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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Detection of Complex Salient Regions. EURASIP Journal on Advances in Signal Processing, vol. 2008, article ID451389, 11 pages.
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Yu Jie, Jaume Amores, N. Sebe, Petia Radeva, & Tian Qi. (2008). Distance Learning for Similarity Estimation. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.30(3):451–462.
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O. Rodriguez-Leor, Carlo Gatta, E Fernandez-Nofrerias, Oriol Pujol, Neus Salvatella, C. Bosch, et al. (2008). Computationally Efficient Image-based IVUS Pullbacks Gating. European Heart Journal, ESC Supplement, Munich, 2008, p. 775.
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Sergio Escalera, Oriol Pujol, Petia Radeva, Jordi Vitria, & Maria Teresa Anguera. (2010). Automatic Detection of Dominance and Expected Interest. EURASIPJ - EURASIP Journal on Advances in Signal Processing, , 12.
Abstract: Article ID 491819
Social Signal Processing is an emergent area of research that focuses on the analysis of social constructs. Dominance and interest are two of these social constructs. Dominance refers to the level of influence a person has in a conversation. Interest, when referred in terms of group interactions, can be defined as the degree of engagement that the members of a group collectively display during their interaction. In this paper, we argue that only using behavioral motion information, we are able to predict the interest of observers when looking at face-to-face interactions as well as the dominant people. First, we propose a simple set of movement-based features from body, face, and mouth activity in order to define a higher set of interaction indicators. The considered indicators are manually annotated by observers. Based on the opinions obtained, we define an automatic binary dominance detection problem and a multiclass interest quantification problem. Error-Correcting Output Codes framework is used to learn to rank the perceived observer's interest in face-to-face interactions meanwhile Adaboost is used to solve the dominant detection problem. The automatic system shows good correlation between the automatic categorization results and the manual ranking made by the observers in both dominance and interest detection problems.
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Ole Larsen, Petia Radeva, & Enric Marti. (1995). Bounds on the optimal elasticity parameters for a snake. Image Analysis and Processing, , 37–42.
Abstract: This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds.
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