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Author Debora Gil; Sergio Vera; Agnes Borras; Albert Andaluz; Miguel Angel Gonzalez Ballester
Title Anatomical Medial Surfaces with Efficient Resolution of Branches Singularities Type Journal Article
Year 2017 Publication Medical Image Analysis Abbreviated Journal MIA
Volume 35 Issue Pages 390-402
Keywords Medial Representations; Shape Recognition; Medial Branching Stability ; Singular Points
Abstract Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a con dent application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an ecient GPU-CPU implementation using standard image processing tools. We show the method computational eciency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies.
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Publisher Elsevier B.V. Place of Publication Editor
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Notes (down) IAM; 600.060; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ GVB2017 Serial 2775
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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Noelia Cubero de Frutos; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell
Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
Year 2017 Publication European Respiratory Journal Abbreviated Journal ERJ
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Notes (down) IAM Approved no
Call Number Admin @ si @ DGC2017b Serial 3632
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Author Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza
Title Evolving weighting schemes for the Bag of Visual Words Type Journal Article
Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications
Volume 28 Issue 5 Pages 925–939
Keywords Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision
Abstract The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
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Publisher Place of Publication Editor Springer
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Notes (down) HUPBA;MV; no menciona Approved no
Call Number Admin @ si @ EPE2017 Serial 2743
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Author Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio
Title Computing quantitative indicators of structural renal damage in pediatric DMSA scans Type Journal Article
Year 2017 Publication Revista Española de Medicina Nuclear e Imagen Molecular Abbreviated Journal REMNIM
Volume 36 Issue 2 Pages 72-77
Keywords
Abstract OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.
MATERIALS AND METHODS:
From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.
RESULTS:
A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).
CONCLUSIONS:
The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators.
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Notes (down) HuPBA;MILAB; no menciona Approved no
Call Number Admin @ si @ SDE2017 Serial 2842
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Author Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla
Title Editorial: Special Issue on Computational Intelligence for Vision and Robotics Type Journal Article
Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications
Volume 28 Issue 5 Pages 853–854
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Notes (down) HuPBA;MILAB; no menciona Approved no
Call Number Admin @ si @ GGE2017 Serial 2845
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Author Cristina Palmero; Jordi Esquirol; Vanessa Bayo; Miquel Angel Cos; Pouya Ahmadmonfared; Joan Salabert; David Sanchez; Sergio Escalera
Title Automatic Sleep System Recommendation by Multi-modal RBG-Depth-Pressure Anthropometric Analysis Type Journal Article
Year 2017 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 122 Issue 2 Pages 212–227
Keywords Sleep system recommendation; RGB-Depth data Pressure imaging; Anthropometric landmark extraction; Multi-part human body segmentation
Abstract This paper presents a novel system for automatic sleep system recommendation using RGB, depth and pressure information. It consists of a validated clinical knowledge-based model that, along with a set of prescription variables extracted automatically, obtains a personalized bed design recommendation. The automatic process starts by performing multi-part human body RGB-D segmentation combining GrabCut, 3D Shape Context descriptor and Thin Plate Splines, to then extract a set of anthropometric landmark points by applying orthogonal plates to the segmented human body. The extracted variables are introduced to the computerized clinical model to calculate body circumferences, weight, morphotype and Body Mass Index categorization. Furthermore, pressure image analysis is performed to extract pressure values and at-risk points, which are also introduced to the model to eventually obtain the final prescription of mattress, topper, and pillow. We validate the complete system in a set of 200 subjects, showing accurate category classification and high correlation results with respect to manual measures.
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Notes (down) HuPBA;MILAB; 303.100 Approved no
Call Number Admin @ si @ PEB2017 Serial 2765
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Author Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Julio C. S. Jacques Junior; Xavier Baro; Evelyne Viegas; Yagmur Gucluturk; Umut Guclu; Marcel A. J. van Gerven; Rob van Lier; Meysam Madadi; Stephane Ayache
Title Design of an Explainable Machine Learning Challenge for Video Interviews Type Conference Article
Year 2017 Publication International Joint Conference on Neural Networks Abbreviated Journal
Volume Issue Pages
Keywords
Abstract This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration.
Address Anchorage; Alaska; USA; May 2017
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Area Expedition Conference IJCNN
Notes (down) HUPBA; no proj Approved no
Call Number Admin @ si @ EGE2017 Serial 2922
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Author Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera
Title Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey Type Book Chapter
Year 2017 Publication Gesture Recognition Abbreviated Journal
Volume Issue Pages 539-578
Keywords Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies
Abstract Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research.
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Notes (down) HUPBA; no proj Approved no
Call Number Admin @ si @ ACB2017a Serial 2981
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Author Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera
Title A survey on deep learning based approaches for action and gesture recognition in image sequences Type Conference Article
Year 2017 Publication 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
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Abstract The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions.
We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research.
Address Washington; USA; May 2017
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Area Expedition Conference FG
Notes (down) HUPBA; no proj Approved no
Call Number Admin @ si @ ACB2017b Serial 2982
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Author Sergio Escalera; Vassilis Athitsos; Isabelle Guyon
Title Challenges in Multi-modal Gesture Recognition Type Book Chapter
Year 2017 Publication Abbreviated Journal
Volume Issue Pages 1-60
Keywords Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM
Abstract This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.
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Notes (down) HuPBA; no proj Approved no
Call Number Admin @ si @ EAG2017 Serial 3008
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad
Title Locality Regularized Group Sparse Coding for Action Recognition Type Journal Article
Year 2017 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 158 Issue Pages 106-114
Keywords Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition
Abstract Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.
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Notes (down) HuPBA; no proj Approved no
Call Number Admin @ si @ BGE2017 Serial 3014
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Author Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari
Title Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases Type Conference Article
Year 2017 Publication 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
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Abstract In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists.
Address Washington; DC; USA; May 2017
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Publisher Place of Publication Editor
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Area Expedition Conference FG
Notes (down) HUPBA; no menciona Approved no
Call Number Admin @ si @ LJG2017 Serial 2924
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Author Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari
Title Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification Type Conference Article
Year 2017 Publication 12th IEEE International Conference on Automatic Face and Gesture Recognition Abbreviated Journal
Volume Issue Pages
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Abstract We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions.
Address Washington; DC; USA; May 2017
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Area Expedition Conference FG
Notes (down) HUPBA; no menciona Approved no
Call Number Admin @ si @ LRL2017 Serial 2925
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Author Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno
Title Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era" Type Conference Article
Year 2017 Publication Workshop on Big Data Analytics and Machine Learning for Data Communication Networks Abbreviated Journal
Volume Issue Pages 43-48
Keywords mobile networks; call detail records; human mobility
Abstract The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes,
have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users.
By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “
always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations.
Address UCLA; USA; August 2017
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-5054-9 Medium
Area Expedition Conference ACMW (SIGCOMM)
Notes (down) HuPBA; no menciona Approved no
Call Number Admin @ si @ FPT2017 Serial 2980
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Author Andrei Polzounov; Artsiom Ablavatski; Sergio Escalera; Shijian Lu; Jianfei Cai
Title WordFences: Text Localization and Recognition Type Conference Article
Year 2017 Publication 24th International Conference on Image Processing Abbreviated Journal
Volume Issue Pages
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Abstract
Address Beijing; China; September 2017
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Area Expedition Conference ICIP
Notes (down) HUPBA; no menciona Approved no
Call Number Admin @ si @ PAE2017 Serial 3007
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