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Alejandro Gonzalez Alzate; Sebastian Ramos; David Vazquez; Antonio Lopez; Jaume Amores |
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Title |
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection |
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Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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3-12 |
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SSL; Pedestrian Detection |
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Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera. |
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Santiago de Compostela; España; June 2015 |
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ADAS; 600.057; 600.054; 600.076 |
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GRV2015; ADAS @ adas @ GRV2015 |
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2454 |
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Monica Piñol; Angel Sappa; Ricardo Toledo |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy |
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Journal Article |
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2015 |
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Neurocomputing |
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NEUCOM |
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150 |
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A |
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106–115 |
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Reinforcement learning; Q-learning; Bag of features; Descriptors |
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This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach. |
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Admin @ si @ PST2015 |
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2473 |
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Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro |
![goto web page (via DOI) doi](img/doi.gif)
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Non-Verbal Communication Analysis in Victim-Offender Mediations |
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Journal Article |
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2015 |
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Pattern Recognition Letters |
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PRL |
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67 |
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1 |
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19-27 |
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Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning |
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We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals. |
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HuPBA;MV |
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Admin @ si @ PEP2015 |
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2583 |
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Frederic Sampedro; Sergio Escalera; Anna Domenech; Ignasi Carrio |
![goto web page (via DOI) doi](img/doi.gif)
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Automatic Tumor Volume Segmentation in Whole-Body PET/CT Scans: A Supervised Learning Approach Source |
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Journal Article |
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2015 |
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Journal of Medical Imaging and Health Informatics |
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JMIHI |
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5 |
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2 |
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192-201 |
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CONTEXTUAL CLASSIFICATION; PET/CT; SUPERVISED LEARNING; TUMOR SEGMENTATION; WHOLE BODY |
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Whole-body 3D PET/CT tumoral volume segmentation provides relevant diagnostic and prognostic information in clinical oncology and nuclear medicine. Carrying out this procedure manually by a medical expert is time consuming and suffers from inter- and intra-observer variabilities. In this paper, a completely automatic approach to this task is presented. First, the problem is stated and described both in clinical and technological terms. Then, a novel supervised learning segmentation framework is introduced. The segmentation by learning approach is defined within a Cascade of Adaboost classifiers and a 3D contextual proposal of Multiscale Stacked Sequential Learning. Segmentation accuracy results on 200 Breast Cancer whole body PET/CT volumes show mean 49% sensitivity, 99.993% specificity and 39% Jaccard overlap Index, which represent good performance results both at the clinical and technological level. |
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HuPBA;MILAB |
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Admin @ si @ SED2015 |
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2584 |
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German Ros; Sebastian Ramos; Manuel Granados; Amir Bakhtiary; David Vazquez; Antonio Lopez |
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Title |
Vision-based Offline-Online Perception Paradigm for Autonomous Driving |
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2015 |
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IEEE Winter Conference on Applications of Computer Vision |
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231 - 238 |
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Autonomous Driving; Scene Understanding; SLAM; Semantic Segmentation |
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Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community. |
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Hawaii; January 2015 |
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ADAS; 600.076 |
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ADAS @ adas @ RRG2015 |
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2499 |
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Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi |
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Title |
Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars |
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Journal Article |
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2015 |
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Neurocomputing |
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NEUCOM |
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150 |
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A |
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147-154 |
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document image analysis; stochastic context-free grammars; text classication features |
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In this paper we dene a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents.
