Biologically Motivated Computer Vision: First IEEE by Maximilian Riesenhuber, Tomaso Poggio (auth.), Seong-Whan

By Maximilian Riesenhuber, Tomaso Poggio (auth.), Seong-Whan Lee, Heinrich H. Bülthoff, Tomaso Poggio (eds.)

It is our nice excitement and honor to prepare the 1st IEEE machine Society overseas Workshop on Biologically prompted laptop imaginative and prescient (BMCV 2000). The workshop BMCV 2000 goals to facilitate debates on biologically encouraged imaginative and prescient structures and to supply a chance for researchers within the sector of imaginative and prescient to work out and percentage the newest advancements in cutting-edge know-how. The fast development being made within the box of desktop imaginative and prescient has had a massive impression at the modeling and implementation of biologically encouraged laptop imaginative and prescient. a large number of recent advances and findings within the area of computing device imaginative and prescient may be awarded at this workshop. via December 1999 a complete of ninety complete papers have been submitted from 28 nations. to make sure the prime quality of workshop and court cases, this system committee chosen and authorised fifty six of them after a radical assessment technique. of those papers 25 can be awarded in five oral periods and 31 in a poster consultation. The papers span quite a few issues in computing device imaginative and prescient from computational theories to their implementation. as well as those very good shows, there'll be 8 invited lectures by means of unique scientists on “hot” subject matters. We needs to upload that this system committee and the reviewers did an exceptional activity inside of a decent schedule.

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Extra resources for Biologically Motivated Computer Vision: First IEEE International Workshop, BMCV 2000 Seoul, Korea, May 15–17, 2000 Proceedings

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In order to make this as stable as possible against lighting or contrast changes, the orientation is determined by the peak in a histogram of local image gradient orientations. The orientation histogram is created using a Gaussian-weighted window with σ of 3 times that of the current smoothing scale. These weights are multiplied by the thresholded gradient values and accumulated in the histogram at locations corresponding to the orientation, R ij . The stability of the resulting keys can be tested by subjecting natural images to affine projection, contrast and brightness changes, and addition of noise.

This formulation has allowed the problem of straight line detection to be treated as an optimization problem, in such a way that unlimited resolution can be achieved without calculating a large portion of the parameter space, as it is implied by the Hough transform and related techniques. e. local extrema and plateaus) have been characterized and explicitly taken into account while devising optimization strategies to control the window scanning. More specifically, the Nelder and Mead’s amoeba technique incorporating simulated annealing has been used in order to scan the parameter space while looking for peaks corresponding to the main straight features in the presented image.

6 Indexing and Matching For indexing, we need to store the SIFT keys for sample images and then identify matching keys from new images. The problem of identifying the most similar keys for high dimensional vectors is known to have high complexity if an exact solution is required. However, a modification of the k-d tree algorithm called the best-bin-first search method (Beis & Lowe [2]) can identify the nearest neighbors with high probability using only a limited amount of computation. An efficient way to cluster reliable model hypotheses is to use the Hough transform [1] to search for keys that agree upon a particular model pose.

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