Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	2836–2850 of 3413 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

[171–180] << 181 182 183 184 185 186 187 188 189 190 >> [191–200]

List View

Citations

Details

	Records
	Author	Victor Ponce; Mario Gorga; Xavier Baro; Sergio Escalera
	Title	Human Behavior Analysis from Video Data Using Bag-of-Gestures			Type	Conference Article
	Year	2011	Publication	22nd International Joint Conference on Artificial Intelligence	Abbreviated Journal
	Volume	3	Issue		Pages	2836-2837
	Keywords
	Abstract	Human Behavior Analysis in Uncontrolled Environments can be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysis from a set of corporal language vocabulary. In this work, we present our achievements characterizing some simple behaviors from visual data on different real applications and discuss our plan for future work: low level vocabulary definition from bag-of-gesture units and high level modelling and inference of human behaviors.
	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	978-1-57735-516-8	Medium
	Area		Expedition		Conference	IJCAI
	Notes	HuPBA;MV			Approved	no
	Call Number	Admin @ si @ PGB2011b			Serial	1770
Permanent link to this record



	Author	Jiaolong Xu; Sebastian Ramos;David Vazquez; Antonio Lopez
	Title	Cost-sensitive Structured SVM for Multi-category Domain Adaptation			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3886 - 3891
	Keywords	Domain Adaptation; Pedestrian Detection
	Abstract	Domain adaptation addresses the problem of accuracy drop that a classifier may suffer when the training data (source domain) and the testing data (target domain) are drawn from different distributions. In this work, we focus on domain adaptation for structured SVM (SSVM). We propose a cost-sensitive domain adaptation method for SSVM, namely COSS-SSVM. In particular, during the re-training of an adapted classifier based on target and source data, the idea that we explore consists in introducing a non-zero cost even for correctly classified source domain samples. Eventually, we aim to learn a more targetoriented classifier by not rewarding (zero loss) properly classified source-domain training samples. We assess the effectiveness of COSS-SSVM on multi-category object recognition.
	Address	Stockholm; Sweden; August 2014
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	ADAS; 600.057; 600.054; 601.217; 600.076			Approved	no
	Call Number	ADAS @ adas @ XRV2014a			Serial	2434
Permanent link to this record



	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Generic Subclass Ensemble: A Novel Approach to Ensemble Classification			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1254 - 1259
	Keywords
	Abstract	Multiple classifier systems, also known as classifier ensembles, have received great attention in recent years because of their improved classification accuracy in different applications. In this paper, we propose a new general approach to ensemble classification, named generic subclass ensemble, in which each base classifier is trained with data belonging to a subset of classes, and thus discriminates among a subset of target categories. The ensemble classifiers are then fused using a combination rule. The proposed approach differs from existing methods that manipulate the target attribute, since in our approach individual classification problems are not restricted to two-class problems. We perform a series of experiments to evaluate the efficiency of the generic subclass approach on a set of benchmark datasets. Experimental results with multilayer perceptrons show that the proposed approach presents a viable alternative to the most commonly used ensemble classification approaches.
	Address	Stockholm; August 2014
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2014b			Serial	2445
Permanent link to this record



	Author	Mohammad Ali Bagheri; Gang Hu; Qigang Gao; Sergio Escalera
	Title	A Framework of Multi-Classifier Fusion for Human Action Recognition			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1260 - 1265
	Keywords
	Abstract	The performance of different action-recognition methods using skeleton joint locations have been recently studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of five action learning techniques, each performing the recognition task from a different perspective. The underlying rationale of the fusion approach is that different learners employ varying structures of input descriptors/features to be trained. These varying structures cannot be attached and used by a single learner. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selection of a poorly performing learner. This leads to having a more robust and general-applicable framework. Also, we propose two simple, yet effective, action description techniques. In order to improve the recognition performance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers' output, showing advanced performance of the proposed methodology.
	Address	Stockholm; Sweden; August 2014
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BHG2014			Serial	2446
Permanent link to this record



	Author	Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg
	Title	Scale Coding Bag-of-Words for Action Recognition			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1514-1519
	Keywords
	Abstract	Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image. Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods.
	Address	Stockholm; August 2014
	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	ICPR
	Notes	CIC; LAMP; 601.240; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ KWB2014			Serial	2450
Permanent link to this record



	Author	David Fernandez; Jon Almazan; Nuria Cirera; Alicia Fornes; Josep Llados
	Title	BH2M: the Barcelona Historical Handwritten Marriages database			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	256 - 261
	Keywords
	Abstract	This paper presents an image database of historical handwritten marriages records stored in the archives of Barcelona cathedral, and the corresponding meta-data addressed to evaluate the performance of document analysis algorithms. The contribution of this paper is twofold. First, it presents a complete ground truth which covers the whole pipeline of handwriting recognition research, from layout analysis to recognition and understanding. Second, it is the first dataset in the emerging area of genealogical document analysis, where documents are manuscripts pseudo-structured with specific lexicons and the interest is beyond pure transcriptions but context dependent.
	Address	Creete Island; Grecia; September 2014
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ FAC2014			Serial	2461
Permanent link to this record



	Author	Lluis Gomez; Dimosthenis Karatzas
	Title	MSER-based Real-Time Text Detection and Tracking			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3110 - 3115
	Keywords
	Abstract	We present a hybrid algorithm for detection and tracking of text in natural scenes that goes beyond the fulldetection approaches in terms of time performance optimization. A state-of-the-art scene text detection module based on Maximally Stable Extremal Regions (MSER) is used to detect text asynchronously, while on a separate thread detected text objects are tracked by MSER propagation. The cooperation of these two modules yields real time video processing at high frame rates even on low-resource devices.
	Address	Stockholm; August 2014
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.056; 601.158; 601.197; 600.077			Approved	no
	Call Number	Admin @ si @ GoK2014a			Serial	2492
Permanent link to this record



