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Laura Igual, Santiago Segui, Jordi Vitria, Fernando Azpiroz, & Petia Radeva. (2007). Eigenmotion-Based Detection of Intestinal Contractions. In Computer Analysis of Images and Patterns, 12th International Conference (Vol. 4673, 293–300). LNCS.
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Cesar Isaza, Joaquin Salas, & Bogdan Raducanu. (2010). Toward the Detection of Urban Infrastructures Edge Shadows. In eds. Blanc–Talon et al (Ed.), 12th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 6474, 30–37). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising.
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Antonio Hernandez, Miguel Reyes, Sergio Escalera, & Petia Radeva. (2010). Spatio-Temporal GrabCut human segmentation for face and pose recovery. In IEEE International Workshop on Analysis and Modeling of Faces and Gestures (33–40).
Abstract: In this paper, we present a full-automatic Spatio-Temporal GrabCut human segmentation methodology. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model for seed initialization. Spatial information is included by means of Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, human segmentation is combined with Shape and Active Appearance Models to perform full face and pose recovery. Results over public data sets as well as proper human action base show a robust segmentation and recovery of both face and pose using the presented methodology.
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Antonio Hernandez, Carlo Gatta, Petia Radeva, Laura Igual, R. Letaz, & Sergio Escalera. (2010). Automatic Vessel Segmentation For Angiography and CT Registration. In Medical Image Computing in Catalunya: Graduate Student Workshop (1–2).
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David Guillamet, Jordi Vitria, & B. Shiele. (2003). Introducing a weighted non-negative matrix factorization for image classification. PRL - Pattern Recognition Letters, 24(14), 2447–2454.
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C. Gratin, Jordi Vitria, F. Moreso, & D. Seron. (1994). Texture Classification using Neural Networks and Local Granulometries. In EURASIP Workshop, Mathematical Morphology and Its Applications to image Processing, J.Serra and P.Soille, editors (pp. 309–316).
Keywords: Neural Networks; Granulometry; Kidney; Texture; Classication
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David Guillamet, & Jordi Vitria. (2002). Determining a Suitable Metric when using Non-negative Matrix Factorization..
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David Guillamet, & Jordi Vitria. (2001). Unsupervised Learning of Part-Based Representations.
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David Guillamet, & Jordi Vitria. (2001). Non-negative Matrix Factorization to Extract Part-Based Representations..
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David Guillamet, & Jordi Vitria. (2003). Evaluation of distance metrics for recognition based on non-negative matrix factorization. PRL - Pattern Recognition Letters, 24(9-10), 1599 –1605.
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David Guillamet, & Jordi Vitria. (2003). An Experimental Evaluation of K-nn for Linear Transforms of Positive Data. In In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science. 2652:317–325.
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David Guillamet, & Jordi Vitria. (2002). Classifying Faces with Non-negative Matrix Factorization..
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David Guillamet, & Jordi Vitria. (2002). Non-negative Matrix Factorization for Face Recognition..
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David Guillamet, & Jordi Vitria. (2001). Discriminant Basis for Object Classification..
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David Guillamet, & Jordi Vitria. (2001). Unsupervised Learning of Structural Object Representations.
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