By Sebastiano Battiato
This publication constitutes the completely refereed complaints of the sixteenth overseas convention on complex techniques for clever imaginative and prescient structures, ACIVS 2015, held Catania, Italy, in October 2015. The seventy six revised complete papers have been rigorously chosen from 129 submissions.
Read 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
In our method, as in , we directly apply two half Gaussian ﬁlters with two diﬀerent λ and the same μ to obtain the directional derivatives. An example for the two discretized ﬁlters is shown in Fig. 2(d). Later, we compute the diﬀerence of the response of these two ﬁlters to obtain the desired smoothed second order derivative information in the ridge/valley directions (illustrated in Fig. 3(b)). We refer to this half Gaussian ﬁlter combination as the difference of half smoothing ﬁlters (DHSF).
A) Smoothing filter (b) (c) Rotating filters DHSF. (d) Discretized DHSF. Fig. 2. A smoothing rotating ﬁlter and Diﬀerence of Half smoothing Filters (DHSF). For (d) top: μ = 10 and λ = 1. 5. 2 Directional Filter In our method, we use a directional ﬁlter made of anisotropic smoothing half Gaussian kernels. For every pixel in the image patch, we spin this directional ﬁlter to obtain a Rotating Signal Descriptor (RSD), which is a function of a rotation angle θ and the underlying signal.
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. This experiment shows that our blur estimation method can rank and select images with best OCR accuracy. In addition, the measured Pearson’s correlation scores for 25 sets show that our method outperforms unsupervised and supervised approaches. Next step is to use the present computation in order to improve the quality of the document and in particular the OCR accuracy.
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