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Jordi Gonzalez, Thomas B. Moeslund, & Liang Wang. (2012). Semantic Understanding of Human Behaviors in Image Sequences: From video-surveillance to video-hermeneutics. CVIU - Computer Vision and Image Understanding, 116(3), 305–306.
Abstract: Purpose: Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries.Methods: First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound (IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations.Results: The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall.Conclusions: Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed.
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Antonio Hernandez, Nadezhda Zlateva, Alexander Marinov, Miguel Reyes, Petia Radeva, Dimo Dimov, et al. (2012). Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization. JAISE - Journal of Ambient Intelligence and Smart Environments, 4(6), 535–546.
Abstract: We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.
Keywords: Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation
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Sophie Wuerger, Kaida Xiao, Dimitris Mylonas, Q. Huang, Dimosthenis Karatzas, & Galina Paramei. (2012). Blue green color categorization in mandarin english speakers. JOSA A - Journal of the Optical Society of America A, 29(2), A102–A1207.
Abstract: Observers are faster to detect a target among a set of distracters if the targets and distracters come from different color categories. This cross-boundary advantage seems to be limited to the right visual field, which is consistent with the dominance of the left hemisphere for language processing [Gilbert et al., Proc. Natl. Acad. Sci. USA 103, 489 (2006)]. Here we study whether a similar visual field advantage is found in the color identification task in speakers of Mandarin, a language that uses a logographic system. Forty late Mandarin-English bilinguals performed a blue-green color categorization task, in a blocked design, in their first language (L1: Mandarin) or second language (L2: English). Eleven color singletons ranging from blue to green were presented for 160 ms, randomly in the left visual field (LVF) or right visual field (RVF). Color boundary and reaction times (RTs) at the color boundary were estimated in L1 and L2, for both visual fields. We found that the color boundary did not differ between the languages; RTs at the color boundary, however, were on average more than 100 ms shorter in the English compared to the Mandarin sessions, but only when the stimuli were presented in the RVF. The finding may be explained by the script nature of the two languages: Mandarin logographic characters are analyzed visuospatially in the right hemisphere, which conceivably facilitates identification of color presented to the LVF.
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Yunchao Gong, Svetlana Lazebnik, Albert Gordo, & Florent Perronnin. (2012). Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval. TPAMI - IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(12), 2916–2929.
Abstract: This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.
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Diego Cheda, Daniel Ponsa, & Antonio Lopez. (2012). Pedestrian Candidates Generation using Monocular Cues. In IEEE Intelligent Vehicles Symposium (pp. 7–12). IEEE Xplore.
Abstract: Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached.
Keywords: pedestrian detection
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Fernando Barrera, Felipe Lumbreras, & Angel Sappa. (2012). Evaluation of Similarity Functions in Multimodal Stereo. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 320–329). LNCS. Springer Berlin Heidelberg.
Abstract: This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
Keywords: Aveiro, Portugal
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Miguel Oliveira, Angel Sappa, & V. Santos. (2012). Color Correction using 3D Gaussian Mixture Models. In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 97–106). LNCS. Springer Berlin Heidelberg.
Abstract: The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
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Angel Sappa, David Geronimo, Fadi Dornaika, Mohammad Rouhani, & Antonio Lopez. (2012). Moving object detection from mobile platforms using stereo data registration. In Marek R. Ogiela, & Lakhmi C. Jain (Eds.), Computational Intelligence paradigms in advanced pattern classification (Vol. 386, pp. 25–37). Springer Berlin Heidelberg.
Abstract: This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios.
Keywords: pedestrian detection
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Pau Baiget, Carles Fernandez, Xavier Roca, & Jordi Gonzalez. (2012). Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance. In Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence (Vol. 384, pp. 87–95). Springer Berlin Heidelberg.
Abstract: The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally.
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Joost Van de Weijer, Robert Benavente, Maria Vanrell, Cordelia Schmid, Ramon Baldrich, Jacob Verbeek, et al. (2012). Color Naming. In Theo Gevers, Arjan Gijsenij, Joost Van de Weijer, & Jan-Mark Geusebroek (Eds.), Color in Computer Vision: Fundamentals and Applications (pp. 287–317). John Wiley & Sons, Ltd.
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Xavier Perez Sala, Laura Igual, Sergio Escalera, & Cecilio Angulo. (2012). Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models. In Vision Robotics: Technologies for Machine Learning and Vision Applications (pp. 23–42). IGI-Global.
Abstract: Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.
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Sergio Escalera, Josep Moya, Laura Igual, Veronica Violant, & Maria Teresa Anguera. (2012). Análisis Comportamental Automatizado de TDAH: la Influencia de la Variable Motivación. In IPSI – Cosmocaixa, Jornadas "Empremtes del present, efectes en la psicoanàlisi, la cultura i la societat.
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Laura Igual, Joan Carles Soliva, Antonio Hernandez, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). A Supervised Graph-cut Deformable Model for Brain MRI Segmentation. Deformation models: tracking, animation and applications. In Computational Vision and Biomechanics. LNCS. Springer Netherlands.
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Theo Gevers, Arjan Gijsenij, Joost Van de Weijer, & J.M. Geusebroek. (2012). Color in Computer Vision: Fundamentals and Applications. The Wiley-IS&T Series in Imaging Science and Technology.
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Mario Hernandez, Joao Sanchez, & Jordi Vitria. (2012). Selected papers from Iberian Conference on Pattern Recognition and Image Analysis (Vol. 45).
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