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Author |
Maya Dimitrova; Ch. Roumenin; Siya Lozanova; David Rotger; Petia Radeva |
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Title |
An Interface System Based on Multimodal Principle for Cardiological Diagnosis Assistance |
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Conference Article |
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Year |
2007 |
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International Conference On Computer Systems And Technologies |
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IIIB.4 |
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1–6 |
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Bulgaria |
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CompSysTech’07 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ DRL2007 |
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833 |
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Author |
Dani Rowe; Ignasi Rius; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Robust Particle Filtering for Object Tracking |
Type |
Miscellaneous |
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Year |
2005 |
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13th International Conference on Image Analysis and Processing (ICIAP’2005), LNCS 3617: 1158–1165, ISBN 3–540–28869–4 |
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Cagliary (Italy) |
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no |
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ISE @ ise @ RRG2005e |
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577 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva |
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Title |
Circular Blurred Shape Model for Symbol Spotting in Documents |
Type |
Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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Pages |
1985-1988 |
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Abstract |
Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors. |
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Cairo, Egypt |
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978-1-4244-5653-6 |
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ICIP |
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MILAB;HuPBA;DAG |
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no |
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Call Number |
BCNPCL @ bcnpcl @ EFP2009b |
Serial |
1184 |
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Author |
Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Automatic Ground-truthing using video registration for on-board detection algorithms |
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Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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Pages |
4389 - 4392 |
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Abstract |
Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ ADS2009 |
Serial |
1201 |
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Author |
Angel Sappa; Mohammad Rouhani |
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Title |
Efficient Distance Estimation for Fitting Implicit Quadric Surfaces |
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Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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Pages |
3521–3524 |
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Abstract |
This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ SaR2009 |
Serial |
1232 |
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Author |
Carlo Gatta; Petia Radeva |
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Title |
Bilateral Enhancers |
Type |
Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
Abbreviated Journal |
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Pages |
3161-3165 |
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Abstract |
Ten years ago the concept of bilateral filtering (BF) became popular in the image processing community. The core of the idea is to blend the effect of a spatial filter, as e.g. the Gaussian filter, with the effect of a filter that acts on image values. The two filters acts on orthogonal domains of a picture: the 2D lattice of the image support and the intensity (or color) domain. The BF approach is an intuitive way to blend these two filters giving rise to algorithms that perform difficult tasks requiring a relatively simple design. In this paper we extend the concept of BF, proposing the bilateral enhancers (BE). We show how to design proper functions to obtain an edge-preserving smoothing and a selective sharpening. Moreover, we show that the proposed algorithm can perform edge-preserving smoothing and selective sharpening simultaneously in a single filtering. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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Notes |
MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ GaR2009b |
Serial |
1243 |
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Author |
Santiago Segui; Laura Igual; Jordi Vitria |
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Title |
Weighted Bagging for Graph based One-Class Classifiers |
Type |
Conference Article |
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Year |
2010 |
Publication |
9th International Workshop on Multiple Classifier Systems |
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Volume |
5997 |
Issue |
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Pages |
1-10 |
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Abstract |
Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. Minimum Spanning Tree Class Descriptor (MSTCD) was presented as a method that achieves better accuracies than other one-class classifiers in high dimensional data. However, the presence of outliers in the target class severely harms the performance of this classifier. In this paper we propose two bagging strategies for MSTCD that reduce the influence of outliers in training data. We show the improved performance on both real and artificially contaminated data. |
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Cairo, Egypt |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-12126-5 |
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MCS |
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Notes |
MILAB;OR;MV |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ SIV2010 |
Serial |
1284 |
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Author |
Ajian Liu; Jun Wan; Sergio Escalera; Hugo Jair Escalante; Zichang Tan; Qi Yuan; Kai Wang; Chi Lin; Guodong Guo; Isabelle Guyon; Stan Z. Li |
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Title |
Multi-Modal Face Anti-Spoofing Attack Detection Challenge at CVPR2019 |
Type |
Conference Article |
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Year |
2019 |
Publication |
IEEE International Conference on Computer Vision and Pattern Recognition-Workshop |
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Anti-spoofing attack detection is critical to guarantee the security of face-based authentication and facial analysis systems. Recently, a multi-modal face anti-spoofing dataset, CASIA-SURF, has been released with the goal of boosting research in this important topic. CASIA-SURF is the largest public data set for facial anti-spoofing attack detection in terms of both, diversity and modalities: it comprises 1,000 subjects and 21,000 video samples. We organized a challenge around this novel resource to boost research in the subject. The Chalearn LAP multi-modal face anti-spoofing attack detection challenge attracted more than 300 teams for the development phase with a total of 13 teams qualifying for the final round. This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results. We analyze the top ranked solutions and draw conclusions derived from the competition. In addition we outline future work directions. |
