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Real-time Object Recognition on a GPU

<p>Shape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion.</p><p>The objects to be recognized can be translated, rotated and scaled.</p><p>The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation).</p><p>The similarity measure is based on dot products between normalized gradient directions in edges.</p><p>Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space.</p><p>A resolution pyramid is used to form a heuristic for the search that then gains real-time performance.</p><p>For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale.</p><p>We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required.</p><p>SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU.</p><p>This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds).</p><p>It is also very scalable and easily benefits from future devices without effort.</p><p>An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions.</p><p>The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.</p>

Identiferoai:union.ndltd.org:UPSALLA/oai:DiVA.org:liu-10238
Date January 2007
CreatorsPettersson, Johan
PublisherLinköping University, Department of Electrical Engineering, Institutionen för systemteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, text

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