Advanced Concepts for Intelligent Vision Systems: 12th by Prarthana Shrestha, Hans Weda, Mauro Barbieri, Peter H. N.

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 court cases of the twelfth foreign convention on complicated options for clever imaginative and prescient platforms, ACIVS 2010, held in Changchun, China, in August 2010. The seventy eight revised complete papers provided have been conscientiously reviewed and chosen from one hundred forty four submissions. The papers are geared up in topical sections on snapshot processing and research; segmentation and side detection; 3D and intensity; algorithms and optimizations; video processing; surveillance and digicam networks; desktop imaginative and prescient; distant sensing; and popularity, type and monitoring.

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Blanc-Talon et al. ): ACIVS 2010, Part I, LNCS 6474, pp. 18–29, 2010. c Springer-Verlag Berlin Heidelberg 2010 Real-Time Retrieval of Near-Duplicate Fragments 19 images contain any visually similar (near-duplicate) fragments without any prior knowledge on how many such fragments (if any) exist. The problem can be specified as follows: Given two random images I and J , identify in them pairs of near-duplicate image fragments. The term ”near-duplicate fragments” refers to unspecified fragments of (almost) identical objects.

Structure of a real-time solution matching a video stream and a query image(s) Fig. 7. g. personal computers equipped with multi-core CPU’s). 0 library (including SURF detector provided there). Fig. 7 presents a screenshot captured during a real-time matching process. pl/~visible. The software has been tested using a variety of low- and medium-class webcams. The capturing resolutions is 640 × 480 (the recommended option) or 320 × 240. In the latter case, the quality of images is very poor and matching is far from perfect, but nevertheless still possible.

23]. Given two images I and J , we assume they contain correspondingly nI and nJ keypoints described by feature vectors of f dimensionality. Thus, both O2O and NN keypoint matching methods require f nI nJ operations. Assuming that n = max(nI , nJ ), the computational complexity of keypoint matching is O(f n2 ). Subsequently, the topological consistency of keypoint pairs is verified. For each matched keypoint pair, the algorithm of neighborhood building and analysis has the complexity O(n + m3 ) (if m neighbors are used).

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