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Author Adria Ruiz; Joost Van de Weijer; Xavier Binefa
Title From emotions to action units with hidden and semi-hidden-task learning Type Conference Article
Year 2015 Publication 16th IEEE International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 3703-3711
Keywords
Abstract Limited annotated training data is a challenging problem in Action Unit recognition. In this paper, we investigate how the use of large databases labelled according to the 6 universal facial expressions can increase the generalization ability of Action Unit classifiers. For this purpose, we propose a novel learning framework: Hidden-Task Learning. HTL aims to learn a set of Hidden-Tasks (Action Units)for which samples are not available but, in contrast, training data is easier to obtain from a set of related VisibleTasks (Facial Expressions). To that end, HTL is able to exploit prior knowledge about the relation between Hidden and Visible-Tasks. In our case, we base this prior knowledge on empirical psychological studies providing statistical correlations between Action Units and universal facial expressions. Additionally, we extend HTL to Semi-Hidden Task Learning (SHTL) assuming that Action Unit training samples are also provided. Performing exhaustive experiments over four different datasets, we show that HTL and SHTL improve the generalization ability of AU classifiers by training them with additional facial expression data. Additionally, we show that SHTL achieves competitive performance compared with state-of-the-art Transductive Learning approaches which face the problem of limited training data by using unlabelled test samples during training.
Address Santiago de Chile; Chile; December 2015
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCV
Notes (down) LAMP; 600.068; 600.079 Approved no
Call Number Admin @ si @ RWB2015 Serial 2671
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Author Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika
Title Multi-observation Face Recognition in Videos based on Label Propagation Type Conference Article
Year 2015 Publication 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 Abbreviated Journal
Volume Issue Pages 10-17
Keywords
Abstract In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the
selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video
sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods.
Address Boston; USA; June 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 CVPRW
Notes (down) LAMP; 600.068; 600.072; Approved no
Call Number Admin @ si @ RBD2015 Serial 2627
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Author Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas
Title Evaluating Real-Time Mirroring of Head Gestures using Smart Glasses Type Conference Article
Year 2015 Publication 16th IEEE International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 452-460
Keywords
Abstract Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis.
Address Santiago de Chile; December 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 ICCVW
Notes (down) LAMP; 600.068; 600.072; Approved no
Call Number Admin @ si @ TRM2015 Serial 2722
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Author Manuel Graña; Bogdan Raducanu
Title Special Issue on Bioinspired and knowledge based techniques and applications Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume Issue Pages 1-3
Keywords
Abstract
Address
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ GrR2015 Serial 2598
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Author Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh
Title Facial expression recognition based on multi observations with application to social robotics Type Book Chapter
Year 2015 Publication Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance Abbreviated Journal
Volume Issue Pages 153-166
Keywords
Abstract Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication
which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression.
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Publisher Nova Science publishers Place of Publication Editor Bruce Flores
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes (down) LAMP; Approved no
Call Number Admin @ si @ DRB2015 Serial 2720
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Author Mikhail Mozerov; Joost Van de Weijer
Title Accurate stereo matching by two step global optimization Type Journal Article
Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 24 Issue 3 Pages 1153-1163
Keywords
Abstract In stereo matching cost filtering methods and energy minimization algorithms are considered as two different techniques. Due to their global extend energy minimization methods obtain good stereo matching results. However, they tend to fail in occluded regions, in which cost filtering approaches obtain better results. In this paper we intend to combine both approaches with the aim to improve overall stereo matching results. We show that a global optimization with a fully connected model can be solved by cost fil tering methods. Based on this observation we propose to perform stereo matching as a two-step energy minimization algorithm. We consider two MRF models: a fully connected model defined on the complete set of pixels in an image and a conventional locally connected model. We solve the energy minimization problem for the fully connected model, after which the marginal function of the solution is used as the unary potential in the locally connected MRF model. Experiments on the Middlebury stereo datasets show that the proposed method achieves state-of-the-arts results.
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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 1057-7149 ISBN Medium
Area Expedition Conference
Notes (down) ISE; LAMP; 600.079; 600.078 Approved no
Call Number Admin @ si @ MoW2015a Serial 2568
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Author Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca
Title A coarse-to-fine approach for fast deformable object detection Type Journal Article
Year 2015 Publication Pattern Recognition Abbreviated Journal PR
Volume 48 Issue 5 Pages 1844-1853
Keywords
Abstract We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part
placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
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Notes (down) ISE; 600.078; 602.005; 605.001; 302.012 Approved no
Call Number Admin @ si @ PVG2015 Serial 2628
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Author Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez
Title Chromatic shadow detection and tracking for moving foreground segmentation Type Journal Article
Year 2015 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 41 Issue Pages 42-53
Keywords Detecting moving objects; Chromatic shadow detection; Temporal local gradient; Spatial and Temporal brightness and angle distortions; Shadow tracking
Abstract Advanced segmentation techniques in the surveillance domain deal with shadows to avoid distortions when detecting moving objects. Most approaches for shadow detection are still typically restricted to penumbra shadows and cannot cope well with umbra shadows. Consequently, umbra shadow regions are usually detected as part of moving objects, thus a ecting the performance of the nal detection. In this paper we address the detection of both penumbra and umbra shadow regions. First, a novel bottom-up approach is presented based on gradient and colour models, which successfully discriminates between chromatic moving cast shadow regions and those regions detected as moving objects. In essence, those regions corresponding to potential shadows are detected based on edge partitioning and colour statistics. Subsequently (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for each potential shadow region for detecting the umbra shadow regions. Our second contribution re nes even further the segmentation results: a tracking-based top-down approach increases the performance of our bottom-up chromatic shadow detection algorithm by properly correcting non-detected shadows.
