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Author (down) X. Orriols; X. Binefa
Title An EM Algorithm for Video Summarization, Generative Model Approach. Type Miscellaneous
Year 2001 Publication Eighth International Conference on Computer Vision, IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence, 1:335–342. Abbreviated Journal
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Address Vancouver.
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Notes Approved no
Call Number Admin @ si @ OBi2001 Serial 199
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Author (down) X. Orriols; Lluis Barcelo; X. Binefa
Title Polynomial Fiber Description of Motion for Video Mosaicing, Proceeding ICIP 2001. Type Miscellaneous
Year 2001 Publication IEEE International Conference on Image Processing, Grecia, 1:1030–1033. Abbreviated Journal
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Notes Approved no
Call Number Admin @ si @ OBB2001a Serial 143
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Author (down) X. Binefa; Petia Radeva; J.A. Cortijo; J. Garcia
Title Contour detection and color influence in defocused environtments. Type Miscellaneous
Year 1998 Publication Abbreviated Journal
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ BRC1998 Serial 28
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Author (down) X. Binefa; J.M. Sanchez; Petia Radeva; Jordi Vitria
Title Linking Visual Cues and Semantic Terms Under Specific Digital Video Domains. Type Miscellaneous
Year 2000 Publication Journal of Visual Languages and Computing, 11(3):253–271. Abbreviated Journal
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Notes OR;MILAB;MV Approved no
Call Number BCNPCL @ bcnpcl @ BRS2000 Serial 337
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Author (down) Wenjuan Gong; Y.Huang; Jordi Gonzalez; Liang Wang
Title An Effective Solution to Double Counting Problem in Human Pose Estimation Type Miscellaneous
Year 2015 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords Pose estimation; double counting problem; mix-ture of parts Model
Abstract The mixture of parts model has been successfully applied to solve the 2D
human pose estimation problem either as an explicitly trained body part model
or as latent variables for pedestrian detection. Even in the era of massive
applications of deep learning techniques, the mixture of parts model is still
effective in solving certain problems, especially in the case with limited
numbers of training samples. In this paper, we consider using the mixture of
parts model for pose estimation, wherein a tree structure is utilized for
representing relations between connected body parts. This strategy facilitates
training and inferencing of the model but suffers from double counting
problems, where one detected body part is counted twice due to lack of
constrains among unconnected body parts. To solve this problem, we propose a
generalized solution in which various part attributes are captured by multiple
features so as to avoid the double counted problem. Qualitative and
quantitative experimental results on a public available dataset demonstrate the
effectiveness of our proposed method.

An Effective Solution to Double Counting Problem in Human Pose Estimation – ResearchGate. Available from: http://www.researchgate.net/publication/271218491AnEffectiveSolutiontoDoubleCountingProbleminHumanPose_Estimation [accessed Oct 22, 2015].
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Notes ISE; 600.078 Approved no
Call Number Admin @ si @ GHG2015 Serial 2590
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Author (down) W.Win; B.Bao; Q.Xu; Luis Herranz; Shuqiang Jiang
Title Editorial Note: Efficient Multimedia Processing Methods and Applications Type Miscellaneous
Year 2019 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 78 Issue 1 Pages
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Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ WBX2019 Serial 3257
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Author (down) Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera
Title Analisis de la Expresion Oral y Gestual en Proyectos Fin de Carrera Via un Sistema de Vision Artificial Type Miscellaneous
Year 2011 Publication Revista electronica de la asociacion de enseñantes universitarios de la informatica AENUI Abbreviated Journal ReVision
Volume 4 Issue 1 Pages 8-18
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Abstract La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación.
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ISSN 1989-1199 ISBN Medium
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Notes MILAB;HuPBA;MV Approved no
Call Number Admin @ si @ PGB2011c Serial 1783
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Author (down) V. Valev; B. Sankur; Petia Radeva
Title Generalized Non-Reducible Descriptors. Type Miscellaneous
Year 1997 Publication Technical Report. Abbreviated Journal
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Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ VSR1997 Serial 65
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Author (down) V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich
Title Pattern Recognition of Fragmented Objects with Adaptive Correlation Filters Type Miscellaneous
Year 2006 Publication Topical Meeting on Optoinformatics / Information Photonics, 150–151 Abbreviated Journal
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Address Saint-Petersburg (Russia)
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Notes ISE Approved no
Call Number ISE @ ise @ KMA2006b Serial 674
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Author (down) V. Chapaprieta; Ernest Valveny
Title Handwritten Digit Recognition Using Point Distribution Models. Type Miscellaneous
Year 2001 Publication Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis, 1:49–54. Abbreviated Journal
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Notes DAG Approved no
Call Number DAG @ dag @ ChV2001 Serial 83
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Author (down) 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
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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.
