Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	286–300 of 738 records found matching your query (RSS \| history):

Search & Display Options

Select All Deselect All

[1–10] << 11 12 13 14 15 16 17 18 19 20 >> [21–30]

List View

Citations

Details

	Records
	Author	Maria Salamo; Sergio Escalera
	Title	Increasing Retrieval Quality in Conversational Recommenders			Type	Journal Article
	Year	2011	Publication	IEEE Transactions on Knowledge and Data Engineering	Abbreviated Journal	TKDE
	Volume	99	Issue		Pages	1-1
	Keywords
	Abstract	IF JCR CCIA 2.286 2009 24/103 JCR Impact Factor 2010: 1.851 A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing speciﬁc preference values. Incremental Critiquing is a conversational recommender system that uses critiquing as a feedback to efﬁciently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user’s soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns Turing the recommendation process, we propose two novel exponential reinforcement learning approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisﬁed critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a global weighting approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology signiﬁcantly improves recommendation efﬁciency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches
	Address
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1041-4347	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ SaE2011			Serial	1713
Permanent link to this record



	Author	Antonio Lopez; Ernest Valveny; Juan J. Villanueva
	Title	Real-time quality control of surgical material packaging by artificial vision			Type	Journal Article
	Year	2005	Publication	Assembly Automation	Abbreviated Journal
	Volume	25	Issue	3	Pages
	Keywords
	Abstract	IF: 0.061)
	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	ADAS;DAG			Approved	no
	Call Number	ADAS @ adas @ LVV2005			Serial	552
Permanent link to this record



	Author	A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva
	Title	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
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS;OR;MV			Approved	no
	Call Number	ADAS @ adas @ PVL2001			Serial	155
Permanent link to this record



	Author	M. Bressan; Jordi Vitria
	Title	Nonparametric Discriminant Analysis and Nearest Neighbor Classification			Type	Journal Article
	Year	2003	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	24	Issue	15	Pages	2743–2749
	Keywords
	Abstract	IF: 0.809
	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	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ BrV2003b			Serial	367
Permanent link to this record



	Author	Cristina Cañero; Petia Radeva
	Title	Vesselness enhancement diffusion			Type	Journal Article
	Year	2003	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	24	Issue	16	Pages	3141–3151
	Keywords
	Abstract	IF: 0.809
	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	BCNPCL @ bcnpcl @ CaR2003			Serial	371
Permanent link to this record



	Author	David Guillamet; Jordi Vitria
	Title	Evaluation of distance metrics for recognition based on non-negative matrix factorization			Type	Journal Article
	Year	2003	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	24	Issue	9-10	Pages	1599 –1605
	Keywords
	Abstract	IF: 0.809
	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	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ GuV2003b			Serial	380
Permanent link to this record



	Author	David Guillamet; Jordi Vitria; B. Shiele
	Title	Introducing a weighted non-negative matrix factorization for image classification			Type	Journal Article
	Year	2003	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	24	Issue	14	Pages	2447–2454
	Keywords
	Abstract	IF: 0.809
	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	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ GVS2003			Serial	382
Permanent link to this record



	Author	A. Sanfeliu; Juan J. Villanueva
	Title	An approach of visual motion analysis			Type	Journal Article
	Year	2005	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	26	Issue	3	Pages	355–368
	Keywords
	Abstract	IF: 1.138
	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	ISE @ ise @ SaV2005			Serial	561
Permanent link to this record



	Author	Jaume Amores; Petia Radeva
	Title	Registration and Retrieval of Highly Elastic Bodies using Contextual Information			Type	Journal Article
	Year	2005	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	26	Issue	11	Pages	1720–1731
	Keywords
	Abstract	IF: 1.138
	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	ADAS;MILAB			Approved	no
	Call Number	ADAS @ adas @ AmR2005b			Serial	592
Permanent link to this record



