Publicacions CVC -- Query Results

	Publicacions CVC Home \| Show All \| Simple Search \| Advanced Search \| Add Record \| Import	Login Quick Search: Field: contains: ...
	2116–2130 of 3413 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

[131–140] << 141 142 143 144 145 146 147 148 149 150 >> [151–160]

List View

Citations

Details

	Records
	Author	M.Gomez; J.Mauri; E.Fernandez-Nofrerias; O.Rodriguez-Leor; C.Julia; Oriol Pujol; Petia Radeva
	Title	Diferenciacion de las estructuras del vaso coronario mediante el procesamiento de imagenes y el analisis de las diferentes texturas a partir de la ecografia intracoronaria			Type	Journal
	Year	2002	Publication	XXXVIII Congreso Nacional de la Sociedad Española de Cardiologia	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Madrid
	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;HuPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ GMF2002f			Serial	433
Permanent link to this record



	Author	M.Gomez; J.Mauri; E.Fernandez-Nofrerias; O.Rodriguez-Leor; C.Julia; Petia Radeva; David Rotger; V.Valle
	Title	Nuevos Avances para la correlacion de imagenes angiograficas y de ecograia intracoronaria.			Type	Miscellaneous
	Year	2002	Publication	Congreso de las Enfermedades Cardiovasculares, XXXVIII Congreso de la Sociedad Española de Cardiologia.	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Madrid, Espanya
	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 @ GMF2002c			Serial	309
Permanent link to this record



	Author	M.Gomez; Josefina Mauri; Eduard Fernandez-Nofrerias; Oriol Rodriguez-Leon; Carme Julia; Debora Gil; Petia Radeva
	Title	Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria			Type	Conference Article
	Year	2002	Publication	XXXVIII Congreso Nacional de la Sociedad Española de Cardiología.	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	IAM;ADAS;MILAB			Approved	no
	Call Number	IAM @ iam @ GMF2002d			Serial	1516
Permanent link to this record



	Author	M.J. Yzuel; J. Pladellorens; Joan Serrat; A. Dupuy
	Title	Application restauration and edge detection techniques in the calculation of left ventricular volumes.			Type	Conference Article
	Year	1993	Publication	Optics in Medicine, Biology and Environmental Research : Selected contributions to the first International Conference on Optics within Life Sciences (OWLS I)	Abbreviated Journal
	Volume		Issue		Pages	374-375
	Keywords
	Abstract
	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		ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ YPS1993			Serial	244
Permanent link to this record



	Author	Maciej Wielgosz; Antonio Lopez; Muhamad Naveed Riaz
	Title	CARLA-BSP: a simulated dataset with pedestrians			Type	Miscellaneous
	Year	2023	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestrian detection, autoencoding, pose estimation, and pose lifting. We also showcase baseline results.
	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			Approved	no
	Call Number	Admin @ si @ WLN2023			Serial	3866
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva
	Title	Towards social pattern characterization from egocentric photo-streams			Type	Journal Article
	Year	2018	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	171	Issue		Pages	104-117
	Keywords	Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks
	Abstract	Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams.
	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; no proj			Approved	no
	Call Number	Admin @ si @ ADC2018			Serial	3022
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	Towards social interaction detection in egocentric photo-streams			Type	Conference Article
	Year	2015	Publication	Proceedings of SPIE, 8th International Conference on Machine Vision , ICMV 2015	Abbreviated Journal
	Volume	9875	Issue		Pages
	Keywords
	Abstract	Detecting social interaction in videos relying solely on visual cues is a valuable task that is receiving increasing attention in recent years. In this work, we address this problem in the challenging domain of egocentric photo-streams captured by a low temporal resolution wearable camera (2fpm). The major difficulties to be handled in this context are the sparsity of observations as well as unpredictability of camera motion and attention orientation due to the fact that the camera is worn as part of clothing. Our method consists of four steps: multi-faces localization and tracking, 3D localization, pose estimation and analysis of f-formations. By estimating pair-to-pair interaction probabilities over the sequence, our method states the presence or absence of interaction with the camera wearer and specifies which people are more involved in the interaction. We tested our method over a dataset of 18.000 images and we show its reliability on our considered purpose. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
	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	ICMV
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ADR2015a			Serial	2702
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	Multi-Face Tracking by Extended Bag-of-Tracklets in Egocentric Videos			Type	Miscellaneous
	Year	2015	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Egocentric images offer a hands-free way to record daily experiences and special events, where social interactions are of special interest. A natural question that arises is how to extract and track the appearance of multiple persons in a social event captured by a wearable camera. In this paper, we propose a novel method to find correspondences of multiple-faces in low temporal resolution egocentric sequences acquired through a wearable camera. This kind of sequences imposes additional challenges to the multitracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution (2 fpm), abrupt changes in the field of view, in illumination conditions and in the target location are very frequent. To overcome such a difficulty, we propose to generate, for each detected face, a set of correspondences along the whole sequence that we call tracklet and to take advantage of their redundancy to deal with both false positive face detections and unreliable tracklets. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which are aimed to correspond to specific persons. Finally, a prototype tracklet is extracted for each eBoT. We validated our method over a dataset of 18.000 images from 38 egocentric sequences with 52 trackable persons and compared to the state-of-the-art methods, demonstrating its effectiveness and robustness.
	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 @ ADR2015b			Serial	2713
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams			Type	Journal Article
	Year	2016	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	149	Issue		Pages	146-156
	Keywords
	Abstract	Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.
	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 @ ADR2016b			Serial	2742
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	With whom do I interact with? Social interaction detection in egocentric photo-streams			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
	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
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ADR2016a			Serial	2791
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	With Whom Do I Interact? Detecting Social Interactions in Egocentric Photo-streams			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Given a user wearing a low frame rate wearable camera during a day, this work aims to automatically detect the moments when the user gets engaged into a social interaction solely by reviewing the automatically captured photos by the worn camera. The proposed method, inspired by the sociological concept of F-formation, exploits distance and orientation of the appearing individuals -with respect to the user- in the scene from a bird-view perspective. As a result, the interaction pattern over the sequence can be understood as a two-dimensional time series that corresponds to the temporal evolution of the distance and orientation features over time. A Long-Short Term Memory-based Recurrent Neural Network is then trained to classify each time series. Experimental evaluation over a dataset of 30.000 images has shown promising results on the proposed method for social interaction detection in egocentric photo-streams.
	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
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ADR2016d			Serial	2835
Permanent link to this record



