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Author |
Eloi Puertas; Miguel Angel Bautista; Daniel Sanchez; Sergio Escalera; Oriol Pujol |
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Title |
Learning to Segment Humans by Stacking their Body Parts, |
Type |
Conference Article |
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Year |
2014 |
Publication |
ECCV Workshop on ChaLearn Looking at People |
Abbreviated Journal |
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Volume |
8925 |
Issue |
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Pages |
685-697 |
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Keywords |
Human body segmentation; Stacked Sequential Learning |
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Abstract |
Human segmentation in still images is a complex task due to the wide range of body poses and drastic changes in environmental conditions. Usually, human body segmentation is treated in a two-stage fashion. First, a human body part detection step is performed, and then, human part detections are used as prior knowledge to be optimized by segmentation strategies. In this paper, we present a two-stage scheme based on Multi-Scale Stacked Sequential Learning (MSSL). We define an extended feature set by stacking a multi-scale decomposition of body
part likelihood maps. These likelihood maps are obtained in a first stage
by means of a ECOC ensemble of soft body part detectors. In a second stage, contextual relations of part predictions are learnt by a binary classifier, obtaining an accurate body confidence map. The obtained confidence map is fed to a graph cut optimization procedure to obtain the final segmentation. Results show improved segmentation when MSSL is included in the human segmentation pipeline. |
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ECCVW |
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Notes |
HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ PBS2014 |
Serial |
2553 |
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Author |
Oriol Pujol; Debora Gil; Petia Radeva |
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Title |
Fundamentals of Stop and Go active models |
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Journal Article |
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Year |
2005 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
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Volume |
23 |
Issue |
8 |
Pages |
681-691 |
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Keywords |
Deformable models; Geodesic snakes; Region-based segmentation |
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Abstract |
An efficient snake formulation should conform to the idea of picking the smoothest curve among all the shapes approximating an object of interest. In current geodesic snakes, the regularizing curvature also affects the convergence stage, hindering the latter at concave regions. In the present work, we make use of characteristic functions to define a novel geodesic formulation that decouples regularity and convergence. This term decoupling endows the snake with higher adaptability to non-convex shapes. Convergence is ensured by splitting the definition of the external force into an attractive vector field and a repulsive one. In our paper, we propose to use likelihood maps as approximation of characteristic functions of object appearance. The better efficiency and accuracy of our decoupled scheme are illustrated in the particular case of feature space-based segmentation. |
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Butterworth-Heinemann |
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Newton, MA, USA |
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0262-8856 |
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Notes |
IAM;MILAB;HuPBA |
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no |
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Call Number |
IAM @ iam @ PGR2005 |
Serial |
1629 |
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Author |
Bhalaji Nagarajan; Ricardo Marques; Marcos Mejia; Petia Radeva |
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Title |
Class-conditional Importance Weighting for Deep Learning with Noisy Labels |
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Conference Article |
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Year |
2022 |
Publication |
17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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Volume |
5 |
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Pages |
679-686 |
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Keywords |
Noisy Labeling; Loss Correction; Class-conditional Importance Weighting; Learning with Noisy Labels |
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Abstract |
Large-scale accurate labels are very important to the Deep Neural Networks to train them and assure high performance. However, it is very expensive to create a clean dataset since usually it relies on human interaction. To this purpose, the labelling process is made cheap with a trade-off of having noisy labels. Learning with Noisy Labels is an active area of research being at the same time very challenging. The recent advances in Self-supervised learning and robust loss functions have helped in advancing noisy label research. In this paper, we propose a loss correction method that relies on dynamic weights computed based on the model training. We extend the existing Contrast to Divide algorithm coupled with DivideMix using a new class-conditional weighted scheme. We validate the method using the standard noise experiments and achieved encouraging results. |
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Virtual; February 2022 |
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VISAPP |
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Notes |
MILAB; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ NMM2022 |
Serial |
3798 |
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Author |
Carles Fernandez; Jordi Gonzalez; Xavier Roca |
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Title |
Automatic Learning of Background Semantics in Generic Surveilled Scenes |
Type |
Conference Article |
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Year |
2010 |
Publication |
11th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
6313 |
Issue |
II |
Pages |
678–692 |
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Abstract |
Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically infer semantics in locations where motion occurs, and such inferences are typically limited to abnormality. Thus, it is interesting to design contributions that automatically categorize more specific semantic regions. State-of-the-art approaches for unsupervised scene labeling exploit trajectory data to segment areas like sources, sinks, or waiting zones. Our method, in addition, incorporates scene-independent knowledge to assign more meaningful labels like crosswalks, sidewalks, or parking spaces. First, a spatiotemporal scene model is obtained from trajectory analysis. Subsequently, a so-called GI-MRF inference process reinforces spatial coherence, and incorporates taxonomy-guided smoothness constraints. Our method achieves automatic and effective labeling of conceptual regions in urban scenarios, and is robust to tracking errors. Experimental validation on 5 surveillance databases has been conducted to assess the generality and accuracy of the segmentations. The resulting scene models are used for model-based behavior analysis. |
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Address |
Crete (Greece) |
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Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-15551-2 |
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Conference |
