| Records |
| Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; Josefina Mauri; Petia Radeva |
| Title |
Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation |
Type |
Journal Article |
| Year |
2013 |
Publication |
Ultrasound in Medicine and Biology |
Abbreviated Journal  |
UMB |
| Volume |
39 |
Issue |
9 |
Pages |
1698-712 |
| Keywords |
Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy |
| Abstract |
Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages. |
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MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ABC2013 |
Serial |
2313 |
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| Author |
G. Zahnd; Simone Balocco; A. Serusclat; P. Moulin; M. Orkisz; D. Vray |
| Title |
Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment Ultrasound in Medicine and Biology |
Type |
Journal Article |
| Year |
2015 |
Publication |
Ultrasound in Medicine and Biology |
Abbreviated Journal |
UMB |
| Volume |
41 |
Issue |
1 |
Pages |
339-345 |
| Keywords |
Arterial stiffness; Atherosclerosis; Common carotid artery; Longitudinal kinetics; Motion tracking; Ultrasound imaging |
| Abstract |
Longitudinal kinetics (LOKI) of the arterial wall consists of the shearing motion of the intima-media complex over the adventitia layer in the direction parallel to the blood flow during the cardiac cycle. The aim of this study was to investigate the local variability of LOKI amplitude along the length of the vessel. By use of a previously validated motion-estimation framework, 35 in vivo longitudinal B-mode ultrasound cine loops of healthy common carotid arteries were analyzed. Results indicated that LOKI amplitude is progressively attenuated along the length of the artery, as it is larger in regions located on the proximal side of the image (i.e., toward the heart) and smaller in regions located on the distal side of the image (i.e., toward the head), with an average attenuation coefficient of -2.5 ± 2.0%/mm. Reported for the first time in this study, this phenomenon is likely to be of great importance in improving understanding of atherosclerosis mechanisms, and has the potential to be a novel index of arterial stiffness. |
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MILAB |
Approved |
no |
| Call Number |
Admin @ si @ ZBS2014 |
Serial |
2556 |
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| Author |
David Vazquez; Antonio Lopez |
| Title |
Intrusion Classification in Intelligent Video Surveillance Systems |
Type |
Report |
| Year |
2008 |
Publication |
Estudis d'Enginyeria Superior en Informática |
Abbreviated Journal |
UAB |
| Volume |
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Issue |
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Pages |
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| Keywords |
Human detection; Car detection; Intrusion detection |
| Abstract |
An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system |
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Bellaterra, Spain |
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PFC |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ VL2008a |
Serial |
1670 |
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| Author |
Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
| Title |
CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
Type |
Journal |
| Year |
2020 |
Publication |
IEEE Transactions on Biometrics, Behavior, and Identity Science |
Abbreviated Journal |
TTBIS |
| Volume |
2 |
Issue |
2 |
Pages |
182 - 193 |
| Keywords |
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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 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 comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. 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/face-anti-spoofing/welcome/challengecvpr2019?authuser=0 |
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| Notes |
HuPBA; no proj |
Approved |
no |
| Call Number |
Admin @ si @ ZLW2020 |
Serial |
3412 |
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| Author |
David Masip; Agata Lapedriza; Jordi Vitria |
| Title |
Boosted Online Learning for Face Recognition |
Type |
Journal Article |
| Year |
2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
Abbreviated Journal |
TSMCB |
| Volume |
39 |
Issue |
2 |
Pages |
530–538 |
| Keywords |
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| Abstract |
Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
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Edition |
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| ISSN |
1083–4419 |
ISBN |
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Medium |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ MLV2009 |
Serial |
1155 |
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| Author |
Fadi Dornaika; Bogdan Raducanu |
| Title |
Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
Type |
Journal Article |
| Year |
2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
Abbreviated Journal |
TSMCB |
| Volume |
39 |
Issue |
4 |
Pages |
935–944 |
| Keywords |
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| Abstract |
Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
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| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ DoR2009a |
Serial |
1218 |
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| Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
| Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
Type |
Journal Article |
| Year |
2011 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
Abbreviated Journal |
TSMCB |
| Volume |
41 |
Issue |
2 |
Pages |
497-506 |
| Keywords |
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| Abstract |
In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
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1083-4419 |
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| Notes |
MILAB; DAG;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ EFP2011 |
