文章基本信息

标题：Region Based Method Used in Image Segmentation and Object Recognition
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作者：Himmat Singh Rana ; Pawan Kumar Mishra
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：3
页码：514-517
语种：English
出版社：Ayushmaan Technologies
摘要：The digital image processing system is one of the main applications used in object detection and image segmentation. The digital image processing has been applied in several areas, especially where it is necessary to use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents an interest object that may be used in a specific study. There are several methods that intend to perform such task, but it is difficult to find a method, that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this work aims to presents an adaptable segmentation method that can be applied to different types of images, providing a better segmentation. The proposed method is based on a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. This paper presents a general approach to image segmentation and object recognition that can adapt the image segmentation algorithm parameters to the changing environmental condition. Segmentation parameters are represented by a team of generalized stochastic learning automata and learned using connectionist reinforcement learning techniques. The edge-border coincidence measure is first used as reinforcement for segmentation evaluation to reduce computational expensed associated with model matching during the early stage of adaptation. This measure alone, however, cannot reliable predict the outcome of object recognition. Therefore, it is used in conjunction with model matching where the matching confidence is used as a reinforcement signal to provide optimal segmentation evaluation in a closed-loop object recognition system. The adaptation alternates between global and local segmentation processes in order to achieve optimal recognition performance. Results are presented for both indoor and outdoor color image where the performance improvement over time is shown for both image segmentation and object recognition.