Optoelectronic complex inner product for evaluating quality of image segmentation

Gregory J. Power; Abdul Ahad Sami Awwal

doi:10.1117/12.408548

29 November 2000 Optoelectronic complex inner product for evaluating quality of image segmentation

Proceedings Volume 4114, Photonic Devices and Algorithms for Computing II; (2000) https://doi.org/10.1117/12.408548
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

In automatic target recognition and machine vision applications, segmentation of the images is a key step. Poor segmentation reduces the recognition performance. For some imaging systems such as MRI and Synthetic Aperture Radar (SAR) it is difficult even for humans to agree on the location of the edge which allows for segmentation. A real- time dynamic approach to determine the quality of segmentation can enable vision systems to refocus of apply appropriate algorithms to ensure high quality segmentation for recognition. A recent approach to evaluate the quality of image segmentation uses percent-pixels-different (PPD). For some cases, PPD provides a reasonable quality evaluation, but it has a weakness in providing a measure for how well the shape of the segmentation matches the true shape. This paper introduces the complex inner product approach for providing a goodness measure for evaluating the segmentation quality based on shape. The complex inner product approach is demonstrated on SAR target chips obtained from the Moving and Stationary Target Acquisition and Recognition (MSTAR) program sponsored by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL). The results are compared to the PPD approach. A design for an optoelectronic implementation of the complex inner product for dynamic segmentation evaluation is introduced.

Citation Download Citation

Gregory J. Power and Abdul Ahad Sami Awwal "Optoelectronic complex inner product for evaluating quality of image segmentation", Proc. SPIE 4114, Photonic Devices and Algorithms for Computing II, (29 November 2000); https://doi.org/10.1117/12.408548

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