Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	1726–1740 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	Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton
	Title	Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis			Type	Journal Article
	Year	2016	Publication	IEEE Transactions on Pattern Analysis and Machine Intelligence	Abbreviated Journal	TPAMI
	Volume	28	Issue		Pages	1489 - 1491
	Keywords
	Abstract	The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others.
	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; ISE;MV;			Approved	no
	Call Number	Admin @ si @			Serial	2851
Permanent link to this record



	Author	Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima
	Title	CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions			Type	Conference Article
	Year	2016	Publication	16th International Conference for Integrated Care	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Poster
	Address	Barcelona; Spain; May 2016
	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	ICIC
	Notes	HuPBA; ISE;MV			Approved	no
	Call Number	Admin @ si @ ABE2016			Serial	2855
Permanent link to this record



	Author	Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund
	Title	Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields			Type	Journal Article
	Year	2016	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	80	Issue		Pages	208–215
	Keywords
	Abstract	This paper proposes a technique for RGB-D scene segmentation using Multi-class Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset. The approach shows that simple multi-modal features with the power of the MMSSL paradigm can achieve better performance than state of the art results on the same dataset.
	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; ISE;MILAB; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ TEG2016			Serial	2843
Permanent link to this record



	Author	Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Roca; Felipe Lumbreras
	Title	Multi-part body segmentation based on depth maps for soft biometry analysis			Type	Journal Article
	Year	2015	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	56	Issue		Pages	14-21
	Keywords	3D shape context; 3D point cloud alignment; Depth maps; Human body segmentation; Soft biometry analysis
	Abstract	This paper presents a novel method extracting biometric measures using depth sensors. Given a multi-part labeled training data, a new subject is aligned to the best model of the dataset, and soft biometrics such as lengths or circumference sizes of limbs and body are computed. The process is performed by training relevant pose clusters, defining a representative model, and fitting a 3D shape context descriptor within an iterative matching procedure. We show robust measures by applying orthogonal plates to body hull. We test our approach in a novel full-body RGB-Depth data set, showing accurate estimation of soft biometrics and better segmentation accuracy in comparison with random forest approach without requiring large training data.
	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; ISE; ADAS; 600.076;600.049; 600.063; 600.054; 302.018;MILAB			Approved	no
	Call Number	Admin @ si @ MEG2015			Serial	2588
Permanent link to this record



	Author	Meysam Madadi; Sergio Escalera; Alex Carruesco; Carlos Andujar; Xavier Baro; Jordi Gonzalez
	Title	Occlusion Aware Hand Pose Recovery from Sequences of Depth Images			Type	Conference Article
	Year	2017	Publication	12th IEEE International Conference on Automatic Face and Gesture Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	State-of-the-art approaches on hand pose estimation from depth images have reported promising results under quite controlled considerations. In this paper we propose a two-step pipeline for recovering the hand pose from a sequence of depth images. The pipeline has been designed to deal with images taken from any viewpoint and exhibiting a high degree of finger occlusion. In a first step we initialize the hand pose using a part-based model, fitting a set of hand components in the depth images. In a second step we consider temporal data and estimate the parameters of a trained bilinear model consisting of shape and trajectory bases. Results on a synthetic, highly-occluded dataset demonstrate that the proposed method outperforms most recent pose recovering approaches, including those based on CNNs.
	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	FG
	Notes	HUPBA; ISE; 602.143; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ MEC2017			Serial	2970
Permanent link to this record



	Author	Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon
	Title	Looking at People Special Issue			Type	Journal Article
	Year	2018	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	126	Issue	2-4	Pages	141-143
	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	HUPBA; ISE; 600.119			Approved	no
	Call Number	Admin @ si @ EGJ2018			Serial	3093
Permanent link to this record