To do so, a combination of motion lters in a data association framework exploits the temporal consistency between objects and shadows to increase
the shadow detection rate. Experimental results exceed current state-of-the-
art in shadow accuracy for multiple well-known surveillance image databases which contain di erent shadowed materials and illumination conditions.
Address
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ISSN ISBN Medium
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Notes (down) ISE; 600.078; 600.063 Approved no
Call Number Admin @ si @ HHM2015 Serial 2703
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Author 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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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes (down) ISE; 600.078 Approved no
Call Number Admin @ si @ GHG2015 Serial 2590
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Author Sergio Escalera; Junior Fabian; Pablo Pardo; Xavier Baro; Jordi Gonzalez; Hugo Jair Escalante; Marc Oliu; Dusan Misevic; Ulrich Steiner; Isabelle Guyon
Title ChaLearn Looking at People 2015: Apparent Age and Cultural Event Recognition Datasets and Results Type Conference Article
Year 2015 Publication 16th IEEE International Conference on Computer Vision Workshops Abbreviated Journal
Volume Issue Pages 243 - 251
Keywords
Abstract Following previous series on Looking at People (LAP) competitions [14, 13, 11, 12, 2], in 2015 ChaLearn ran two new competitions within the field of Looking at People: (1) age estimation, and (2) cultural event recognition, both in
still images. We developed a crowd-sourcing application to collect and label data about the apparent age of people (as opposed to the real age). In terms of cultural event recognition, one hundred categories had to be recognized. These
tasks involved scene understanding and human body analysis. This paper summarizes both challenges and data, as well as the results achieved by the participants of the competition.
Address Santiago de Chile; December 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 ICCVW
Notes (down) ISE; 600.063; 600.078;MV Approved no
Call Number Admin @ si @ EFP2015 Serial 2704
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Author Ivan Huerta; Marco Pedersoli; Jordi Gonzalez; Alberto Sanfeliu
Title Combining where and what in change detection for unsupervised foreground learning in surveillance Type Journal Article
Year 2015 Publication Pattern Recognition Abbreviated Journal PR
Volume 48 Issue 3 Pages 709-719
Keywords Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance
Abstract Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art.
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Notes (down) ISE; 600.063; 600.078 Approved no
Call Number Admin @ si @ HPG2015 Serial 2589
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Author Wenjuan Gong; W.Zhang; Jordi Gonzalez; Y.Ren; Z.Li
Title Enhanced Asymmetric Bilinear Model for Face Recognition Type Journal Article
Year 2015 Publication International Journal of Distributed Sensor Networks Abbreviated Journal IJDSN
Volume Issue Pages Article ID 218514
Keywords
Abstract Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initializing the factor probabilities randomly, we initialize them with nearest neighbor method and optimize them for the test data. Above that, we update the factor model to be identified. We validate the proposed method on a designed data sample and extended Yale B dataset. The experiment results show that the enhanced asymmetric models give promising results and good recognition accuracies.
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Notes (down) ISE; 600.063; 600.078 Approved no
Call Number Admin @ si @ GZG2015 Serial 2592
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Author Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca
Title Factorized appearances for object detection Type Journal Article
Year 2015 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 138 Issue Pages 92–101
Keywords Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts
Abstract Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.

A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories.
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Notes (down) ISE; 600.063; 600.078 Approved no
Call Number Admin @ si @ GPG2015 Serial 2705
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Author Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil
Title Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy Type Journal Article
Year 2015 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 10 Issue 6 Pages 935-945
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Address
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Notes (down) IAM; MV; 600.075 Approved no
Call Number Admin @ si @ SBS2015a Serial 2611
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Author Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell
Title Quasi-real time digital assessment of Central Airway Obstruction Type Conference Article
Year 2015 Publication 3rd European congress for bronchology and interventional pulmonology ECBIP2015 Abbreviated Journal
Volume Issue Pages
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Abstract
Address Barcelona; Spain; April 2015
Corporate Author Thesis
Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ECBIP
Notes (down) IAM; MV; 600.075 Approved no
Call Number SGT2015 Serial 2612
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