	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	All the people around me: face clustering in egocentric photo streams			Type	Conference Article
	Year	2017	Publication	24th International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	face discovery; face clustering; deepmatching; bag-of-tracklets; egocentric photo-streams
	Abstract	arxiv1703.01790 Given an unconstrained stream of images captured by a wearable photo-camera (2fpm), we propose an unsupervised bottom-up approach for automatic clustering appearing faces into the individual identities present in these data. The problem is challenging since images are acquired under real world conditions; hence the visible appearance of the people in the images undergoes intensive variations. Our proposed pipeline consists of first arranging the photo-stream into events, later, localizing the appearance of multiple people in them, and finally, grouping various appearances of the same person across different events. Experimental results performed on a dataset acquired by wearing a photo-camera during one month, demonstrate the effectiveness of the proposed approach for the considered purpose.
	Address	Beijing; China; September 2017
	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	ICIP
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ EDR2017			Serial	3025
Permanent link to this record



	Author	Maedeh Aghaei; Petia Radeva
	Title	Bag-of-Tracklets for Person Tracking in Life-Logging Data			Type	Conference Article
	Year	2014	Publication	17th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume	269	Issue		Pages	35-44
	Keywords
	Abstract	By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the 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	978-1-61499-451-0	Medium
	Area		Expedition		Conference	CCIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ AgR2015			Serial	2607
Permanent link to this record



	Author	Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
	Title	Optimized CT-MR neurological image fusion framework using biologically inspired spiking neural model in hybrid ℓ1 - ℓ0 layer decomposition domain			Type	Journal Article
	Year	2021	Publication	Biomedical Signal Processing and Control	Abbreviated Journal	BSPC
	Volume	68	Issue		Pages	102535
	Keywords
	Abstract	Medical image fusion plays an important role in the clinical diagnosis of several critical neurological diseases by merging complementary information available in multimodal images. In this paper, a novel CT-MR neurological image fusion framework is proposed using an optimized biologically inspired feedforward neural model in two-scale hybrid ℓ1 − ℓ0 decomposition domain using gray wolf optimization to preserve the structural as well as texture information present in source CT and MR images. Initially, the source images are subjected to two-scale ℓ1 − ℓ0 decomposition with optimized parameters, giving a scale-1 detail layer, a scale-2 detail layer and a scale-2 base layer. Two detail layers at scale-1 and 2 are fused using an optimized biologically inspired neural model and weighted average scheme based on local energy and modified spatial frequency to maximize the preservation of edges and local textures, respectively, while the scale-2 base layer gets fused using choose max rule to preserve the background information. To optimize the hyper-parameters of hybrid ℓ1 − ℓ0 decomposition and biologically inspired neural model, a fitness function is evaluated based on spatial frequency and edge index of the resultant fused image obtained by adding all the fused components. The fusion performance is analyzed by conducting extensive experiments on different CT-MR neurological images. Experimental results indicate that the proposed method provides better-fused images and outperforms the other state-of-the-art fusion methods in both visual and quantitative assessments.
	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; no proj			Approved	no
	Call Number	Admin @ si @ DGR2021b			Serial	3636
Permanent link to this record



	Author	Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
	Title	Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization			Type	Journal Article
	Year	2021	Publication	International Journal of Imaging Systems and Technology	Abbreviated Journal	IMA
	Volume	31	Issue	4	Pages	2170-2188
	Keywords
	Abstract	Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.
	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; no menciona			Approved	no
	Call Number	Admin @ si @ DGR2021a			Serial	3630
Permanent link to this record