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Notes HuPBA; ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ GGM2017 Serial 2932
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Author (down) T. Alejandra Vidal; Andrew J. Davison; Juan Andrade; David W. Murray
Title Active Control for Single Camera SLAM Type Miscellaneous
Year 2006 Publication IEEE International Conference on Robotics and Automation, 1930–1936 Abbreviated Journal
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Address Orlando (Florida)
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Notes Approved no
Call Number DAG @ dag @ VDA2006 Serial 666
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Author (down) T. Alejandra Vidal; A. Sanfeliu; Juan Andrade
Title Autonomous Single Camera Exploration Type Miscellaneous
Year 2006 Publication Jornada de Recerca en Automatica, Visio i Robotica Abbreviated Journal
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Address Barcelona (Spain)
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Notes Approved no
Call Number Admin @ si @ VSA2006c Serial 680
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Author (down) Stefan Lonn; Petia Radeva; Mariella Dimiccoli
Title A picture is worth a thousand words but how to organize thousands of pictures? Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
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Abstract We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ LRD2018 Serial 3111
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Author (down) Spyridon Bakas; Mauricio Reyes; Andras Jakab; Stefan Bauer; Markus Rempfler; Alessandro Crimi; Russell Takeshi Shinohara; Christoph Berger; Sung Min Ha; Martin Rozycki; Marcel Prastawa; Esther Alberts; Jana Lipkova; John Freymann; Justin Kirby; Michel Bilello; Hassan Fathallah-Shaykh; Roland Wiest; Jan Kirschke; Benedikt Wiestler; Rivka Colen; Aikaterini Kotrotsou; Pamela Lamontagne; Daniel Marcus; Mikhail Milchenko; Arash Nazeri; Marc-Andre Weber; Abhishek Mahajan; Ujjwal Baid; Dongjin Kwon; Manu Agarwal; Mahbubul Alam; Alberto Albiol; Antonio Albiol; Varghese Alex; Tuan Anh Tran; Tal Arbel; Aaron Avery; Subhashis Banerjee; Thomas Batchelder; Kayhan Batmanghelich; Enzo Battistella; Martin Bendszus; Eze Benson; Jose Bernal; George Biros; Mariano Cabezas; Siddhartha Chandra; Yi-Ju Chang; Joseph Chazalon; Shengcong Chen; Wei Chen; Jefferson Chen; Kun Cheng; Meinel Christoph; Roger Chylla; Albert Clérigues; Anthony Costa; Xiaomeng Cui; Zhenzhen Dai; Lutao Dai; Eric Deutsch; Changxing Ding; Chao Dong; Wojciech Dudzik; Theo Estienne; Hyung Eun Shin; Richard Everson; Jonathan Fabrizio; Longwei Fang; Xue Feng; Lucas Fidon; Naomi Fridman; Huan Fu; David Fuentes; David G Gering; Yaozong Gao; Evan Gates; Amir Gholami; Mingming Gong; Sandra Gonzalez-Villa; J Gregory Pauloski; Yuanfang Guan; Sheng Guo; Sudeep Gupta; Meenakshi H Thakur; Klaus H Maier-Hein; Woo-Sup Han; Huiguang He; Aura Hernandez-Sabate; Evelyn Herrmann; Naveen Himthani; Winston Hsu; Cheyu Hsu; Xiaojun Hu; Xiaobin Hu; Yan Hu; Yifan Hu; Rui Hua
Title Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords BraTS; challenge; brain; tumor; segmentation; machine learning; glioma; glioblastoma; radiomics; survival; progression; RECIST
Abstract Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multiparametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e. 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in preoperative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that undergone gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
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Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ BRJ2018 Serial 3252
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