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Wenjuan Gong, Xuena Zhang, Jordi Gonzalez, Andrews Sobral, Thierry Bouwmans, Changhe Tu, et al. (2016). Human Pose Estimation from Monocular Images: A Comprehensive Survey. SENS - Sensors, 16(12), 1966.
Abstract: Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
Keywords: human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods
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Maria Oliver, Gloria Haro, Mariella Dimiccoli, Baptiste Mazin, & Coloma Ballester. (2016). A computational model of amodal completion. In SIAM Conference on Imaging Science.
Abstract: This paper presents a computational model to recover the most likely interpretation of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth. Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2016). Sparse representation over learned dictionary for symbol recognition. SP - Signal Processing, 125, 36–47.
Abstract: In this paper we propose an original sparse vector model for symbol retrieval task. More speci cally, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols.
Keywords: Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points
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Joana Maria Pujadas-Mora, Alicia Fornes, Josep Llados, & Anna Cabre. (2016). Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources. In K.Matthijs, S.Hin, H.Matsuo, & J.Kok (Eds.), The future of historical demography. Upside down and inside out (pp. 127–131). Acco Publishers.
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Mariella Dimiccoli. (2016). Figure-ground segregation: A fully nonlocal approach. VR - Vision Research, 126, 308–317.
Abstract: We present a computational model that computes and integrates in a nonlocal fashion several configural cues for automatic figure-ground segregation. Our working hypothesis is that the figural status of each pixel is a nonlocal function of several geometric shape properties and it can be estimated without explicitly relying on object boundaries. The methodology is grounded on two elements: multi-directional linear voting and nonlinear diffusion. A first estimation of the figural status of each pixel is obtained as a result of a voting process, in which several differently oriented line-shaped neighborhoods vote to express their belief about the figural status of the pixel. A nonlinear diffusion process is then applied to enforce the coherence of figural status estimates among perceptually homogeneous regions. Computer simulations fit human perception and match the experimental evidence that several cues cooperate in defining figure-ground segregation. The results of this work suggest that figure-ground segregation involves feedback from cells with larger receptive fields in higher visual cortical areas.
Keywords: Figure-ground segregation; Nonlocal approach; Directional linear voting; Nonlinear diffusion
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Gloria Fernandez Esparrach, Jorge Bernal, Cristina Rodriguez de Miguel, Debora Gil, Fernando Vilariño, Henry Cordova, et al. (2016). Utilidad de la visión por computador para la localización de pólipos pequeños y planos. In XIX Reunión Nacional de la Asociación Española de Gastroenterología, Gastroenterology Hepatology (Vol. 39, 94).
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