Document Type


Date of Award


Degree Name

Master of Science in Computer and Information Science - (M.S.)


Computer and Information Science

First Advisor

Frank Y. Shih


The chain code is a widely-used description for a contour image. Recently, a mid-crack code algorithm has been proposed as another more precise method for image representation. New algorithms using this new mid-crack code for image representation, restoration, and skeletonization are developed. The efficiency and accuracy can be increased obviously.

Firstly, the conversion of a binary image with multiple regions into the mid-crack codes is presented. A fast on-line implementation can be achieved using tables look-up. The input binary image may contain several object regions and their mid-crack codes can be extracted at the same time in a single-pass row-by-row scan. The perimeter and area of each region can be obtained during the execution of the algorithm. The inclusion relationship among region boundaries also can be easily determined.

Secondly, a simple and fast algorithm for the restoration of binary images based on mid-crack codes description is proposed. The algorithm developed has the advantages of speed, simplicity, and less storage. The algorithm also can be applied to gray-scale images with multiple regions efficiently.

Thirdly, it was observed that there exist four problems when running on some images with an in-contour in the restoration algorithm by Chang and Leu. We present the problems by a counterexample and propose simple improvements to modify the results so that the modified algorithm will allow the robustness, flexibility and correctness of the region filling and the complete reconstruction of an image. The idea of the improvement is similar to that of the restoration from mid-crack code description.

Finally, a new thinning algorithm for binary images based on the safe-point testing and mid-crack code tracing is established. Thinning is treated as the deletion of nonsafe border pixels from contour to the center layer-by-layer. The deletion is determined by masking a 3x3 weighted template and table look-up. The resulting skeleton does not require cleaning or pruning. The skeleton obtained possesses single-pixel thickness and preserves connectivity. The algorithm is very simple and efficient since only boundary pixels in each iteration are processed and look-up tables are used.



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