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

By Sebastiano Battiato

This ebook constitutes the completely refereed court cases of the sixteenth foreign convention on complex thoughts 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 concentrating on options for construction adaptive, clever, secure and safe imaging platforms. the point of interest of the convention is on following subject: low-level snapshot processing, video processing and digicam networks, movement and monitoring, protection, forensics and biometrics, intensity and 3D, snapshot caliber development and overview, type and popularity, multidimensional sign processing, multimedia compression, retrieval, and navigation.

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

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3 it can be seen that the proposed method makes the background more homogenous than [16]. In Fig. 4 the proposed method correctly processes the grass area and results in more gradual depth change. In Fig. 5, the proposed method and methods [14,16] show results with almost the same quality. a. Input image b. Edge map for the proposed algorithm b. Edge width estimation by Zhuo and Sim [16] c. Proposed edge width estimation Fig. 3. Full defocus map for bird image. Blue and purple areas are sharp regions, yellow are white areas are blurry regions.

This method considers each text patch of document image to extract a working zone in order to limit computation time. The pixels in the zone are classified into two classes based on fuzzy cmeans approach. The fuzziness rate of each pixel in the two classes is then employed to measure the blur level. The visual results show that the estimated blur is fitted with human blur perception. The quantitative evaluation is made comparing bl index with OCR accuracy. We calculated SROCC for 25 sets of the database DIQA with the estimated blur.

X,y In order to improve the performance of the proposed method we merge the results of the joint sparse coding deringing and the results of the total variation minimization deringing. Denote the image obtained by joint sparse coding deringing as Isc (x, y), the image obtained by total variation minimization as Itv (x, y) and the distance from the nearest edge as ρ(x, y) (see below). Then the final image is constructed as follows: I(x, y) = I tv (x, y), if ρ(x, y) ≤ 3; I sc (x, y), else. Thus the values on the edges are taken from the total variation minimization deringing method, and other values are taken from the joint sparse coding deringing method.

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