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30 September 2003 Improving object recognition accuracy and speed through nonuniform sampling
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Proceedings Volume 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision; (2003) https://doi.org/10.1117/12.519095
Event: Photonics Technologies for Robotics, Automation, and Manufacturing, 2003, Providence, RI, United States
Abstract
Silhouette-based shape retrieval and recognition have been well studied, because silhouettes are compact representations of object shape, and because they can be reliably extracted in controlled-environment applications such as digitizing museum collections. In past work, we developed a fast and accurate method for retrieval and recognition of object silhouettes and other closed planar contours. The method is based on a combination of alignment, correspondence, eigenspace dimensionality reduction, and example-based retrieval. Its efficiency and accuracy result from the particular forms of each of these components and the way they are combined. This paper presents two improvements to the method: non-uniform sampling and a new similarity measure. The improved method ranks first in retrieval accuracy in comparison with eight prior methods tested on a benchmark database of 1,400 shapes. Its classification accuracy is 96.8% for the first-ranked class hypothesis, and it returns the correct classification in the top ten hypotheses 99.8% of the time. Average time for retrieval and recognition is approximately 0.6 seconds in Matlab on a PC.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Boaz J. Super "Improving object recognition accuracy and speed through nonuniform sampling", Proc. SPIE 5267, Intelligent Robots and Computer Vision XXI: Algorithms, Techniques, and Active Vision, (30 September 2003); https://doi.org/10.1117/12.519095
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