ECCV |
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Notes |
ISE |
Approved |
no |
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Call Number |
ISE @ ise @ FGR2010 |
Serial |
1439 |
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Permanent link to this record |
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Author |
Debora Gil; Oriol Rodriguez; J. Mauri; Petia Radeva |
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Title |
Statistical descriptors of the Myocardial perfusion in angiographic images |
Type |
Conference Article |
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Year |
2006 |
Publication |
Proc. Computers in Cardiology |
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Pages |
677-680 |
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Keywords |
Anisotropic processing; intravascular ultrasound (IVUS); vessel border segmentation; vessel structure classification. |
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Abstract |
Restoration of coronary flow after primary percutaneous coronary intervention in acute myocardial infarction does not always correlate with adequate myocardial perfusion. Recently, coronary angiography has been used to assess microcirculation integrity (Myocardial BlushAnalysis, MBA). Although MBA correlates with patient prognosis there are few image processing methods addressing objective perfusion quantification. The goal of this work is to develop statistical descriptors of the myocardial dyeing pattern allowing objective assessment of myocardial perfusion. Experiments on healthy right coronary arteries show that our approach allows reliable measurements without any specific image acquisition protocol. |
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Notes |
IAM;MILAB |
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no |
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Call Number |
IAM @ iam @ GRR2006 |
Serial |
1528 |
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Author |
Anna Esposito; Italia Cirillo; Antonietta Esposito; Leopoldina Fortunati; Gian Luca Foresti; Sergio Escalera; Nikolaos Bourbakis |
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Title |
Impairments in decoding facial and vocal emotional expressions in high functioning autistic adults and adolescents |
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Conference Article |
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Year |
2020 |
Publication |
Faces and Gestures in E-health and welfare workshop |
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667-674 |
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Virtual; November 2020 |
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FGW |
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Notes |
HUPBA |
Approved |
no |
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Call Number |
Admin @ si @ ECE2020 |
Serial |
3516 |
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Permanent link to this record |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Error-Correcting Output Codes Library |
Type |
Journal Article |
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Year |
2010 |
Publication |
Journal of Machine Learning Research |
Abbreviated Journal |
JMLR |
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Volume |
11 |
Issue |
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Pages |
661-664 |
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Abstract |
(Feb):661−664
In this paper, we present an open source Error-Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multi-class categorization problems. This library contains both state-of-the-art coding (one-versus-one, one-versus-all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) and decoding designs (hamming, euclidean, inverse hamming, laplacian, β-density, attenuated, loss-based, probabilistic kernel-based, and loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier. |
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ISSN |
1532-4435 |
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Notes |
MILAB;HUPBA |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EPR2010c |
Serial |
1286 |
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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 |
Type |
Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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Volume |
47 |
Issue |
2 |
Pages |
659–671 |
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Keywords |
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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Notes |
OR; HuPBA; 605.203; 600.046;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ IPE2014 |
Serial |
2352 |
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Author |
Gemma Roig; Xavier Boix; F. de la Torre; Joan Serrat; C. Vilella |
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Title |
Hierarchical CRF with product label spaces for parts-based Models |
Type |
Conference Article |
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Year |
2011 |
Publication |
IEEE Conference on Automatic Face and Gesture Recognition |
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Volume |
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Issue |
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Pages |
657-664 |
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Keywords |
Shape; Computational modeling; Principal component analysis; Random variables; Color; Upper bound; Facial features |
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Abstract |
Non-rigid object detection is a challenging an open research problem in computer vision. It is a critical part in many applications such as image search, surveillance, human-computer interaction or image auto-annotation. Most successful approaches to non-rigid object detection make use of part-based models. In particular, Conditional Random Fields (CRF) have been successfully embedded into a discriminative parts-based model framework due to its effectiveness for learning and inference (usually based on a tree structure). However, CRF-based approaches do not incorporate global constraints and only model pairwise interactions. This is especially important when modeling object classes that may have complex parts interactions (e.g. facial features or body articulations), because neglecting them yields an oversimplified model with suboptimal performance. To overcome this limitation, this paper proposes a novel hierarchical CRF (HCRF). The main contribution is to build a hierarchy of part combinations by extending the label set to a hierarchy of product label spaces. In order to keep the inference computation tractable, we propose an effective method to reduce the new label set. We test our method on two applications: facial feature detection on the Multi-PIE database and human pose estimation on the Buffy dataset. |
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Santa Barbara, CA, USA, 2011 |
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ADAS |
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no |
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Call Number |
Admin @ si @ RBT2011 |
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1862 |
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Author |
Xavier Perez Sala; Fernando De la Torre; Laura Igual; Sergio Escalera; Cecilio Angulo |
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Title |
Subspace Procrustes Analysis |
Type |
Conference Article |
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Year |
2014 |
Publication |
ECCV Workshop on ChaLearn Looking at People |
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Volume |
8925 |
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654-668 |
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Abstract |
