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| Author | Reza Azad; Maryam Asadi-Aghbolaghi; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Dynamic 3D Hand Gesture Recognition by Learning Weighted Depth Motion Maps | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Circuits and Systems for Video Technology | Abbreviated Journal | TCSVT |
| Volume | 29 | Issue | 6 | Pages | 1729-1740 |
| Keywords | Hand gesture recognition; Multilevel temporal sampling; Weighted depth motion map; Spatio-temporal description; VLAD encoding | ||||
| Abstract | Hand gesture recognition from sequences of depth maps is a challenging computer vision task because of the low inter-class and high intra-class variability, different execution rates of each gesture, and the high articulated nature of human hand. In this paper, a multilevel temporal sampling (MTS) method is first proposed that is based on the motion energy of key-frames of depth sequences. As a result, long, middle, and short sequences are generated that contain the relevant gesture information. The MTS results in increasing the intra-class similarity while raising the inter-class dissimilarities. The weighted depth motion map (WDMM) is then proposed to extract the spatio-temporal information from generated summarized sequences by an accumulated weighted absolute difference of consecutive frames. The histogram of gradient (HOG) and local binary pattern (LBP) are exploited to extract features from WDMM. The obtained results define the current state-of-the-art on three public benchmark datasets of: MSR Gesture 3D, SKIG, and MSR Action 3D, for 3D hand gesture recognition. We also achieve competitive results on NTU action dataset. | ||||
| Address | June 2019, | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ AAK2018 | Serial | 3213 | ||
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| Author | Ester Fornells; Manuel De Armas; Maria Teresa Anguera; Sergio Escalera; Marcos Antonio Catalán; Josep Moya | ||||
| Title | Desarrollo del proyecto del Consell Comarcal del Baix Llobregat “Buen Trato a las personas mayores y aquellas en situación de fragilidad con sufrimiento emocional: Hacia un envejecimiento saludable” | Type | Journal | ||
| Year | 2018 | Publication | Informaciones Psiquiatricas | Abbreviated Journal | |
| Volume | 232 | Issue | Pages | 47-59 | |
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| ISSN | 0210-7279 | ISBN | Medium | ||
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| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ FAA2018 | Serial | 3214 | ||
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| Author | Suman Ghosh | ||||
| Title | Word Spotting and Recognition in Images from Heterogeneous Sources A | Type | Book Whole | ||
| Year | 2018 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Text is the most common way of information sharing from ages. With recent development of personal images databases and handwritten historic manuscripts the demand for algorithms to make these databases accessible for browsing and indexing are in rise. Enabling search or understanding large collection of manuscripts or image databases needs fast and robust methods. Researchers have found different ways to represent cropped words for understanding and matching, which works well when words are already segmented. However there is no trivial way to extend these for non-segmented documents. In this thesis we explore different methods for text retrieval and recognition from unsegmented document and scene images. Two different ways of representation exist in literature, one uses a fixed length representation learned from cropped words and another a sequence of features of variable length. Throughout this thesis, we have studied both these representation for their suitability in segmentation free understanding of text. In the first part we are focused on segmentation free word spotting using a fixed length representation. We extended the use of the successful PHOC (Pyramidal Histogram of Character) representation to segmentation free retrieval. In the second part of the thesis, we explore sequence based features and finally, we propose a unified solution where the same framework can generate both kind of representations. | ||||
| Address | November 2018 | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Ernest Valveny | |
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| ISSN | ISBN | 978-84-948531-0-4 | Medium | ||
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| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Gho2018 | Serial | 3217 | ||
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| Author | Gholamreza Anbarjafari; Sergio Escalera | ||||
| Title | Human-Robot Interaction: Theory and Application | Type | Book Whole | ||
| Year | 2018 | Publication | Human-Robot Interaction: Theory and Application | Abbreviated Journal | |
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| ISSN | ISBN | 978-1-78923-316-2 | Medium | ||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ AnE2018 | Serial | 3216 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | One-view occlusion detection for stereo matching with a fully connected CRF model | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 28 | Issue | 6 | Pages | 2936-2947 |
| Keywords | Stereo matching; energy minimization; fully connected MRF model; geodesic distance filter | ||||
| Abstract | In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method [15] to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution is proposed that we call one-view occlusion detection (OVOD). In contrast to the standard winner takes all (WTA) estimation, the proposed OVOD solution allows to find occluded regions in the disparity map and simultaneously improve the matching result. As a result we can perform only
one energy minimization process and avoid the cost calculation for the second view and the left-right check procedure. We show that the OVOD approach considerably improves results for cost augmentation and energy minimization techniques in comparison with the standard one-view affinity space implementation. We apply our method to the Middlebury data set and reach state-ofthe-art especially for median, average and mean squared error metrics. |
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| Notes | LAMP; 600.098; 600.109; 602.133; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ MoW2019 | Serial | 3221 | ||
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| Author | Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi | ||||
| Title | WiCV 2018: The Fourth Women In Computer Vision Workshop | Type | Conference Article | ||
| Year | 2018 | Publication | 4th Women in Computer Vision Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 1941-19412 | ||
| Keywords | Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning | ||||
| Abstract | We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | WiCV | ||
| Notes | DAG; 600.121; 600.129 | Approved | no | ||
| Call Number | Admin @ si @ DBR2018 | Serial | 3222 | ||
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| Author | Arnau Baro; Pau Riba; Alicia Fornes | ||||
| Title | A Starting Point for Handwritten Music Recognition | Type | Conference Article | ||
| Year | 2018 | Publication | 1st International Workshop on Reading Music Systems | Abbreviated Journal | |
| Volume | Issue | Pages | 5-6 | ||
| Keywords | Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA | ||||
