New method for chain coding based on convolution

Kent Pu Qing; Robert W. Means

doi:10.1117/12.50833

1 December 1991 New method for chain coding based on convolution

Kent Pu Qing, Robert W. Means

Proceedings Volume 1567, Applications of Digital Image Processing XIV; (1991) https://doi.org/10.1117/12.50833
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

Shape analysis and synthesis are important capabilities in many image processing applications such as scene analysis, computer-aided design, and cartoon generation. A very important aspect of shape analysis is proper representation of object boundaries. Chain coding is an efficient, often-used method of representing these boundaries. The conventional method to generate a chain code is: (1) find and select an object boundary pixel; (2) find the nearest edge pixel, code its orientation, and mark it as used once; (3) repeat step (2) until there are no more boundary pixels. This procedure is computationally expensive. The bottleneck of the process is determining the direction to the nearest edge pixel. In the worst case, six of the eight surrounding pixels must be checked for 8-neighbor connectivity. We present a new, fast method for determining the connectivity for each pixel in the entire image using a 3 X 3 convolution kernel that produces an oriented connectivity map for the entire image. The chain code is then generated by following the map. The significant advantage is the ability to exploit high-speed convolutional processors such as HNC's Vision Processor (ViP). Using the ViP, the necessary convolution can be accomplished in less than 7 milliseconds for a 512 X 512 image. The ViP also can perform most other image processing functions within 7 milliseconds. Here we introduce the chain code algorithm based on a convolution result.

Citation Download Citation

Kent Pu Qing and Robert W. Means "New method for chain coding based on convolution", Proc. SPIE 1567, Applications of Digital Image Processing XIV, (1 December 1991); https://doi.org/10.1117/12.50833

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KEYWORDS

Convolution

Image processing

Image compression

Shape analysis

Digital image processing

Computer aided design

Neural networks

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