	Author	Egils Avots; Meysam Madadi; Sergio Escalera; Jordi Gonzalez; Xavier Baro; Paul Pallin; Gholamreza Anbarjafari
	Title	From 2D to 3D geodesic-based garment matching			Type	Journal Article
	Year	2019	Publication	Multimedia Tools and Applications	Abbreviated Journal	MTAP
	Volume	78	Issue	18	Pages	25829–25853
	Keywords	Shape matching; Geodesic distance; Texture mapping; RGBD image processing; Gaussian mixture model
	Abstract	A new approach for 2D to 3D garment retexturing is proposed based on Gaussian mixture models and thin plate splines (TPS). An automatically segmented garment of an individual is matched to a new source garment and rendered, resulting in augmented images in which the target garment has been retextured using the texture of the source garment. We divide the problem into garment boundary matching based on Gaussian mixture models and then interpolate inner points using surface topology extracted through geodesic paths, which leads to a more realistic result than standard approaches. We evaluated and compared our system quantitatively by root mean square error (RMS) and qualitatively using the mean opinion score (MOS), showing the benefits of the proposed methodology on our gathered dataset.
	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; ISE; 600.098; 600.119; 602.133			Approved	no
	Call Number	Admin @ si @ AME2019			Serial	3317
Permanent link to this record



	Author	Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari
	Title	Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition	Abbreviated Journal
	Volume	10165	Issue		Pages
	Keywords
	Abstract	Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.
	Address	Cancun; Mexico; December 2016
	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	ICPR
	Notes	HuPBA; ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ BHR2016			Serial	2902
Permanent link to this record



	Author	Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven
	Title	End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks			Type	Miscellaneous
	Year	2017	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	arXiv:1703.03305 Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies. We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them.
	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; ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ GGM2017			Serial	2932
Permanent link to this record



	Author	Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez
	Title	End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data			Type	Journal Article
	Year	2022	Publication	IET Computer Vision	Abbreviated Journal	IETCV
	Volume	16	Issue	1	Pages	50-66
	Keywords	Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation
	Abstract	Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets.
	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; ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ MEB2022			Serial	3652
Permanent link to this record



	Author	Martha Mackay; Fernando Alonso; Pere Salamero; Xavier Baro; Jordi Gonzalez; Sergio Escalera
	Title	Care and caring: future proofing the new demographics			Type	Conference Article
	Year	2015	Publication	6th International Carers Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	With an ageing population, the issue of care provision is becoming increasingly important. The simple aspiration of the majority of older people is to live safely and well at home. Housing will be part of health & care integration in the following years and decades. A higher proportion of people will have to rely on informal care through family, friends, neighbors and others who provide care to an older person in need of assistance (around 80% of care across the EU). They do not usually have a formal status and are usually unpaid. We need to ensure that all disabled or chronically ill people can get the help they need without overburdening their families. The physical and emotional stress of carers is one of the dangers that this dependency can bring. To prevent carers burnout it is necessary to provide new solutions that are affordable and user friendly for the families and caregivers.
	Address	Gothenburg; Sweden; September 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	CARERS
	Notes	HuPBA; ISE; 600.078;MV			Approved	no
	Call Number	Admin @ si @ MAS2015b			Serial	2678
Permanent link to this record



	Author	Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez
	Title	Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments			Type	Journal Article
	Year	2014	Publication	IEEE Transactions on Systems, Man and Cybernetics (Part B)	Abbreviated Journal	TSMCB
	Volume	44	Issue	12	Pages	2379-2390
	Keywords
	Abstract	The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios.
	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	2168-2267	ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA; ISE; 600.078;MILAB			Approved	no
	Call Number	Admin @ si @ LRE2014			Serial	2442
Permanent link to this record