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California; June 2019 |
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CVPRW |
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Notes |
HuPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ LWE2019 |
Serial |
3329 |
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Author |
Shifeng Zhang; Xiaobo Wang; Ajian Liu; Chenxu Zhao; Jun Wan; Sergio Escalera; Hailin Shi; Zezheng Wang; Stan Z. Li |
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Title |
A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
Type |
Conference Article |
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Year |
2019 |
Publication |
32nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Pages |
919-928 |
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Abstract |
Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of 1,000 subjects with 21,000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at https://sites.google.com/qq.com/chalearnfacespoofingattackdete/. |
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California; June 2019 |
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CVPR |
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Notes |
HuPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ ZWL2019 |
Serial |
3331 |
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Author |
Ciprian Corneanu; Meysam Madadi; Sergio Escalera; Aleix M. Martinez |
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Title |
What does it mean to learn in deep networks? And, how does one detect adversarial attacks? |
Type |
Conference Article |
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Year |
2019 |
Publication |
32nd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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Pages |
4752-4761 |
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Abstract |
The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why. i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case. |
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California; June 2019 |
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CVPR |
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Notes |
HuPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ CME2019 |
Serial |
3332 |
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Author |
Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz |
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Title |
LSTA: Long Short-Term Attention for Egocentric Action Recognition |
Type |
Conference Article |
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Year |
2019 |
Publication |
32nd IEEE Conference on Computer Vision and Pattern Recognition |
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Pages |
9946-9955 |
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Abstract |
Egocentric activity recognition is one of the most challenging tasks in video analysis. It requires a fine-grained discrimination of small objects and their manipulation. While some methods base on strong supervision and attention mechanisms, they are either annotation consuming or do not take spatio-temporal patterns into account. In this paper we propose LSTA as a mechanism to focus on features from spatial relevant parts while attention is being tracked smoothly across the video sequence. We demonstrate the effectiveness of LSTA on egocentric activity recognition with an end-to-end trainable two-stream architecture, achieving state-of-the-art performance on four standard benchmarks. |
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California; June 2019 |
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CVPR |
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Notes |
HuPBA; no proj |
Approved |
no |
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Call Number |
Admin @ si @ SEL2019 |
Serial |
3333 |
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Author |
Georg Langs; Petia Radeva; David Rotger; Francesc Carreras |
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Title |
Building and Registering Parameterized 3D Models of Vessel Trees for Visualization during Intervention |
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Miscellaneous |
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Year |
2004 |
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17th International Conference on Pattern Recognition, ICPR’04 |
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Cambridge, United Kingdom |
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MILAB |
Approved |
no |
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BCNPCL @ bcnpcl @ LRR2004b |
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463 |
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Author |
Gemma Sanchez; Josep Llados; K. Tombre |
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Title |
An Error-Correction Graph Grammar to Recognize Textured Symbols. |
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Miscellaneous |
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Year |
2001 |
Publication |
Fourth IAPR International Workshop on Graphics Recognition, GREC 2001, 135–146. |
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Canada |
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DAG |
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no |
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DAG @ dag @ SLT2001b |
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164 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers |
Type |
Conference Article |
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Year |
2013 |
Publication |
26th Canadian Conference on Artificial Intelligence |
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Volume |
7884 |
Issue |
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Pages |
1-12 |
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Keywords |
Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature |
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Abstract |
Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology. |
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Address |
Canada; May 2013 |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
0302-9743 |
ISBN |
978-3-642-38456-1 |
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Conference |
AI |
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Notes |
HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ BGE2013b |
Serial |
2249 |
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Author |
Michael Villamizar; A. Sanfeliu; Juan Andrade |
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Title |
Orientation Invariant Features for Multiclass Object Recognition |
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Miscellaneous |
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Year |
2006 |
Publication |
11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 655–664 |
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Cancun (Mexico) |
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Admin @ si @ VSA2006b |
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664 |
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