Two sets of text classication features are used to perform an initial classication of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models
and the results showed that the proposed grammatical model outperformed
the other methods. Furthermore, grammars also provide the document structure
along with its segmentation. |
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DAG; 601.158; 600.077; 600.061 |
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Admin @ si @ ACS2015 |
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2531 |
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Miguel Oliveira; Victor Santos; Angel Sappa |
![goto web page (via DOI) doi](img/doi.gif)
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Multimodal Inverse Perspective Mapping |
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Journal Article |
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2015 |
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Information Fusion |
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IF |
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24 |
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108–121 |
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Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles |
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Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints. |
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ADAS; 600.055; 600.076 |
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Admin @ si @ OSS2015c |
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2532 |
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Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Efficient segmentation-free keyword spotting in historical document collections |
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Journal Article |
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2015 |
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Pattern Recognition |
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PR |
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48 |
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2 |
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545–555 |
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Historical documents; Keyword spotting; Segmentation-free; Dense SIFT features; Latent semantic analysis; Product quantization |
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In this paper we present an efficient segmentation-free word spotting method, applied in the context of historical document collections, that follows the query-by-example paradigm. We use a patch-based framework where local patches are described by a bag-of-visual-words model powered by SIFT descriptors. By projecting the patch descriptors to a topic space with the latent semantic analysis technique and compressing the descriptors with the product quantization method, we are able to efficiently index the document information both in terms of memory and time. The proposed method is evaluated using four different collections of historical documents achieving good performances on both handwritten and typewritten scenarios. The yielded performances outperform the recent state-of-the-art keyword spotting approaches. |
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DAG; ADAS; 600.076; 600.077; 600.061; 601.223; 602.006; 600.055 |
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Admin @ si @ RAT2015a |
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2544 |
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Daniel Sanchez; Miguel Angel Bautista; Sergio Escalera |
![goto web page (via DOI) doi](img/doi.gif)
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HuPBA 8k+: Dataset and ECOC-GraphCut based Segmentation of Human Limbs |
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Journal Article |
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2015 |
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Neurocomputing |
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NEUCOM |
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150 |
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173–188 |
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Human limb segmentation; ECOC; Graph-Cuts |
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Human multi-limb segmentation in RGB images has attracted a lot of interest in the research community because of the huge amount of possible applications in fields like Human-Computer Interaction, Surveillance, eHealth, or Gaming. Nevertheless, human multi-limb segmentation is a very hard task because of the changes in appearance produced by different points of view, clothing, lighting conditions, occlusions, and number of articulations of the human body. Furthermore, this huge pose variability makes the availability of large annotated datasets difficult. In this paper, we introduce the HuPBA8k+ dataset. The dataset contains more than 8000 labeled frames at pixel precision, including more than 120000 manually labeled samples of 14 different limbs. For completeness, the dataset is also labeled at frame-level with action annotations drawn from an 11 action dictionary which includes both single person actions and person-person interactive actions. Furthermore, we also propose a two-stage approach for the segmentation of human limbs. In a first stage, human limbs are trained using cascades of classifiers to be split in a tree-structure way, which is included in an Error-Correcting Output Codes (ECOC) framework to define a body-like probability map. This map is used to obtain a binary mask of the subject by means of GMM color modelling and GraphCuts theory. In a second stage, we embed a similar tree-structure in an ECOC framework to build a more accurate set of limb-like probability maps within the segmented user mask, that are fed to a multi-label GraphCut procedure to obtain final multi-limb segmentation. The methodology is tested on the novel HuPBA8k+ dataset, showing performance improvements in comparison to state-of-the-art approaches. In addition, a baseline of standard action recognition methods for the 11 actions categories of the novel dataset is also provided. |
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Admin @ si @ SBE2015 |
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G. Zahnd; Simone Balocco; A. Serusclat; P. Moulin; M. Orkisz; D. Vray |
![goto web page (via DOI) doi](img/doi.gif)
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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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Admin @ si @ ZBS2014 |
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Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection |
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2015 |
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IEEE Intelligent Vehicles Symposium IV2015 |
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356-361 |
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Pedestrian Detection |
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Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. |
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Seoul; Corea; June 2015 |
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IV |
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ADAS @ adas @ GVX2015 |
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Author |
Antonio Hernandez |
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Title |
From pixels to gestures: learning visual representations for human analysis in color and depth data sequences |
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2015 |
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PhD Thesis, Universitat de Barcelona-CVC |
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The visual analysis of humans from images is an important topic of interest due to its relevance to many computer vision applications like pedestrian detection, monitoring and surveillance, human-computer interaction, e-health or content-based image retrieval, among others.
In this dissertation we are interested in learning different visual representations of the human body that are helpful for the visual analysis of humans in images and video sequences. To that end, we analyze both RGB and depth image modalities and address the problem from three different research lines, at different levels of abstraction; from pixels to gestures: human segmentation, human pose estimation and gesture recognition.
First, we show how binary segmentation (object vs. background) of the human body in image sequences is helpful to remove all the background clutter present in the scene. The presented method, based on Graph cuts optimization, enforces spatio-temporal consistency of the produced segmentation masks among consecutive frames. Secondly, we present a framework for multi-label segmentation for obtaining much more detailed segmentation masks: instead of just obtaining a binary representation separating the human body from the background, finer segmentation masks can be obtained separating the different body parts.
At a higher level of abstraction, we aim for a simpler yet descriptive representation of the human body. Human pose estimation methods usually rely on skeletal models of the human body, formed by segments (or rectangles) that represent the body limbs, appropriately connected following the kinematic constraints of the human body. In practice, such skeletal models must fulfill some constraints in order to allow for efficient inference, while actually limiting the expressiveness of the model. In order to cope with this, we introduce a top-down approach for predicting the position of the body parts in the model, using a mid-level part representation based on Poselets.
