Read e-book online Image Analysis and Recognition: 10th International PDF

By Guoying Zhao, Matti Pietikäinen (auth.), Mohamed Kamel, Aurélio Campilho (eds.)

ISBN-10: 3642390935

ISBN-13: 9783642390937

ISBN-10: 3642390943

ISBN-13: 9783642390944

This publication constitutes the completely refereed court cases of the tenth overseas convention on snapshot research and popularity, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The ninety two revised complete papers provided have been rigorously reviewed and chosen from 177 submissions. The papers are equipped in topical sections on biometrics: behavioral; biometrics: physiological; type and regression; item popularity; photograph processing and research: representations and types, compression, enhancement , function detection and segmentation; 3D snapshot research; monitoring; scientific imaging: photo segmentation, picture registration, photo research, coronary snapshot research, retinal picture research, computing device aided analysis, mind snapshot research; cellphone snapshot research; RGB-D digicam functions; tools of moments; applications.

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Read or Download Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings PDF

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19–26, 2013. c Springer-Verlag Berlin Heidelberg 2013 20 P. Moreno and J. e. a single feature dimension along frames) for each dimension of the data samples. Although the TemporalBoost algorithm improves the classification performance, it is ignoring the information contained in the spatio-temporal patterns. In this work we present the advantages of the FuzzyBoost algorithm, which finds both the temporal and spatio-temporal patterns that improve the classification performance. The remaining components of this work are the same as [2], namely: (i) the framebased waving pattern extraction, which utilizes the Focus Of Attention (FOA) features; (ii) the optic flow computation [9] and (iii) the segmentation and labeling of moving targets in the image, which utilizes the LOTS method [10] for segmentation and hungarian assigment [11] for labeling.

Do for each cell cj j = 1 . . nC do Fjt (d) = δ[dc = cj ∧ dt ∈ wt = {1, . . , t}]; end end Alg. 1 shows the feature set selection of TemporalBoost, a heuristic that builds temporal threads in the spatio-temporal feature volume and was used previously on the same problem [2]. In this work we address the search for sets in the full spatio-temporal volume, guiding the search and reducing the number of possible candidates through dimensionality reduction algorithms. Dimensionality reduction algorithms, as explained below, provide a projection matrix that we explore in order to find feature set candidates.

9065 34 A. Fernandez et al. 95%. Finally, considering the domain and according to the experts opinion, isolated movements of one only frame are discarded, because a movement without continuity does not represent a significant movement, and even the expert is not able to detect it. 65%. 5 Conclusions In this paper a new approach to analyze facial expression changes is presented in order to support the audiologists when they are testing the hearing of patients with cognitive decline or other disabilities.

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Image Analysis and Recognition: 10th International Conference, ICIAR 2013, Póvoa do Varzim, Portugal, June 26-28, 2013. Proceedings by Guoying Zhao, Matti Pietikäinen (auth.), Mohamed Kamel, Aurélio Campilho (eds.)


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