A two-stage string matching method for the recognition of two-dimensional (2-D) objects is proposed in this work. The first stage is a global cyclic string matching. The second stage is a local matching with local dissimilarity measure computing. The dissimilarity measure function of the input shape and the reference shape are obtained by combining the global matching cost and the local dissimilarity measure. The proposed method has the advantage that there is no need to set any parameter in the recognition process. Experimental results indicate that the hostage string matching approach significantly improves the recognition rates compared to the one-stage string matching method.

The difference threshold of judging chromatic light was evaluated in this study. The experimental factors included target colour, background colour, interspacing, target surface interferences, and luminance level. Results showed that humans were more sensitive to green light than to red or blue light. The differential brightness sensitivity was higher for dark target on bright background than for bright target on dark background. The interspacing between two targets also affected differential brightness sensitivity, but the luminance level of the standard stimulus was found to have no effect on difference threshold. In addition, there was significant individual differences in differential brightness sensitivity. Further, by comparing the results obtained from the Analytic Hierarchy Process (AHP) and the method of constant stimuli, it suggests that the AHP approach was a valid and effective method to assess difference threshold.

This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.