	Author	Sergio Escalera; Jordi Gonzalez; Xavier Baro; Miguel Reyes; Oscar Lopes; Isabelle Guyon; V. Athitsos; Hugo Jair Escalante
	Title	Multi-modal Gesture Recognition Challenge 2013: Dataset and Results			Type	Conference Article
	Year	2013	Publication	15th ACM International Conference on Multimodal Interaction	Abbreviated Journal
	Volume		Issue		Pages	445-452
	Keywords
	Abstract	The recognition of continuous natural gestures is a complex and challenging problem due to the multi-modal nature of involved visual cues (e.g. fingers and lips movements, subtle facial expressions, body pose, etc.), as well as technical limitations such as spatial and temporal resolution and unreliable depth cues. In order to promote the research advance on this field, we organized a challenge on multi-modal gesture recognition. We made available a large video database of 13; 858 gestures from a lexicon of 20 Italian gesture categories recorded with a KinectTM camera, providing the audio, skeletal model, user mask, RGB and depth images. The focus of the challenge was on user independent multiple gesture learning. There are no resting positions and the gestures are performed in continuous sequences lasting 1-2 minutes, containing between 8 and 20 gesture instances in each sequence. As a result, the dataset contains around 1:720:800 frames. In addition to the 20 main gesture categories, ‘distracter’ gestures are included, meaning that additional audio and gestures out of the vocabulary are included. The final evaluation of the challenge was defined in terms of the Levenshtein edit distance, where the goal was to indicate the real order of gestures within the sequence. 54 international teams participated in the challenge, and outstanding results were obtained by the first ranked participants.
	Address	Sidney; Australia; December 2013
	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	978-1-4503-2129-7	Medium
	Area		Expedition		Conference	ICMI
	Notes	HUPBA; ISE; 600.063;MV			Approved	no
	Call Number	Admin @ si @ EGB2013			Serial	2373
Permanent link to this record



	Author	Sergio Escalera; Xavier Baro; Jordi Gonzalez; Miguel Angel Bautista; Meysam Madadi; Miguel Reyes; Victor Ponce; Hugo Jair Escalante; Jaime Shotton; Isabelle Guyon
	Title	ChaLearn Looking at People Challenge 2014: Dataset and Results			Type	Conference Article
	Year	2014	Publication	ECCV Workshop on ChaLearn Looking at People	Abbreviated Journal
	Volume	8925	Issue		Pages	459-473
	Keywords	Human Pose Recovery; Behavior Analysis; Action and in- teractions; Multi-modal gestures; recognition
	Abstract	This paper summarizes the ChaLearn Looking at People 2014 challenge data and the results obtained by the participants. The competition was split into three independent tracks: human pose recovery from RGB data, action and interaction recognition from RGB data sequences, and multi-modal gesture recognition from RGB-Depth sequences. For all the tracks, the goal was to perform user-independent recognition in sequences of continuous images using the overlapping Jaccard index as the evaluation measure. In this edition of the ChaLearn challenge, two large novel data sets were made publicly available and the Microsoft Codalab platform were used to manage the competition. Outstanding results were achieved in the three challenge tracks, with accuracy results of 0.20, 0.50, and 0.85 for pose recovery, action/interaction recognition, and multi-modal gesture recognition, respectively.
	Address
	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	ECCVW
	Notes	HuPBA; ISE; 600.063;MV			Approved	no
	Call Number	Admin @ si @ EBG2014			Serial	2529
Permanent link to this record



	Author	Sergio Escalera; Jordi Gonzalez; Xavier Baro; Pablo Pardo; Junior Fabian; Marc Oliu; Hugo Jair Escalante; Ivan Huerta; Isabelle Guyon
	Title	ChaLearn Looking at People 2015 new competitions: Age Estimation and Cultural Event Recognition			Type	Conference Article
	Year	2015	Publication	IEEE International Joint Conference on Neural Networks IJCNN2015	Abbreviated Journal
	Volume		Issue		Pages	1-8
	Keywords
	Abstract	Following previous series on Looking at People (LAP) challenges [1], [2], [3], in 2015 ChaLearn runs two new competitions within the field of Looking at People: age and cultural event recognition in still images. We propose thefirst crowdsourcing application to collect and label data about apparent age of people instead of the real age. In terms of cultural event recognition, tens of categories have to be recognized. This involves scene understanding and human analysis. This paper summarizes both challenges and data, providing some initial baselines. The results of the first round of the competition were presented at ChaLearn LAP 2015 IJCNN special session on computer vision and robotics http://www.dtic.ua.es/∼jgarcia/IJCNN2015. Details of the ChaLearn LAP competitions can be found at http://gesture.chalearn.org/.
	Address	Killarney; Ireland; July 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	IJCNN
	Notes	HuPBA; ISE; 600.063; 600.078;MV			Approved	no
	Call Number	Admin @ si @ EGB2015			Serial	2591
Permanent link to this record