Finally, we propose a framework for gesture recognition based on the bag of visual words framework. We leverage the benefits of RGB and depth image modalities by combining modality-specific visual vocabularies in a late fusion fashion. A new rotation-variant depth descriptor is presented, yielding better results than other state-of-the-art descriptors. Moreover, spatio-temporal pyramids are used to encode rough spatial and temporal structure. In addition, we present a probabilistic reformulation of Dynamic Time Warping for gesture segmentation in video sequences. A Gaussian-based probabilistic model of a gesture is learnt, implicitly encoding possible deformations in both spatial and time domains. |
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January 2015 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Sergio Escalera;Stan Sclaroff |
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978-84-940902-0-2 |
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HuPBA;MILAB |
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Admin @ si @ Her2015 |
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2576 |
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Author |
Hongxing Gao |
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Title |
Focused Structural Document Image Retrieval in Digital Mailroom Applications |
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2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In this work, we develop a generic framework that is able to handle the document retrieval problem in various scenarios such as searching for full page matches or retrieving the counterparts for specific document areas, focusing on their structural similarity or letting their visual resemblance to play a dominant role. Based on the spatial indexing technique, we propose to search for matches of local key-region pairs carrying both structural and visual information from the collection while a scheme allowing to adjust the relative contribution of structural and visual similarity is presented.
Based on the fact that the structure of documents is tightly linked with the distance among their elements, we firstly introduce an efficient detector named Distance Transform based Maximally Stable Extremal Regions (DTMSER). We illustrate that this detector is able to efficiently extract the structure of a document image as a dendrogram (hierarchical tree) of multi-scale key-regions that roughly correspond to letters, words and paragraphs. We demonstrate that, without benefiting from the structure information, the key-regions extracted by the DTMSER algorithm achieve better results comparing with state-of-the-art methods while much less amount of key-regions are employed.
We subsequently propose a pair-wise Bag of Words (BoW) framework to efficiently embed the explicit structure extracted by the DTMSER algorithm. We represent each document as a list of key-region pairs that correspond to the edges in the dendrogram where inclusion relationship is encoded. By employing those structural key-region pairs as the pooling elements for generating the histogram of features, the proposed method is able to encode the explicit inclusion relations into a BoW representation. The experimental results illustrate that the pair-wise BoW, powered by the embedded structural information, achieves remarkable improvement over the conventional BoW and spatial pyramidal BoW methods.
To handle various retrieval scenarios in one framework, we propose to directly query a series of key-region pairs, carrying both structure and visual information, from the collection. We introduce the spatial indexing techniques to the document retrieval community to speed up the structural relationship computation for key-region pairs. We firstly test the proposed framework in a full page retrieval scenario where structurally similar matches are expected. In this case, the pair-wise querying method achieves notable improvement over the BoW and spatial pyramidal BoW frameworks. Furthermore, we illustrate that the proposed method is also able to handle focused retrieval situations where the queries are defined as a specific interesting partial areas of the images. We examine our method on two types of focused queries: structure-focused and exact queries. The experimental results show that, the proposed generic framework obtains nearly perfect precision on both types of focused queries while it is the first framework able to tackle structure-focused queries, setting a new state of the art in the field.
Besides, we introduce a line verification method to check the spatial consistency among the matched key-region pairs. We propose a computationally efficient version of line verification through a two step implementation. We first compute tentative localizations of the query and subsequently employ them to divide the matched key-region pairs into several groups, then line verification is performed within each group while more precise bounding boxes are computed. We demonstrate that, comparing with the standard approach (based on RANSAC), the line verification proposed generally achieves much higher recall with slight loss on precision on specific queries. |
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January 2015 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados;Dimosthenis Karatzas;Marçal Rusiñol |
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978-84-943427-0-7 |
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DAG; 600.077 |
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Admin @ si @ Gao2015 |
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2577 |
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Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; German Ros; David Vazquez; Antonio Lopez |
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Title |
3D-Guided Multiscale Sliding Window for Pedestrian Detection |
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Conference Article |
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2015 |
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Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
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9117 |
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560-568 |
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Pedestrian Detection |
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The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy. |
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Santiago de Compostela; España; June 2015 |
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IbPRIA |
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ADAS; 600.076; 600.057; 600.054 |
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ADAS @ adas @ GVR2015 |
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2585 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
An Overview of Color Name Applications in Computer Vision |
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2015 |
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Computational Color Imaging Workshop |
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color features; color names; object recognition |
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In this article we provide an overview of color name applications in computer vision. Color names are linguistic labels which humans use to communicate color. Computational color naming learns a mapping from pixels values to color names. In recent years color names have been applied to a wide variety of computer vision applications, including image classification, object recognition, texture classification, visual tracking and action recognition. Here we provide an overview of these results which show that in general color names outperform photometric invariants as a color representation. |
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Saint Etienne; France; March 2015 |
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CCIW |
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LAMP; 600.079; 600.068 |
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Call Number |
Admin @ si @ WeK2015 |
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2586 |
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