Computer Vision Pattern Recognition

Efficient Visual Recognition Using the Hausdorff Distance by William Rucklidge

By William Rucklidge

This e-book offers the theoretical features and sensible improvement of a working laptop or computer imaginative and prescient procedure for looking a picture for a unique version item; the program is trustworthy, tolerates imperfections within the snapshot and version, and is quick sufficient for real-world applications.
A variety of seek concepts are evaluated. The features of the author's snapshot seek approach are tested on a number of examples, and purposes utilizing it to trace relocating items and navigate cellular robots are proven. This booklet is vital studying for anyone drawn to model-based item recognition.

Show description

Read Online or Download Efficient Visual Recognition Using the Hausdorff Distance PDF

Best computer vision & pattern recognition books

Robot Motion Planning

One of many final ambitions in Robotics is to create self sustaining robots. Such robots will settle for high-level descriptions of initiatives and may execute them with out extra human intervention. The enter descriptions will specify what the consumer wishes performed instead of tips to do it. The robots could be any form of flexible machine built with actuators and sensors lower than the keep watch over of a computing procedure.

Advanced Technologies in Ad Hoc and Sensor Networks: Proceedings of the 7th China Conference on Wireless Sensor Networks

Complex applied sciences in advert Hoc and Sensor Networks collects chosen papers from the seventh China convention on instant Sensor Networks (CWSN2013) held in Qingdao, October 17-19, 2013. The e-book beneficial properties cutting-edge experiences on Sensor Networks in China with the topic of “Advances in instant sensor networks of China”.

Advances in Biometrics for Secure Human Authentication and Recognition

''Supplying a high-level assessment of ways to guard your company's actual and intangible resources, Asset safety via safeguard information explains the easiest how you can enlist the help of your staff because the first defensive line in safeguarding corporation resources and mitigating defense hazards. It stories key issues surrounding computing device security--including privateness, entry controls, and probability management--to assist you fill the gaps that would exist among administration and the technicians securing your community structures.

Additional resources for Efficient Visual Recognition Using the Hausdorff Distance

Example text

In general, I use this second interpretation of the Hausdorffdistance in terms of disks; in Chap. 3, I show how this interpretation can be very useful. The undirected Hausdorff distance, H(A, B), is the maximum of h(A, B) and h(B, A). It measures the degree of mismatch between two sets by finding the point of A that is farthest from any point of B and vice versa. Intuitively, if the Hausdorff distance is d, then every point of A must be within a distance d of some point of B and vice versa; also, at least one point of A or B must be exactly d away from its closest neighbour in the other set.

1. 1)2, []~(/(P1 ) -- ~(1(1)2)[] = IIp - z, zll. t h e , H G = Pn)of Let A and B be any two point sets, and 9a. g2 E G any two transformations. qI ( A ). q~ ( B ) ) = max( h,( 91( A ). g2 ( B ) ) . h,( 92 (13 ). ql ( A ) ) ) --- max ( sup inf I [ . ql ( a ' ) - . ))- I'll. ~"l, inf ] ] . q 2 ' ( . q~(A)). g2(B)) inf HLq~ I ( g l ( A ) ) . G gl,gzEG = inf H ( 9 ( A } . B) so H c ( A. B) = I2fc( A. [1)" Ha is therefore synametric. [] When G is the group of translations and [1" II is any norm, [1(t'1 + t) - (I'2 + t)ll = It holds also when G is the group of rigid motions and I1,,1-p2 II.

If the transformation is incorrect, though, the function should be much more irregular, and have a larger overall amplitude. On the other hand, if the transformation is correct, but the image of the object is incomplete, due to occlusion, then there will be some large local peaks in this function, corresponding to the parts of the model which are not visible in the image, but apart from these peaks, it should also be small. These last two cases might have a similar overall chamfer distance, but inspection of this function might disambiguate them; the large local peaks in the second case might then be discounted, yielding a lower corrected distance value.

Download PDF sample

Rated 4.39 of 5 – based on 41 votes