By Sebastiano Battiato

This ebook constitutes the completely refereed court cases of the sixteenth overseas convention on complex ideas 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 thoughts for development adaptive, clever, secure and safe imaging structures. the point of interest of the convention is on following subject: low-level photograph processing, video processing and digital camera networks, movement and monitoring, protection, forensics and biometrics, intensity and 3D, snapshot caliber development and review, type and popularity, multidimensional sign processing, multimedia compression, retrieval, and navigation.

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

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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]. The proposed edge width estimation method works well on images with a relatively low amount of noise, which is usually the case with defocused images.

Fig. 1. Edge model In our model we use the following definitions of the Gaussian filter and ideal step edge function Gσ (x) = √ x2 1 e− 2σ2 , 2πσ H(x) = 1, 0, x ≥ 0, x < 0. 2 σ x) σ ∀σ > 0, σ > 0. (1) Estimation of Edge Width For the estimation of edge width we use the unsharp masking approach. Let Uσ,α [Eσ0 ](x) be the result of unsharp masking applied to the edge Eσ0 (x): U σ,α [Eσ0 ](x) = (1 + α)Eσ0 (x) − αEσ0 ∗ Gσ = = (1 + α)Eσ0 (x) − αE√σ2 +σ2 (x). 0 (2) Using (1) and supposing σ = σ0 = σ1 , (2) holds U σ1 ,α [Eσ1 ](x) = (1 + α)Eσ1 (x) − αE√2σ1 (x) = √ 2σ2 σ2 √ = (1 + α)Eσ1 (x) − αE 2σ2 ( √ x) = Uσ2 ,α [Eσ2 ]( x).

An illustration of DHSF is presented in Fig. 3(a) and Fig. 2(c). 2 Estimating the Direction of Second Order Statistics Such as Ridges and Valleys By convolving the image patch with the DHSF (by the technique of rotating the images, as explained above), for each pixel in the image patch we obtain a pixel signal which captures a directional second order information around the pixel. The idea is to extract the directions at which these second order statistics such as ridges and valleys occur and to construct a descriptor from this information.

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