Procrustes Analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Then, a non-rigid 2-D model is computed by modeling (e.g., PCA) the residual. Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes Subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can simultaneously model all rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling dierent views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more ecient in space and time. Experiments using SPA to learn 2-D models of bodies from motion capture data illustrate the benets of our approach. |
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ECCVW |
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OR; HuPBA;MILAB |
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no |
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Admin @ si @ PTI2014 |
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2539 |
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Author |
Suman Ghosh; Ernest Valveny |
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Title |
A Sliding Window Framework for Word Spotting Based on Word Attributes |
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Conference Article |
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2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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652-661 |
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Word spotting; Sliding window; Word attributes |
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In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets. |
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Santiago de Compostela; June 2015 |
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Springer International Publishing |
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LNCS |
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0302-9743 |
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978-3-319-19389-2 |
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IbPRIA |
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DAG; 600.077 |
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no |
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Admin @ si @ GhV2015b |
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2716 |
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Author |
Bartlomiej Twardowski; Pawel Zawistowski; Szymon Zaborowski |
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Title |
Metric Learning for Session-Based Recommendations |
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Conference Article |
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Year |
2021 |
Publication |
43rd edition of the annual BCS-IRSG European Conference on Information Retrieval |
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12656 |
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650-665 |
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Session-based recommendations; Deep metric learning; Learning to rank |
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Session-based recommenders, used for making predictions out of users’ uninterrupted sequences of actions, are attractive for many applications. Here, for this task we propose using metric learning, where a common embedding space for sessions and items is created, and distance measures dissimilarity between the provided sequence of users’ events and the next action. We discuss and compare metric learning approaches to commonly used learning-to-rank methods, where some synergies exist. We propose a simple architecture for problem analysis and demonstrate that neither extensively big nor deep architectures are necessary in order to outperform existing methods. The experimental results against strong baselines on four datasets are provided with an ablation study. |
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Virtual; March 2021 |
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ECIR |
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LAMP; 600.120 |
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no |
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Admin @ si @ TZZ2021 |
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3586 |
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Permanent link to this record |
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Author |
Joan Serrat; Ferran Diego; Felipe Lumbreras; Jose Manuel Alvarez; Antonio Lopez; C. Elvira |
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Title |
Dynamic Comparison of Headlights |
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Journal Article |
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2008 |
Publication |
Journal of Automobile Engineering |
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222 |
Issue |
5 |
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643–656 |
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video alignment |
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ADAS @ adas @ SDL2008a |
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958 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Automatic Non-Rigid Temporal Alignment of IVUS Sequences |
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2012 |
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15th International Conference on Medical Image Computing and Computer Assisted Intervention |
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1 |
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642-650 |
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Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability. |
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Nice, France |
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Springer-Verlag Berlin, Heidelberg |
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978-3-642-33414-6 |
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MILAB |
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Admin @ si @ ABC2012 |
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2168 |
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Debora Gil; Oriol Rodriguez-Leor; Petia Radeva; J. Mauri |
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Myocardial Perfusion Characterization From Contrast Angiography Spectral Distribution |
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2008 |
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IEEE Transactions on Medical Imaging |
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27 |
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5 |
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641-649 |
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Contrast angiography; myocardial perfusion; spectral analysis. |
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Despite recovering a normal coronary flow after acute myocardial infarction, percutaneous coronary intervention does not guarantee a proper perfusion (irrigation) of the infarcted area. This damage in microcirculation integrity may detrimentally affect the patient survival. Visual assessment of the myocardium opacification in contrast angiography serves to define a subjective score of the microcirculation integrity myocardial blush analysis (MBA). Although MBA correlates with patient prognosis its visual assessment is a very difficult task that requires of a highly expertise training in order to achieve a good intraobserver and interobserver agreement. In this paper, we provide objective descriptors of the myocardium staining pattern by analyzing the spectrum of the image local statistics. The descriptors proposed discriminate among the different phenomena observed in the angiographic sequence and allow defining an objective score of the myocardial perfusion. |
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IAM @ iam @ GRR2008 |
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