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Maria Vanrell, & Jordi Vitria. (1997). Optimal 3x3 decomposable disks for morphological transformations. Image and Vision Computing, 15(2): 845–854.
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Ignasi Rius, Dani Rowe, Jordi Gonzalez, & Xavier Roca. (2005). A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 529–536.
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Dani Rowe, Ignasi Rius, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2005). Probabilistic Image-Based Tracking: Improving Particle Filtering. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522: 85–92.
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Agata Lapedriza, David Masip, & Jordi Vitria. (2005). The contribution of external features to face recognition. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 537–544.
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Agata Lapedriza, David Masip, & Jordi Vitria. (2005). Are external face features useful for automatic face classification?.
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Antonio Lopez, Joan Serrat, J. Saludes, Cristina Cañero, Felipe Lumbreras, & T. Graf. (2005). Ridgeness for Detecting Lane Markings.
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Juan Andrade, T. Alejandra Vidal, & A. Sanfeliu. (2005). Multirobot C-SLAM: Simultaneous localization, control, and mapping.
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Xavier Baro. (2005). Fast traffic sign detection on gray-scale images.
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Agata Lapedriza. (2005). Face Classification using External Face Features.
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Muhammad Anwer Rao, David Vazquez, & Antonio Lopez. (2011). Opponent Colors for Human Detection. In J. Vitria, J.M. Sanches, & M. Hernandez (Eds.), 5th Iberian Conference on Pattern Recognition and Image Analysis (Vol. 6669, pp. 363–370). LNCS. Berlin Heidelberg: Springer.
Abstract: Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.
Keywords: Pedestrian Detection; Color; Part Based Models
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Jaume Amores, N. Sebe, & Petia Radeva. (2005). Efficient Object-Class Recognition by Boosting Contextual Information.
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Francesc Tous, Maria Vanrell, & Ramon Baldrich. (2005). Relaxed Grey-World: Computational Colour Constancy by Surface Matching. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522:192–199.
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Agnes Borras, & Josep Llados. (2005). Object Image Retrieval by Shape Content in Complex Scenes Using Geometric Constraints. In Pattern Recognition And Image Analysis (Vol. 3522, 325–332). Springer Link.
Abstract: This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are repre- sented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method al- lows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling.
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Oriol Pujol, & Petia Radeva. (2005). Solving Particularization with Supervised Clustering Competition Scheme. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 11–18.
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Joan Mas, Gemma Sanchez, & Josep Llados. (2005). An Adjacency Grammar to Recognize Symbols and Gestures in a Digital Pen Framework. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 115–122.
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