Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	1771–1785 of 3413 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

[101–110] << 111 112 113 114 115 116 117 118 119 120 >> [121–130]

List View

Citations

Details

	Records
	Author	Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa
	Title	Learning a Part-based Pedestrian Detector in Virtual World			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	15	Issue	5	Pages	2121-2131
	Keywords	Domain Adaptation; Pedestrian Detection; Virtual Worlds
	Abstract	Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM.
	Address
	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	1931-0587	ISBN	978-1-4673-2754-1	Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	ADAS @ adas @ XVL2014			Serial	2433
Permanent link to this record



	Author	Jiaolong Xu
	Title	Domain Adaptation of Deformable Part-based Models			Type	Book Whole
	Year	2015	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	On-board pedestrian detection is crucial for Advanced Driver Assistance Systems (ADAS). An accurate classication is fundamental for vision-based pedestrian detection. The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset. We start by exploiting the maximum detection accuracy of the virtual-world trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area.
	Address	April 2015
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Antonio Lopez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-943427-1-4	Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ Xu2015			Serial	2631
Permanent link to this record



	Author	Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari
	Title	Dominant and Complementary Emotion Recognition from Still Images of Faces			Type	Journal Article
	Year	2018	Publication	IEEE Access	Abbreviated Journal	ACCESS
	Volume	6	Issue		Pages	26391 - 26403
	Keywords
	Abstract	Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
	Address
	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
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ GLW2018			Serial	3122
Permanent link to this record



	Author	Jian Yang; Zhong Jin; Jing-Yu Yang; David Zhang; Alejandro F. Frangi
	Title	Essence of kernel Fisher discriminant: KPCA plus LDA			Type	Journal
	Year	2004	Publication	Pattern Recognition, 37(10): 2097–2100 (IF: 2.176)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	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
	Notes				Approved	no
	Call Number	Admin @ si @ YJY2004			Serial	480
Permanent link to this record



	Author	Jian Yang; Alejandro F. Frangi; Jing-Yu Yang; David Zhang; Zhong Jin
	Title	KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition			Type	Journal
	Year	2005	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):230–244 (IF: 3.810)	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	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
	Notes				Approved	no
	Call Number	Admin @ si @ YFY2005a			Serial	516
Permanent link to this record



	Author	Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora
	Title	Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts			Type	Conference Article
	Year	2018	Publication	16th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	528-533
	Keywords	Crowdsourcing; Gamification; Handwritten documents; Performance evaluation
	Abstract	Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance.
	Address	Niagara Falls, USA; August 2018
	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	ICFHR
	Notes	DAG; 600.097; 603.057; 600.121			Approved	no
	Call Number	Admin @ si @ CRF2018			Serial	3169
Permanent link to this record



	Author	Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi
	Title	Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images			Type	Conference Article
	Year	2021	Publication	4th International Conference on Historical Cryptology	Abbreviated Journal
	Volume		Issue		Pages	34-37
	Keywords
	Abstract	Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering.
	Address	Virtual; September 2021
	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	HistoCrypt
	Notes	DAG; 602.230; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ CSF2021			Serial	3617
Permanent link to this record



	Author	Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi
	Title	A Web-based Interactive Transcription Tool for Encrypted Manuscripts			Type	Conference Article
	Year	2020	Publication	3rd International Conference on Historical Cryptology	Abbreviated Journal
	Volume		Issue		Pages	52-59
	Keywords
	Abstract	Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available.
	Address	Virtual; June 2020
	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	HistoCrypt
	Notes	DAG; 600.140; 602.230; 600.121			Approved	no
	Call Number	Admin @ si @ CSF2020			Serial	3447
Permanent link to this record



	Author	Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari
	Title	Integrating Vision and Language for First Impression Personality Analysis			Type	Journal Article
	Year	2018	Publication	IEEE Multimedia	Abbreviated Journal	MULTIMEDIA
	Volume	25	Issue	2	Pages	24 - 33
	Keywords
	Abstract	The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness.
	Address
	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
	Notes	HUPBA; 602.133			Approved	no
	Call Number	Admin @ si @ GAL2018			Serial	3124
Permanent link to this record



	Author	Jean-Pascal Jacob; Mariella Dimiccoli; Lionel Moisan
	Title	Active skeleton for bacteria modeling			Type	Journal Article
	Year	2016	Publication	Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization	Abbreviated Journal	CMBBE
	Volume	5	Issue	4	Pages	274-286
	Keywords	Bacteria modelling; medial axis; active contours; active skeleton; shape contraints
	Abstract	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 modeling which provides several advantages: an easy computation of shape properties (perimeter, length, thickness, orientation), an improved boundary accuracy in noisy images, and a natural bacteria-centered 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 minimizing an energy function which incorporates bacteria shape constraints. Experimental results on biological images and comparative evaluation of the performances validate the proposed approach for modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the proposed method can be found online at this http URL
	Address
	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
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ JDM2016			Serial	2711
Permanent link to this record



	Author	Jean-Pascal Jacob; Mariella Dimiccoli; L. Moisan
	Title	Active skeleton for bacteria modelling			Type	Journal Article
	Year	2017	Publication	Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization	Abbreviated Journal	CMBBE
	Volume	5	Issue	4	Pages	274-286
	Keywords
	Abstract	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.
	Address
	Corporate Author				Thesis
	Publisher	Taylor & Francis Group	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	MILAB;			Approved	no
	Call Number	Admin @ si @JDM2017			Serial	2784
Permanent link to this record