Serial |
1784 |
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| Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Ludmila I. Kuncheva |
| Title |
Occlusion handling via random subspace classifiers for human detection |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Systems, Man, and Cybernetics (Part B) |
Abbreviated Journal |
TSMCB |
| Volume |
44 |
Issue |
3 |
Pages |
342-354 |
| Keywords |
Pedestriand Detection; occlusion handling |
| Abstract |
This paper describes a general method to address partial occlusions for human detection in still images. The Random Subspace Method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach’s capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labelling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes |
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Edition |
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| ISSN |
2168-2267 |
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| Notes |
ADAS; 605.203; 600.057; 600.054; 601.042; 601.187; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ MVL2014 |
Serial |
2213 |
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| Author |
Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez |
| Title |
Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) |
Abbreviated Journal |
TSMCB |
| Volume |
44 |
Issue |
12 |
Pages |
2379-2390 |
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| Abstract |
The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. |
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2168-2267 |
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| Notes |
HuPBA; ISE; 600.078;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ LRE2014 |
Serial |
2442 |
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| Author |
Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera |
| Title |
A Gesture Recognition System for Detecting Behavioral Patterns of ADHD |
Type |
Journal Article |
| Year |
2016 |
Publication |
IEEE Transactions on System, Man and Cybernetics, Part B |
Abbreviated Journal |
TSMCB |
| Volume |
46 |
Issue |
1 |
Pages |
136-147 |
| Keywords |
Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data |
| Abstract |
We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. |
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HuPBA; MILAB; |
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no |
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Admin @ si @ BHE2016 |
Serial |
2566 |
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| Author |
Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure |
| Title |
3D Perception With Slanted Stixels on GPU |
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Journal Article |
| Year |
2021 |
Publication |
IEEE Transactions on Parallel and Distributed Systems |
Abbreviated Journal |
TPDS |
| Volume |
32 |
Issue |
10 |
Pages |
2434-2447 |
| Keywords |
Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure |
| Abstract |
This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. |
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ADAS; 600.124; 600.118 |
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no |
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Admin @ si @ HEV2021 |
Serial |
3561 |
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| Author |
Albert Gordo; Florent Perronnin; Yunchao Gong; Svetlana Lazebnik |
| Title |
Asymmetric Distances for Binary Embeddings |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
36 |
Issue |
1 |
Pages |
33-47 |
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| Abstract |
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH), PCA Embedding (PCAE), PCA Embedding with random rotations (PCAE-RR), and PCA Embedding with iterative quantization (PCAE-ITQ). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. |
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0162-8828 |
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DAG; 600.045; 605.203; 600.077 |
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no |
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Admin @ si @ GPG2014 |
Serial |
2272 |
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| Author |
E. Provenzi; Carlo Gatta; M. Fierro; A. Rizzi |
| Title |
A Spatially Variant White-Patch and Gray-World Method for Color Image Enhancement Driven by Local Constant |
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Journal |
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2008 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
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30 |
Issue |
10 |
Pages |
1757–1770 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ PGF2008 |
Serial |
1001 |
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| Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez |
| Title |
Domain Adaptation of Deformable Part-Based Models |
Type |
Journal Article |
| Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
36 |
Issue |
12 |
Pages |
2367-2380 |
| Keywords |
Domain Adaptation; Pedestrian Detection |
| Abstract |
The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors. |
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Edition |
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| ISSN |
0162-8828 |
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| Notes |
ADAS; 600.057; 600.054; 601.217; 600.076 |
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no |
| Call Number |
ADAS @ adas @ XRV2014b |
Serial |
2436 |
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| Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
| Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
Type |
Journal Article |
| Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
| Volume |
31 |
Issue |
9 |
Pages |
1630–1644 |
| Keywords |
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| Abstract |
The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RVT2009 |
Serial |
1220 |
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