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Author |
Laura Igual; Xavier Perez Sala; Sergio Escalera; Cecilio Angulo; Fernando De la Torre |
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Title |
Continuous Generalized Procrustes Analysis |
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Journal Article |
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Year |
2014 |
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Pattern Recognition |
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PR |
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47 |
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2 |
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659–671 |
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Procrustes analysis; 2D shape model; Continuous approach |
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Abstract |
PR4883, PII: S0031-3203(13)00327-0
Two-dimensional shape models have been successfully applied to solve many problems in computer vision, such as object tracking, recognition, and segmentation. Typically, 2D shape models are learned from a discrete set of image landmarks (corresponding to projection of 3D points of an object), after applying Generalized Procustes Analysis (GPA) to remove 2D rigid transformations. However, the
standard GPA process suffers from three main limitations. Firstly, the 2D training samples do not necessarily cover a uniform sampling of all the 3D transformations of an object. This can bias the estimate of the shape model. Secondly, it can be computationally expensive to learn the shape model by sampling 3D transformations. Thirdly, standard GPA methods use only one reference shape, which can might be insufficient to capture large structural variability of some objects.
To address these drawbacks, this paper proposes continuous generalized Procrustes analysis (CGPA).
CGPA uses a continuous formulation that avoids the need to generate 2D projections from all the rigid 3D transformations. It builds an efficient (in space and time) non-biased 2D shape model from a set of 3D model of objects. A major challenge in CGPA is the need to integrate over the space of 3D rotations, especially when the rotations are parameterized with Euler angles. To address this problem, we introduce the use of the Haar measure. Finally, we extended CGPA to incorporate several reference shapes. Experimental results on synthetic and real experiments show the benefits of CGPA over GPA. |
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OR; HuPBA; 605.203; 600.046;MILAB |
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no |
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Admin @ si @ IPE2014 |
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2352 |
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Author |
R. Clariso; David Masip; A. Rius |
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Title |
Student projects empowering mobile learning in higher education |
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Journal |
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2014 |
Publication |
Revista de Universidad y Sociedad del Conocimiento |
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RUSC |
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11 |
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192-207 |
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1698-580X |
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OR;MV |
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no |
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Admin @ si @ CMR2014 |
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2619 |
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Author |
Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
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Title |
Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models |
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Journal Article |
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Year |
2011 |
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PloS one |
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Plos |
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6 |
Issue |
8 |
Pages |
e23323 |
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JCR Impact Factor 2010: 4.411
Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions |
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Public Library of Science |
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OR;MV |
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no |
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Admin @ si @ RMT2011 |
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1883 |
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Author |
Santiago Segui; Michal Drozdzal; Guillem Pascual; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
Generic Feature Learning for Wireless Capsule Endoscopy Analysis |
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Journal Article |
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Year |
2016 |
Publication |
Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
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Volume |
79 |
Issue |
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Pages |
163-172 |
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Keywords |
Wireless capsule endoscopy; Deep learning; Feature learning; Motility analysis |
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Abstract |
The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase). |
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OR; MILAB;MV; |
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no |
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Admin @ si @ SDP2016 |
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2836 |
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Author |
D. Seron; F. Moreso; C. Gratin; Jordi Vitria; E. Condom |
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Title |
Automated classification of renal interstitium and tubules by local texture analysis and a neural network |
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Year |
1996 |
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Analytical and Quantitative Cytology and Histology |
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18 |
Issue |
5 |
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410-9, PMID: 8908314 |
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OR;MV |
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BCNPCL @ bcnpcl @ SMG1996 |
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76 |
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