	Author	Naveen Onkarappa; Angel Sappa
	Title	Speed and Texture: An Empirical Study on Optical-Flow Accuracy in ADAS Scenarios			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	15	Issue	1	Pages	136-147
	Keywords
	Abstract	IF: 3.064 Increasing mobility in everyday life has led to the concern for the safety of automotives and human life. Computer vision has become a valuable tool for developing driver assistance applications that target such a concern. Many such vision-based assisting systems rely on motion estimation, where optical flow has shown its potential. A variational formulation of optical flow that achieves a dense flow field involves a data term and regularization terms. Depending on the image sequence, the regularization has to appropriately be weighted for better accuracy of the flow field. Because a vehicle can be driven in different kinds of environments, roads, and speeds, optical-flow estimation has to be accurately computed in all such scenarios. In this paper, we first present the polar representation of optical flow, which is quite suitable for driving scenarios due to the possibility that it offers to independently update regularization factors in different directional components. Then, we study the influence of vehicle speed and scene texture on optical-flow accuracy. Furthermore, we analyze the relationships of these specific characteristics on a driving scenario (vehicle speed and road texture) with the regularization weights in optical flow for better accuracy. As required by the work in this paper, we have generated several synthetic sequences along with ground-truth flow fields.
	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	1524-9050	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ OnS2014a			Serial	2386
Permanent link to this record



	Author	Bogdan Raducanu; Fadi Dornaika
	Title	A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning			Type	Journal Article
	Year	2012	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	45	Issue	6	Pages	2432-2444
	Keywords
	Abstract	IF= 2.61 IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0031-3203	ISBN		Medium
	Area		Expedition		Conference
	Notes	OR; MV			Approved	no
	Call Number	Admin @ si @ RaD2012a			Serial	1884
Permanent link to this record



	Author	Sergio Escalera; Xavier Baro; Jordi Vitria; Petia Radeva; Bogdan Raducanu
	Title	Social Network Extraction and Analysis Based on Multimodal Dyadic Interaction			Type	Journal Article
	Year	2012	Publication	Sensors	Abbreviated Journal	SENS
	Volume	12	Issue	2	Pages	1702-1719
	Keywords
	Abstract	IF=1.77 (2010) Social interactions are a very important component in peopleís lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. For our study, we used a set of videos belonging to New York Timesí Blogging Heads opinion blog. The Social Network is represented as an oriented graph, whose directed links are determined by the Influence Model. The linksí weights are a measure of the ìinfluenceî a person has over the other. The states of the Influence Model encode automatically extracted audio/visual features from our videos using state-of-the art algorithms. Our results are reported in terms of accuracy of audio/visual data fusion for speaker segmentation and centrality measures used to characterize the extracted social network.
	Address
	Corporate Author				Thesis
	Publisher	Molecular Diversity Preservation International	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; OR;HuPBA;MV			Approved	no
	Call Number	Admin @ si @ EBV2012			Serial	1885
Permanent link to this record



	Author	Miguel Oliveira; Angel Sappa; Victor Santos
	Title	A probabilistic approach for color correction in image mosaicking applications			Type	Journal Article
	Year	2015	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	14	Issue	2	Pages	508 - 523
	Keywords	Color correction; image mosaicking; color transfer; color palette mapping functions
	Abstract	Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
	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	1057-7149	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS; 600.076			Approved	no
	Call Number	Admin @ si @ OSS2015b			Serial	2554
Permanent link to this record



	Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
	Title	Low-dimensional and Comprehensive Color Texture Description			Type	Journal Article
	Year	2012	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	116	Issue	I	Pages	54-67
	Keywords
	Abstract	Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are deﬁned independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges). A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent deﬁnition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is deﬁned from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap
	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	1077-3142	ISBN		Medium
	Area		Expedition		Conference
	Notes	CAT;CIC			Approved	no
	Call Number	Admin @ si @ ASV2012			Serial	1827
Permanent link to this record



	Author	Armin Mehri; Parichehr Behjati; Angel Sappa
	Title	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
	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	MSIAU			Approved	no
	Call Number	Admin @ si @ MBS2023			Serial	3876
Permanent link to this record