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Target recognition by texture segmentation algorithm
DOI:10.1016/j.eswa.2015.09.057.png)
摘要
En 中文
In order to improve the performance of image segmentation, this paper presented a gray level jump segmentation algorithm, which defined the direction of the texture, simultaneously, calculated the width of ridge line, gave the distance characteristics between textures, and established the mathematical model of the texture border, accordingly presented a new texture segmentation algorithm and compared with other texture segmentation algorithms. The simulation results show that the segmentation algorithm has some advantages to texture segmentation, such as has higher segmentation precision, faster segmentation speed, stronger anti-noise capability, less lost information of target, and so on. The segmented regions hardly contain other texture regions and background region. Moreover, this paper extracted the characteristic points and characteristic parameters in various segmented regions for texture image to obtain the characteristic vector, compared the characteristic vector with the standard template vectors, and identified the type of target in a range of threshold value. Experimental results show that the proposed target recognition approach has higher recognition rate and faster recognition speed than the existing target recognition approaches. Advancements in image processing through the study of texture segmentation are not only applicable to image fields, but also are of important theoretical value to target recognition. These researches in this paper will play an important role in a theoretical reference and practical significance to the development of all target recognition departments based on image system such as the aerospace, public security, road traffic, and so on. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Texture segmentation
Gray level jump
Ridge line direction
Texture density
Feature extraction
Correct recognition rate
AI总结
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期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Event and Community Development: Planning Legacy for the 2008 European Capital of Culture, Liverpool
Texture segmentation using independent-scale component-wise Riemannian-covariance Gaussian mixture model in KL measure based multi-scale nonlinear structure tensor space
PATTERN RECOGNITION
IF7.6