	Author	Jean-Marc Ogier; Wenyin Liu; Josep Llados (eds)
	Title	Graphics Recognition: Achievements, Challenges, and Evolution			Type	Book Whole
	Year	2010	Publication	8th International Workshop GREC 2009.	Abbreviated Journal
	Volume	6020	Issue		Pages
	Keywords
	Abstract
	Address	La Rochelle
	Corporate Author				Thesis
	Publisher	Springer Link	Place of Publication		Editor	Jean-Marc Ogier; Wenyin Liu; Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title	Lecture Notes in Computer Science	Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-13727-3	Medium
	Area		Expedition		Conference	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ OLL2010			Serial	1976
Permanent link to this record



	Author	Jean-Christophe Burie; J. Chazalon; M. Coustaty; S. Eskenazi; Muhammad Muzzamil Luqman; M. Mehri; Nibal Nayef; Jean-Marc Ogier; S. Prum; Marçal Rusiñol
	Title	ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc)			Type	Conference Article
	Year	2015	Publication	13th International Conference on Document Analysis and Recognition ICDAR2015	Abbreviated Journal
	Volume		Issue		Pages	1161 - 1165
	Keywords
	Abstract	Smartphones are enabling new ways of capture, hence arises the need for seamless and reliable acquisition and digitization of documents, in order to convert them to editable, searchable and a more human-readable format. Current stateof-the-art works lack databases and baseline benchmarks for digitizing mobile captured documents. We have organized a competition for mobile document capture and OCR in order to address this issue. The competition is structured into two independent challenges: smartphone document capture, and smartphone OCR. This report describes the datasets for both challenges along with their ground truth, details the performance evaluation protocols which we used, and presents the final results of the participating methods. In total, we received 13 submissions: 8 for challenge-I, and 5 for challenge-2.
	Address	Nancy; France; August 2015
	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	ICDAR
	Notes	DAG; 600.077; 601.223; 600.084			Approved	no
	Call Number	Admin @ si @ BCC2015			Serial	2681
Permanent link to this record



	Author	Jürgen Brauer; Wenjuan Gong; Jordi Gonzalez; Michael Arens
	Title	On the Effect of Temporal Information on Monocular 3D Human Pose Estimation			Type	Conference Article
	Year	2011	Publication	2nd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams	Abbreviated Journal
	Volume		Issue		Pages	906 - 913
	Keywords
	Abstract	We address the task of estimating 3D human poses from monocular camera sequences. Many works make use of multiple consecutive frames for the estimation of a 3D pose in a frame. Although such an approach should ease the pose estimation task substantially since multiple consecutive frames allow to solve for 2D projection ambiguities in principle, it has not yet been investigated systematically how much we can improve the 3D pose estimates when using multiple consecutive frames opposed to single frame information. In this paper we analyze the difference in quality of 3D pose estimates based on different numbers of consecutive frames from which 2D pose estimates are available. We validate the use of temporal information on two major different approaches for human pose estimation – modeling and learning approaches. The results of our experiments show that both learning and modeling approaches benefit from using multiple frames opposed to single frame input but that the benefit is small when the 2D pose estimates show a high quality in terms of precision.
	Address	Barcelona
	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	978-1-4673-0062-9	Medium
	Area		Expedition		Conference	ARTEMIS
	Notes	ISE			Approved	no
	Call Number	Admin @ si @BGG 2011			Serial	1860
Permanent link to this record



	Author	Jaykishan Patel; Alban Flachot; Javier Vazquez; David H. Brainard; Thomas S. A. Wallis; Marcus A. Brubaker; Richard F. Murray
	Title	A deep convolutional neural network trained to infer surface reflectance is deceived by mid-level lightness illusions			Type	Journal Article
	Year	2023	Publication	Journal of Vision	Abbreviated Journal	JV
	Volume	23	Issue	9	Pages	4817-4817
	Keywords
	Abstract	A long-standing view is that lightness illusions are by-products of strategies employed by the visual system to stabilize its perceptual representation of surface reflectance against changes in illumination. Computationally, one such strategy is to infer reflectance from the retinal image, and to base the lightness percept on this inference. CNNs trained to infer reflectance from images have proven successful at solving this problem under limited conditions. To evaluate whether these CNNs provide suitable starting points for computational models of human lightness perception, we tested a state-of-the-art CNN on several lightness illusions, and compared its behaviour to prior measurements of human performance. We trained a CNN (Yu & Smith, 2019) to infer reflectance from luminance images. The network had a 30-layer hourglass architecture with skip connections. We trained the network via supervised learning on 100K images, rendered in Blender, each showing randomly placed geometric objects (surfaces, cubes, tori, etc.), with random Lambertian reflectance patterns (solid, Voronoi, or low-pass noise), under randomized point+ambient lighting. The renderer also provided the ground-truth reflectance images required for training. After training, we applied the network to several visual illusions. These included the argyle, Koffka-Adelson, snake, White’s, checkerboard assimilation, and simultaneous contrast illusions, along with their controls where appropriate. The CNN correctly predicted larger illusions in the argyle, Koffka-Adelson, and snake images than in their controls. It also correctly predicted an assimilation effect in White's illusion. It did not, however, account for the checkerboard assimilation or simultaneous contrast effects. These results are consistent with the view that at least some lightness phenomena are by-products of a rational approach to inferring stable representations of physical properties from intrinsically ambiguous retinal images. Furthermore, they suggest that CNN models may be a promising starting point for new models of human lightness perception.
	Address
	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
	Notes	MACO; CIC			Approved	no
	Call Number	Admin @ si @ PFV2023			Serial	3890
Permanent link to this record