In this paper, an efficient tunnel crack detection and recognition method is proposed. It combines the analysis of crack intensity feature and the application of Support Vector Machine algorithm. At first, the original image is transformed into a binary image. Based on two thresholds technique, the object edge image can be obtained. Then assuming the image can be separated to some local images, we catogerize the local image into three types of pattern. They are the crack, non-crack and intermediate type, which have both of the two properties. A trainable classifier is built to classify these patterns. During this process, "Balanced" sub-images that satisfy for the two centers of geometric and gravity, are used as a trainable sample for the classifier. This leads to an effective classification system.
At present, various kinds of robots such as AIBO, ASIMO and etc, are available in public. However, the development of robots is still having some difficulties since of their complexity, continual changes of environments, limitation of resources and etc. To overcome this problem, robot developers often use the simulator that allows to program and test robots' program effectively under ideal environmental conditions where specified various conditions can easily be reproduced. It is still difficult to realize the simulator regardless of its usefulness, because the cost of simulator implementation seems the unexpected cost in the development of robots. As a result, it is need to realize the open robot simulation environment in which any kind of robots can be simulated. This paper focuses on vision-based robot simulation environment and describes a method to construct it. Finally, we implemented a simulator for Robocup Sony 4-Legged League by using this method.
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