matlab 图像特征 代码,数字图像特征提取+matlab源代码
数字图像特征提取+matlab源代码
时间:2020-11-14 11:07来源:毕业论文
基于选定的样本图像及实验需求，将该图像转换为灰度图；随后对该样本图像实施平滑处理；主要采用中值滤波和邻域平均法完成了预处理工作。
摘 要
毕业论文关键词：图像预处理技术；LBP；CLBP；形状特征；矩特征
Image feature extraction technology represents a fundamental concept in image processing. It enables computers to perform feature extraction and analysis on images. At certain points or along specific curves within an image, subsets can be identified as portions of the overall structure. Depending on these subsets, different digital image extraction methods are employed. Key features typically include edge characteristics, shape descriptors, color attributes, texture properties, and other defining elements. These feature descriptors play a crucial role in tasks such as image identification and classification. In practical applications, sample images often contain numerous irrelevant information sources, which can lead to challenges in accurate feature extraction due to factors like background illumination variations, size differences, and noise interference. Therefore, minimizing the impact of noise while maintaining effective characterization becomes essential for optimal performance. This paper focuses on gray-scale processing and image smoothing for selected sample images before proceeding with texture and shape feature extraction. For shape analysis, both basic parameters (such as area, perimeter) and advanced metrics (like circularity) are measured alongside moment-based approaches to evaluate shape characteristics. Texture analysis employs local binary patterns (LBP) and complete local binary patterns (CLBP). The similarity between samples is assessed using histogram distance calculations between training and test sets. To enhance classification reliability under noise conditions, this paper evaluates four modes: standard LBP (LBP), rotation-invariant LBP (LBP-RI), CLBP-S mode, and CLBP-S-M mode. These methods are then applied with nearest neighbor classification for effective image categorization.
Keywords: image processing; LBP; shape feature; moments feature
目录
第一章绪论.1
1.1研究意义..1
1.2图像特征提取技术的发展趋势2
1.3研究内容..2
1.4研究方法..2
第二章图像预处理技术.4
数字图像特征提取+matlab源代码:http://www.lwfree.com/fanyi/lunwen_64783.html
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