	Author	Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados
	Title	Embedding Document Structure to Bag-of-Words through Pair-wise Stable Key-regions			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2903 - 2908
	Keywords
	Abstract	Since the document structure carries valuable discriminative information, plenty of efforts have been made for extracting and understanding document structure among which layout analysis approaches are the most commonly used. In this paper, Distance Transform based MSER (DTMSER) is employed to efficiently extract the document structure as a dendrogram of key-regions which roughly correspond to structural elements such as characters, words and paragraphs. Inspired by the Bag of Words (BoW) framework, we propose an efficient method for structural document matching by representing the document image as a histogram of key-region pairs encoding structural relationships. Applied to the scenario of document image retrieval, experimental results demonstrate a remarkable improvement when comparing the proposed method with typical BoW and pyramidal BoW methods.
	Address	Stockholm; Sweden; August 2014
	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	ICPR
	Notes	DAG; 600.056; 600.061; 600.077			Approved	no
	Call Number	Admin @ si @ GRK2014b			Serial	2497
Permanent link to this record



	Author	P. Wang; V. Eglin; C. Garcia; C. Largeron; Josep Llados; Alicia Fornes
	Title	A Coarse-to-Fine Word Spotting Approach for Historical Handwritten Documents Based on Graph Embedding and Graph Edit Distance			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3074 - 3079
	Keywords	word spotting; coarse-to-fine mechamism; graphbased representation; graph embedding; graph edit distance
	Abstract	Effective information retrieval on handwritten document images has always been a challenging task, especially historical ones. In the paper, we propose a coarse-to-fine handwritten word spotting approach based on graph representation. The presented model comprises both the topological and morphological signatures of the handwriting. Skeleton-based graphs with the Shape Context labelled vertexes are established for connected components. Each word image is represented as a sequence of graphs. Aiming at developing a practical and efficient word spotting approach for large-scale historical handwritten documents, a fast and coarse comparison is first applied to prune the regions that are not similar to the query based on the graph embedding methodology. Afterwards, the query and regions of interest are compared by graph edit distance based on the Dynamic Time Warping alignment. The proposed approach is evaluated on a public dataset containing 50 pages of historical marriage license records. The results show that the proposed approach achieves a compromise between efficiency and accuracy.
	Address	Stockholm; Sweden; August 2014
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 600.061; 602.006; 600.077			Approved	no
	Call Number	Admin @ si @ WEG2014a			Serial	2515
Permanent link to this record



	Author	Claudio Baecchi; Francesco Turchini; Lorenzo Seidenari; Andrew Bagdanov; Alberto del Bimbo
	Title	Fisher vectors over random density forest for object recognition			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	4328-4333
	Keywords
	Abstract
	Address	Stockholm; Sweden; August 2014
	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	ICPR
	Notes	LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ BTS2014			Serial	2518
Permanent link to this record



	Author	Federico Bartoli; Giuseppe Lisanti; Svebor Karaman; Andrew Bagdanov; Alberto del Bimbo
	Title	Unsupervised scene adaptation for faster multi- scale pedestrian detection			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3534 - 3539
	Keywords
	Abstract
	Address	Stockholm; Sweden; August 2014
	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	ICPR
	Notes	LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ BLK2014			Serial	2519
Permanent link to this record



	Author	Francisco Cruz; Oriol Ramos Terrades
	Title	EM-Based Layout Analysis Method for Structured Documents			Type	Conference Article
	Year	2014	Publication	22nd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	315-320
	Keywords
	Abstract	In this paper we present a method to perform layout analysis in structured documents. We proposed an EM-based algorithm to fit a set of Gaussian mixtures to the different regions according to the logical distribution along the page. After the convergence, we estimate the final shape of the regions according to the parameters computed for each component of the mixture. We evaluated our method in the task of record detection in a collection of historical structured documents and performed a comparison with other previous works in this task.
	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	1051-4651	ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG; 602.006; 600.061; 600.077			Approved	no
	Call Number	Admin @ si @ CrR2014			Serial	2530
Permanent link to this record



	Author	Patricia Suarez; Dario Carpio; Angel Sappa
	Title	A Deep Learning Based Approach for Synthesizing Realistic Depth Maps			Type	Conference Article
	Year	2023	Publication	22nd International Conference on Image Analysis and Processing	Abbreviated Journal
	Volume	14234	Issue		Pages	369–380
	Keywords
	Abstract	This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality.
	Address	Udine; Italia; Setember 2023
	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	ICIAP
	Notes	MSIAU			Approved	no
	Call Number	Admin @ si @ SCS2023a			Serial	3968
Permanent link to this record



	Author	Md.Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig
	Title	Food Places Classification in Egocentric Images Using Siamese Neural Networks			Type	Conference Article
	Year	2019	Publication	22nd International Conference of the Catalan Association of Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages	145-151
	Keywords
	Abstract	Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
	Address	Illes Balears; October 2019
	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	CCIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ SBR2019			Serial	3368
Permanent link to this record



	Author	Jose Manuel Alvarez; Theo Gevers; Antonio Lopez
	Title	Learning Photometric Invariance from Diversified Color Model Ensembles			Type	Conference Article
	Year	2009	Publication	22nd IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	565–572
	Keywords	road detection
	Abstract	Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.
	Address	Miami (USA)
	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	1063-6919	ISBN	978-1-4244-3992-8	Medium
	Area		Expedition		Conference	CVPR
	Notes	ADAS;ISE			Approved	no
	Call Number	ADAS @ adas @ AGL2009			Serial	1169
Permanent link to this record