By Hossein Ziaei Nafchi, Reza Farrahi Moghaddam, Mohamed Cheriet (auth.), Jong-Il Park, Junmo Kim (eds.)
The quantity set, including LNCS 7728 and 7729, includes the rigorously reviewed and chosen papers provided on the 9 workshops that have been held along side the eleventh Asian convention on desktop imaginative and prescient, ACCV 2012, in Daejeon, South Korea, in November 2012. From a complete of 310 papers submitted, seventy eight have been chosen for presentation. LNCS 7728 includes the papers chosen for the foreign Workshop on laptop imaginative and prescient with neighborhood Binary development variations, the Workshop on Computational images and Low-Level imaginative and prescient, the Workshop on Developer-Centered machine imaginative and prescient, and the Workshop on history versions problem. LNCS 7729 comprises the papers chosen for the Workshop on e-Heritage, the Workshop on colour intensity Fusion in desktop imaginative and prescient, the Workshop on Face research, the Workshop on Detection and monitoring in not easy Environments, and the foreign Workshop on clever cellular Vision.
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Additional info for Computer Vision - ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012, Revised Selected Papers, Part II
D. Kuhnert 170 Region Segmentation and Object Extraction Based on Virtual Edge and Global Features . . . . . . . . . . . . . . . . . . . . . . . Fumihiko Mori and Terunori Mori 182 Adaptive Sampling for Low Latency Vision Processing . . . . . . . . David Gibson, Henk Muller, Neill Campbell, and David Bull 194 Colorimetric Correction for Stereoscopic Camera Arrays . . . . . . . Clyde Mouﬀranc and Vincent Nozick 206 Camera Calibration Using Vertical Lines .
C Springer-Verlag Berlin Heidelberg 2013 2 H. Ziaei Nafchi, R. Farrahi Moghaddam, and M. Cheriet version of the proposed algorithm has been submitted to the H-DIBCO 2012 contest. The rest of the paper is organized as follows. In section 2, previous work is presented. Phase congruency model is described in section 3. In section 4, feature maps used in this paper are discussed. Binarization model is described in section 5. Section 6 provides comprehensive experimental results for the proposed method.
Also, Gaussian and median ﬁltering are used in order to improve the ﬁnal binarized output. Gaussian ﬁlter is used for further enhance the output and median ﬁlter is applied to remove noises. To detect bleed-through degradation, a feature map based on regional minima is proposed and used. The proposed binarization method provides output binary images with high recall values and competitive precision values. Promising experimental results obtained on the DIBCO’09, H-DIBCO’10 and DIBCO’11 datasets, and this shows the robustness of the proposed binarization method against a large number of diﬀerent types of degradation.