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Advanced Concepts for Intelligent Vision Systems: 16th by Sebastiano Battiato PDF

By Sebastiano Battiato

ISBN-10: 3319259024

ISBN-13: 9783319259024

ISBN-10: 3319259032

ISBN-13: 9783319259031

This e-book constitutes the completely refereed court cases of the sixteenth overseas convention on complex options for clever imaginative and prescient platforms, ACIVS 2015, held Catania, Italy, in October 2015. The seventy six revised complete papers have been conscientiously chosen from 129 submissions.

Acivs 2015 is a convention targeting ideas for construction adaptive, clever, secure and safe imaging platforms. the point of interest of the convention is on following subject: low-level photo processing, video processing and digital camera networks, movement and monitoring, defense, forensics and biometrics, intensity and 3D, snapshot caliber development and evaluate, type and popularity, multidimensional sign processing, multimedia compression, retrieval, and navigation.

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Read Online or Download Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings PDF

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Extra info for Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings

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15–22, 2015. 1007/978-3-319-25903-1 2 16 A. Nasonov et al. General purpose blur estimation methods can also be used for sparse defocus map construction problem. The method [6] is based on the assumption that the blur of the image is close to Gaussian. The image is divided into blocks, and the blur kernel is supposed to be uniform inside the block. The estimation of the blurriness of the block is based on the maximum of difference ratio between an original image and its two re-blurred versions. Block-based approach provides good blur estimation for highly textured areas but it shows inadequate results for blocks not containing edges, for example, flat areas.

These sharp peaks correspond to the two directions of the curve (an entering and leaving path). In case of a junction, the number of peaks corresponds to the number of crest lines (ridges/valleys) in the junction (point 4 in Fig. 4). We obtain the same information for the bent lines (illustrated in point 2 on Fig. 4). Finally, due to the strong smoothing (parameter μ), D is close to 0 in the presence of noise without any crest line nor edge (illustrated in point 10 in Fig. 4). This illustrates the robustness of this method in the presence of noise.

6, λ1 = 2 and λ2 = 2 2 to obtain a 256 length√descriptor. In the second step, we smooth the image patch with μ = 6, λ1 = 2 2 and λ2 = 4 and obtain another 256 length descriptor. Lastly, we concatenate the two parts to form a 512 size RSD-DOG(3-SCALES) descriptor. The performance of these two variants of RSD-DOG is compared with the performance of SIFT, GLOH, DAISY, GIST and LIDRIC descriptors. For SIFT and GLOH, the descriptors are extracted from the binaries provided by Oxford group. For DAISY descriptor, the patches are extracted from the code provided by 3 .

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Advanced Concepts for Intelligent Vision Systems: 16th International Conference, ACIVS 2015, Catania, Italy, October 26-29, 2015. Proceedings by Sebastiano Battiato

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