By Sergey V. Sai, Ilya S. Sai, Nikolay Yu. Sorokin (auth.), Jacques Blanc-Talon, Don Bone, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This quantity collects the papers authorised for presentation on the twelfth Int- nationwide convention on “Advanced thoughts for clever imaginative and prescient structures” (ACIVS 2010). Following the ?rst assembly in Baden-Baden (Germany) in 1999, whichwaspartofalargemulticonference,theACIVSconferencethendeveloped into an self sustaining scienti?c occasion and has ever because maintained the culture of being a unmarried song convention. ACIVS 2010 attracted computing device scientists from 29 di?erent international locations, commonly from Europe, Australia, and the united states, but additionally from Asia. even supposing ACIVS is a convention on all parts of picture and video processing, submissions are likely to assemble inside yes significant ?elds of curiosity. This yr 3D and intensity processing and desktop imaginative and prescient and surveillance have been well known issues. Noteworthy are the becoming variety of papers with regards to theoretical devel- ments. we wish to thank the invited audio system Mubarak Shah (University of vital Florida), Richard Kleihorst (VITO, Belgium), Richard Hartley (A- tralian nationwide University), and David Suter (Adelaide college) for his or her precious contributions.
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Extra info for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I
Human subjects selected for the test were male and female undergraduate students with no knowledge of image quality assessment. Human subjects were trained as per ITU-Recommendations , and a small training session was conducted before the test using images other than the actual test images. The 160 images were rated using a ﬁve point quality scale; excellent, good, fair, poor and bad. The corresponding ratings were scaled upon a 1-100 score. Each image was rated by 50 humans, resulting in total 8000 human judgments.
Othman1 and Hamid R. ca Abstract. The task of image thresholding mainly classiﬁes the image data into two regions, a necessary step in many image analysis and recognition applications. Diﬀerent images, however, possess diﬀerent characteristics making the thresholding by one single algorithm very diﬃcult if not impossible. Hence, to optimally binarize a single image, one must usually try more than one threshold in order to obtain maximum segmentation accuracy. This approach could be very complex and time-consuming especially when a large number of images should be segmented in real time.
The Otsu method uses the image histogram to assign a threshold to the image . It divides the image into two diﬀerent classes of gray levels and assigns a threshold to the image where the variance of these two level is minimal . Liu et al.  made a comparison between Otsu segmentation technique and K-means method. Zhu et al.  try to reduce the long search time of 2D Otsu method by proposing a new algorithm that improves the histogram usage. Fengjie et al.  propose a new algorithm combining the 2D Otsu segmentation technique with genetic algorithms to segment images of cables covered with ice from their background.
Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part I by Sergey V. Sai, Ilya S. Sai, Nikolay Yu. Sorokin (auth.), Jacques Blanc-Talon, Don Bone, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)