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

By Sebastiano Battiato

This e-book constitutes the completely refereed complaints of the sixteenth foreign convention on complicated 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 targeting innovations for development adaptive, clever, secure and safe imaging platforms. the focal point of the convention is on following subject: low-level picture processing, video processing and digicam networks, movement and monitoring, protection, forensics and biometrics, intensity and 3D, snapshot caliber development and evaluation, type and popularity, multidimensional sign processing, multimedia compression, retrieval, and navigation.

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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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LIDRIC has a dimension of 768. Oliva et al. [14] employ Gabor filters to the greyscale input image at four different angles and at four spatial scales to obtain the GIST descriptor. The descriptor has a dimension of 512 and is more global. Authors of [12] propose a new descriptor called RSD-HoG. It is based on the RSD-DOG 25 orientation of the edges. The edge orientations are extracted by using a rotating half Gaussian kernel. The descriptor is constructed in a novel way, by embedding the response of the rotating half Gaussian kernel in a Histogram of oriented Gradient (HoG) framework.

In: CIFED 2014, Nancy, France, pp. 109–124, September 2014 16. : Edge-based blur kernel estimation using patch priors. In: 2013 IEEE International Conference on Computational Photography (ICCP), Cambridge, MA, pp. 1–8, April 2013 17. : Blind restoration of motion blurred barcode images using ridgelet transform and radial basis function neural network, pp. 63–80, April 2014 18. : Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, Bombay, Inde, pp.

Fig. 2. Edge profile examples and the result of the proposed edge width estimation method. The blue thin line is the original profile, the red thick line is an isolated edge, the green puncture line is a model edge with found edge width. 2 Full Defocus Map Using sparse defocus map at edge locations, the full defocus map is recovered by an edge-aware interpolation method [8]. We use the MatLab software provided by Zhuo and Sim [16] and substitute their sparse defocus map with ours. Edge Width Estimation for Defocus Map from a Single Image 4 19 Results and Discussion We demonstrate the effectiveness of the proposed method by comparing our full defocus maps with results provided by [14] and [16].

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