By Chul Keun Kim, Doug Young Suh, Gwang Hoon Park (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This publication constitutes the refereed court cases of the eighth foreign convention on complicated innovations for clever imaginative and prescient platforms, ACIVS 2006, held in Antwerp, Belgium in September 2006.
The forty five revised complete papers and sixty five revised poster papers offered have been conscientiously reviewed and chosen from round 242 submissions. The papers are geared up in topical sections on noise relief and recovery, segmentation, movement estimation and monitoring, video processing and coding, digital camera calibration, photo registration and stereo matching, biometrics and defense, clinical imaging, picture retrieval and picture figuring out, in addition to category and recognition.
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006. Proceedings
Cleaning is a process that includes enhancement and elimination of spurious details (dust particles, for instance). In this paper we address this last point. Practical application of mathematical models for inpainting deserve by themselves their own study. The pieces we want to restore belong to the Spanish baroque. We are not going to get deeper into the artistic analysis, except for the special features we can deduce, having mathematical consequences, which are the following: – In the artistic style we deal, the edges of the original pieces are not strictly well outlined, that is, some blur was present even when the work was painted (as a diﬀerence with photographs or drawings, or even other kind of paintings.
K represents the deterministic part of the deterioration, and n, the noise, is the stochastic part. As we said above, in our context, we may consider noise is not present , because the pictures have been cleaned by the professional restorers when we start the digital process. Then, we have: f (x, y) = fo (x, y) ∗ k(x, y) = fo (x − s, y − u)k(s, u)dsdu The deconvolution problem consists of recovering the original function, fo . This is an ill conditioned problem (). ). In the sense of Fourier transform, f = fo k (1) and k is a function decaying to zero in the large frequences, making the above formula unstable if used to recover fo .
Let us note that it was not the goal of this paper digital color processing, which is a diﬀerent problem, that can be studied in further works. Mathematical Models for Restoration of Baroque Paintings (a) 33 (b) (c) Fig. 3. 93, K = 103 ; (c) After the artistic restoration As a conclusion, we have devised a numerical algorithm for image deconvolution which is both reliable and robust for a large class of paintings. It allows diﬀerent processes independent from deconvolution without loss of accuracy and it is fast enough to be performed with a low computational cost and time.
Advanced Concepts for Intelligent Vision Systems: 8th International Conference, ACIVS 2006, Antwerp, Belgium, September 18-21, 2006. Proceedings by Chul Keun Kim, Doug Young Suh, Gwang Hoon Park (auth.), Jacques Blanc-Talon, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)