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David Berga; Xose R. Fernandez-Vidal; Xavier Otazu; V. Leboran; Xose M. Pardo |
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Title |
Psychophysical evaluation of individual low-level feature influences on visual attention |
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Journal Article |
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Year |
2019 |
Publication |
Vision Research |
Abbreviated Journal  |
VR |
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154 |
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60-79 |
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Visual attention; Psychophysics; Saliency; Task; Context; Contrast; Center bias; Low-level; Synthetic; Dataset |
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In this study we provide the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of synthetically-generated image patterns. Design of visual stimuli was inspired by the ones used in previous psychophysical experiments, namely in free-viewing and visual searching tasks, to provide a total of 15 types of stimuli, divided according to the task and feature to be analyzed. Our interest is to analyze the influences of low-level feature contrast between a salient region and the rest of distractors, providing fixation localization characteristics and reaction time of landing inside the salient region. Eye-tracking data was collected from 34 participants during the viewing of a 230 images dataset. Results show that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. temporality of fixations, 4. task difficulty and 5. center bias. This experimentation proposes a new psychophysical basis for saliency model evaluation using synthetic images. |
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NEUROBIT; 600.128; 600.120 |
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Admin @ si @ BFO2019a |
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3274 |
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Author |
David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa |
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Title |
Pedestrian Detection Using AdaBoost Learning of Features and Vehicle Pitch Estimation |
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Miscellaneous |
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Year |
2006 |
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6th IASTED International Conference on Visualization, Imaging and Image Processing |
Abbreviated Journal |
VIIP |
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400–405 |
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ADAS, pedestrian detection, adaboost learning, pitch estimation, haar wavelets, edge orientation histograms. |
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In this paper we propose a combination of different Haar filter sets and Edge Orientation Histograms (EOH) in order to learn a model for pedestrian detection. As we will show, with the addition of EOH we obtain better ROCs than using Haar filters alone. Hence, a model consisting of discriminant features, selected by AdaBoost, is applied at pedestrian-sized image windows in order to perform
the classification. Additionally, taking into account the final application, a driver assistance system with realtime requirements, we propose a novel stereo-based camera pitch estimation to reduce the number of explored windows.
With this approach, the system can work in urban roads, as will be illustrated by current results. |
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Palma de Mallorca (Spain) |
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ADAS |
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ADAS @ adas @ GSL2006 |
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672 |
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Petia Radeva; Jordi Vitria; Fernando Vilariño; Panagiota Spyridonos; Fernando Azpiroz; Juan Malagelada; Fosca de Iorio; Anna Accarino |
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Title |
Cascade analysis for intestinal contraction detection |
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Patent |
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2009 |
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US 2009/0284589 A1 |
Abbreviated Journal |
USPO |
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1-25 |
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A method and system cascade analysisi for intestinal contraction detection is provided by extracting from image frames captured in-vivo. The method and system also relate to the detection of turbid liquids in intestinal tracts, to automatic detection of video image frames taken in the gastrointestinal tract including a field of view obstructed by turbid media, and more particulary, to extraction of image data obstructed by turbid media. |
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US Patent Office |
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US Patent Office |
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MILAB; OR; MV;SIAI |
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IAM @ iam @ RVV2009 |
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1700 |
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Author |
Simone Balocco; Carlo Gatta; Oriol Pujol; J. Mauri; Petia Radeva |
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Title |
SRBF: Speckle Reducing Bilateral Filtering |
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Journal Article |
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Year |
2010 |
Publication |
Ultrasound in Medicine and Biology |
Abbreviated Journal |
UMB |
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Volume |
36 |
Issue |
8 |
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1353-1363 |
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Abstract |
Speckle noise negatively affects medical ultrasound image shape interpretation and boundary detection. Speckle removal filters are widely used to selectively remove speckle noise without destroying important image features to enhance object boundaries. In this article, a fully automatic bilateral filter tailored to ultrasound images is proposed. The edge preservation property is obtained by embedding noise statistics in the filter framework. Consequently, the filter is able to tackle the multiplicative behavior modulating the smoothing strength with respect to local statistics. The in silico experiments clearly showed that the speckle reducing bilateral filter (SRBF) has superior performances to most of the state of the art filtering methods. The filter is tested on 50 in vivo US images and its influence on a segmentation task is quantified. The results using SRBF filtered data sets show a superior performance to using oriented anisotropic diffusion filtered images. This improvement is due to the adaptive support of SRBF and the embedded noise statistics, yielding a more homogeneous smoothing. SRBF results in a fully automatic, fast and flexible algorithm potentially suitable in wide ranges of speckle noise sizes, for different medical applications (IVUS, B-mode, 3-D matrix array US). |
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MILAB;HUPBA |
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no |
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BCNPCL @ bcnpcl @ BGP2010 |
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1314 |
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Author |
Marina Alberti; Simone Balocco; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Title |
Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation |
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Journal Article |
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2013 |
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Ultrasound in Medicine and Biology |
Abbreviated Journal |
UMB |
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39 |
Issue |
9 |
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1698-712 |
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Keywords |
Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy |
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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 |
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Admin @ si @ ABC2013 |
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2313 |
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G. Zahnd; Simone Balocco; A. Serusclat; P. Moulin; M. Orkisz; D. Vray |
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Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment Ultrasound in Medicine and Biology |
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2015 |
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Ultrasound in Medicine and Biology |
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UMB |
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41 |
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1 |
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339-345 |
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Arterial stiffness; Atherosclerosis; Common carotid artery; Longitudinal kinetics; Motion tracking; Ultrasound imaging |
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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 |
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Admin @ si @ ZBS2014 |
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2556 |
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Author |
David Vazquez; Antonio Lopez |
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Title |
Intrusion Classification in Intelligent Video Surveillance Systems |
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Report |
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2008 |
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Estudis d'Enginyeria Superior en Informática |
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UAB |
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Human detection; Car detection; Intrusion detection |
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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 |
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ADAS |
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no |
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ADAS @ adas @ VL2008a |
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1670 |
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Shifeng Zhang; Ajian Liu; Jun Wan; Yanyan Liang; Guogong Guo; Sergio Escalera; Hugo Jair Escalante; Stan Z. Li |
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CASIA-SURF: A Dataset and Benchmark for Large-scale Multi-modal Face Anti-spoofing |
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2020 |
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IEEE Transactions on Biometrics, Behavior, and Identity Science |
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TTBIS |
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2 |
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2 |
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182 - 193 |
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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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HuPBA; no proj |
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Admin @ si @ ZLW2020 |
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3412 |
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David Masip; Agata Lapedriza; Jordi Vitria |
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Boosted Online Learning for Face Recognition |
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2009 |
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IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
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2 |
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530–538 |
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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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1083–4419 |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2009 |
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1155 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
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Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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2009 |
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IEEE Transactions on Systems, Man and Cybernetics part B |
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TSMCB |
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39 |
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4 |
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935–944 |
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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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OR;MV |
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BCNPCL @ bcnpcl @ DoR2009a |
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1218 |
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