-down model fitting for hand pose recovery in sequences of depth images Type Journal Article
Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 79 Issue Pages 63-75
Keywords
Abstract State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. We evaluate our approach on a new created synthetic hand dataset along with NYU and MSRA real datasets. Results demonstrate that the proposed method outperforms the most recent pose recovering approaches, including those based on CNNs.
Address
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes HUPBA; 600.098 Approved no
Call Number Admin @ si @ MEC2018 Serial 3203
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Author Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen
Title (down) Top-Down Deep Appearance Attention for Action Recognition Type Conference Article
Year 2017 Publication 20th Scandinavian Conference on Image Analysis Abbreviated Journal
Volume 10269 Issue Pages 297-309
Keywords Action recognition; CNNs; Feature fusion
Abstract Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches.
Address Tromso; June 2017
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SCIA
Notes LAMP; 600.109; 600.068; 600.120 Approved no
Call Number Admin @ si @ RKW2017b Serial 3039
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title (down) Top-Down Color Attention for Object Recognition Type Conference Article
Year 2009 Publication 12th International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 979 - 986
Keywords
Abstract Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
Address Kyoto, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5499 ISBN 978-1-4244-4420-5 Medium
Area Expedition Conference ICCV
Notes CIC Approved no
Call Number CAT @ cat @ SWV2009 Serial 1196
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Author Armin Mehri; Parichehr Behjati; Angel Sappa
Title (down) TnTViT-G: Transformer in Transformer Network for Guidance Super Resolution Type Journal Article
Year 2023 Publication IEEE Access Abbreviated Journal ACCESS
Volume 11 Issue Pages 11529-11540
Keywords
Abstract Image Super Resolution is a potential approach that can improve the image quality of low-resolution optical sensors, leading to improved performance in various industrial applications. It is important to emphasize that most state-of-the-art super resolution algorithms often use a single channel of input data for training and inference. However, this practice ignores the fact that the cost of acquiring high-resolution images in various spectral domains can differ a lot from one another. In this paper, we attempt to exploit complementary information from a low-cost channel (visible image) to increase the image quality of an expensive channel (infrared image). We propose a dual stream Transformer-based super resolution approach that uses the visible image as a guide to super-resolve another spectral band image. To this end, we introduce Transformer in Transformer network for Guidance super resolution, named TnTViT-G, an efficient and effective method that extracts the features of input images via different streams and fuses them together at various stages. In addition, unlike other guidance super resolution approaches, TnTViT-G is not limited to a fixed upsample size and it can generate super-resolved images of any size. Extensive experiments on various datasets show that the proposed model outperforms other state-of-the-art super resolution approaches. TnTViT-G surpasses state-of-the-art methods by up to 0.19∼2.3dB , while it is memory efficient.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
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Area Expedition Conference
Notes MSIAU Approved no
Call Number Admin @ si @ MBS2023 Serial 3876
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