On Image Coding and Understanding: A Bayesian Formulation for the Problem of Template Matching Based on Coded Image Data
Abstract
Some instances of the template matching problem, primarily for binary images corrupted with spatially white binary symmetric noise, were studied. The author used pixel-valued image data as well as data coded by two simple schemes, a modification of the Hadamard basis and the coarsening of resolution. Bayesian matching rules residing on M-ary hypothesis tests were developed. The performance evaluation of these rules was studied. This approach to the matching problem was intended to show the trade-off between the quantization and external noise with respect to the ability to detect an object of the image. He considered the case of the black square template on a white background or without a known background as well as a synthetic template without a known background. He called external noise the noise generated at the moment he received the uncoded image, in which case he had a "corrupt-code-detect system," or the noise coming as the effect of the transmission of the coded image over a noisy channel, in which case he had a "code-corrupt-detect system." In both cases, the noise was assumed to be white. Sum-of-pixels and histogram statistics were introduced to overcome the computational load induced by the correlation statistic with the penalty of an augmented probability of false alarm rate. What is intended to be shown in the present work is the usefulness and ability of combining an image coding technique with an algorithm for extracting some "base" information used in image understanding. Numerical and simulation results are provided.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1990
- Accession Number
- ADA452163
Entities
People
- Emmanuil N. Frantzeskakis
Organizations
- University of Maryland