| Abstract | In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. | ||||
| Address | Paris; France; September 2018 | ||||
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| Area | Expedition | Conference | WORMS | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRF2018 | Serial | 3223 | ||
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| Author | Laura Lopez-Fuentes; Alessandro Farasin; Harald Skinnemoen; Paolo Garza | ||||
| Title | Deep Learning models for passability detection of flooded roads | Type | Conference Article | ||
| Year | 2018 | Publication | MediaEval 2018 Multimedia Benchmark Workshop | Abbreviated Journal | |
| Volume | 2283 | Issue | Pages | ||
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| Abstract | In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task. | ||||
| Address | Sophia Antipolis; France; October 2018 | ||||
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| Area | Expedition | Conference | MediaEval | ||
| Notes | LAMP; 600.084; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ LFS2018 | Serial | 3224 | ||
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| Author | Anjan Dutta; Hichem Sahbi | ||||
| Title | Stochastic Graphlet Embedding | Type | Journal Article | ||
| Year | 2018 | Publication | IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | 1-14 | ||
| Keywords | Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality | ||||
| Abstract | Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
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| Notes | DAG; 602.167; 602.168; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DuS2018 | Serial | 3225 | ||
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| Author | Xim Cerda-Company; Xavier Otazu | ||||
| Title | Color induction in equiluminant flashed stimuli | Type | Journal Article | ||
| Year | 2019 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 36 | Issue | 1 | Pages | 22-31 |
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| Abstract | Color induction is the influence of the surrounding color (inducer) on the perceived color of a central region. There are two different types of color induction: color contrast (the color of the central region shifts away from that of the inducer) and color assimilation (the color shifts towards the color of the inducer). Several studies on these effects have used uniform and striped surrounds, reporting color contrast and color assimilation, respectively. Other authors [J. Vis. 12(1), 22 (2012) [CrossRef] ] have studied color induction using flashed uniform surrounds, reporting that the contrast is higher for shorter flash duration. Extending their study, we present new psychophysical results using both flashed and static (i.e., non-flashed) equiluminant stimuli for both striped and uniform surrounds. Similarly to them, for uniform surround stimuli we observed color contrast, but we did not obtain the maximum contrast for the shortest (10 ms) flashed stimuli, but for 40 ms. We only observed this maximum contrast for red, green, and lime inducers, while for a purple inducer we obtained an asymptotic profile along the flash duration. For striped stimuli, we observed color assimilation only for the static (infinite flash duration) red–green surround inducers (red first inducer, green second inducer). For the other inducers’ configurations, we observed color contrast or no induction. Since other studies showed that non-equiluminant striped static stimuli induce color assimilation, our results also suggest that luminance differences could be a key factor to induce it. | ||||
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| Notes | NEUROBIT; 600.120; 600.128 | Approved | no | ||
| Call Number | Admin @ si @ CeO2019 | Serial | 3226 | ||
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| Author | Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes | ||||
| Title | Optical Music Recognition by Long Short-Term Memory Networks | Type | Book Chapter | ||
| Year | 2018 | Publication | Graphics Recognition. Current Trends and Evolutions | Abbreviated Journal | |
| Volume | 11009 | Issue | Pages | 81-95 | |
| Keywords | Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory | ||||
| Abstract | Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach. | ||||
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| Publisher | Springer | Place of Publication | Editor | A. Fornes, B. Lamiroy | |
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | ISBN | 978-3-030-02283-9 | Medium | ||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG; 600.097; 601.302; 601.330; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ BRC2018 | Serial | 3227 | ||
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| Author | Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan | ||||
| Title | Synthetic Data Generation for End-to-End Thermal Infrared Tracking | Type | Journal Article | ||
| Year | 2019 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 28 | Issue | 4 | Pages | 1837 - 1850 |
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| Abstract | The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers. | ||||
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| Notes | LAMP; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ YGW2019 | Serial | 3228 | ||
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| Author | Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari | ||||
| Title | Objects as context for detecting their semantic parts | Type | Conference Article | ||
| Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 6907 - 6916 | ||
| Keywords | Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection | ||||
| Abstract | We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | LAMP; 600.109; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ GMF2018 | Serial | 3229 | ||
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| Author | Antonio Lopez | ||||
| Title | Pedestrian Detection Systems | Type | Book Chapter | ||
| Year | 2018 | Publication | Wiley Encyclopedia of Electrical and Electronics Engineering | Abbreviated Journal | |
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| Abstract | Pedestrian detection is a highly relevant topic for both advanced driver assistance systems (ADAS) and autonomous driving. In this entry, we review the ideas behind pedestrian detection systems from the point of view of perception based on computer vision and machine learning. | ||||
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| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ Lop2018 | Serial | 3230 | ||
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| Author | Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva | ||||
| Title | Assessment of intracoronary stent location and extension in intravascular ultrasound sequences | Type | Journal Article | ||
| Year | 2019 | Publication | Medical Physics | Abbreviated Journal | MEDPHYS |
| Volume | 46 | Issue | 2 | Pages | 484-493 |
| Keywords | IVUS; malapposition; stent; ultrasound | ||||
| Abstract | PURPOSE:
An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. METHODS: The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. RESULTS: The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. CONCLUSIONS: Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method. |
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| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ BCR2019 | Serial | 3231 | ||
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