By Prarthana Shrestha, Hans Weda, Mauro Barbieri, Peter H. N. de With (auth.), Jacques Blanc-Talon, Don Bone, Wilfried Philips, Dan Popescu, Paul Scheunders (eds.)
This booklet constitutes the refereed lawsuits of the twelfth overseas convention on complicated ideas for clever imaginative and prescient structures, ACIVS 2010, held in Changchun, China, in August 2010. The seventy eight revised complete papers awarded have been rigorously reviewed and chosen from one hundred forty four submissions. The papers are equipped in topical sections on photo processing and research; segmentation and facet detection; 3D and intensity; algorithms and optimizations; video processing; surveillance and digital camera networks; laptop imaginative and prescient; distant sensing; and popularity, category and monitoring.
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Extra resources for Advanced Concepts for Intelligent Vision Systems: 12th International Conference, ACIVS 2010, Sydney, Australia, December 13-16, 2010, Proceedings, Part II
The graph is formed from 8000 human judgments of 160 images corrupted with varying amount of white noise according to procedure explained in previous paragraph. 8 WHITENOISE Fig. 1. B. Mansoor and A. Anwar shows an inverse relation between amount of noise and human subjective image quality perception. HPV decreases sharply with even small variation in white noise, depicting signiﬁcant human sensitiveness to this type of distortion. The subjective results show that (a) For good human perception of an image quality even slight amount of white noise is not acceptable.
The object precision remains very high which means that the number of false positives has not changed (see Table 1). Apart form examples shown in Fig. 3, further examples of performances are shown in Figs 4 and 5. 3 Real-Time Implementation The overall structure of the implemented real-time solution is shown in Fig. 6. The operations are practically identical for both database images and for on-line captured images (video frames). We use a standard keypoint detector/descriptor (SURF) which provides variable numbers of keypoints, depending on the image complexity.
Pixel diﬀerence based, correlation based, edge based, spectral distance based, context based and human visual system based measures. The mutual relationship between these measures is found by plotting them via Kohonen’s Self Organizing Map (SOM) algorithm. e. Spectral Phase-Magnitude Error (S2), Block Spectral Phase-Magnitude Error (S5), Human Visual System Absolute norm (H1), Human Visual System L2 norm (H2), Edge Stability Measure (E2) are most discriminating. In this paper, we have investigated subjectively the performance of these quality measures for White noise corrupted images.