Object silhouettes and surface directions through stereo matching image processing

Akira Akiyama; Hideo Kumagai

doi:10.1117/12.2187657

3 September 2015 Object silhouettes and surface directions through stereo matching image processing

Akira Akiyama, Hideo Kumagai

Proceedings Volume 9579, Novel Optical Systems Design and Optimization XVIII; 95790D (2015) https://doi.org/10.1117/12.2187657
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

We have studied the object silhouettes and surface direction through the stereo matching image processing to recognize the position, size and surface direction of the object. For this study we construct the pixel number change distribution of the HSI color component level, the binary component level image by the standard deviation threshold, the 4 directional pixels connectivity filter, the surface elements correspondence by the stereo matching and the projection rule relation. We note that the HSI color component level change tendency of the object image near the focus position is more stable than the HSI color component level change tendency of the object image over the unfocused range. We use the HSI color component level images near the fine focused position to extract the object silhouette. We extract the object silhouette properly. We find the surface direction of the object by the pixel numbers of the correspondence surface areas and the projection cosine rule after the stereo matching image processing by the characteristic areas and the synthesized colors. The epipolar geometry is used in this study because a pair of imager is arranged on the same epipolar plane. The surface direction detection results in the proper angle calculation. The construction of the object silhouettes and the surface direction detection of the object are realized.

Citation Download Citation

Akira Akiyama and Hideo Kumagai "Object silhouettes and surface directions through stereo matching image processing", Proc. SPIE 9579, Novel Optical Systems Design and Optimization XVIII, 95790D (3 September 2015); https://doi.org/10.1117/12.2187657

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KEYWORDS

Linear filtering

Image filtering

Image processing

Binary data

RGB color model

Imaging systems

Sensors

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