摘 要 图像特征提取技术是图像处理中的一个概念，可以通过计算机对图像特征进行提取，分析。有时图像中某一个点，某一条曲线，区域可以看作是一个子集。根据不同的子集，图像提取的方法也不同。图像的典型特征有边缘特征、形状特征、颜色特征、纹理特征等。这些特征信息对图像的识别和分类有重要的作用。在实际问题中，样本图片中参杂很多不相关的信息， 图像特征提取可能受到背景、光照、大小等噪声干扰，如何提取出具有良好表征性能且受到噪声干扰最小的特征参数，是本文的研究重点。 本文针对所选的样本图像和实验的要求，对图像进行了灰度化处理，接着对样本图像进行平滑处理，主要采用了中值滤波法和邻域均值法，对图像进行了预处理。 在预处理的基础上，对数字图像的纹理和形状特征进行特征提取。在形状特征提取中，一方面对样本图像的一些基本形状特征参数，包括面积、周长、圆形度和形状复杂度进行比较；另一方面结合图像矩特征和均值、方差、熵、梯度等特征分析图像的形状特点。 在纹理特征提取中，采用了局部二值模式和完全局部二值模式对图像纹理特征进行提取。提取出样本图像 LBP 和 CLBP 的直方图特征，并通过计算训练图像和测试图像的直方图距离来比较其相似性。选取了 LBP、CLBP_S模式、CLBP_M 和CLBP_S_M 四种模式，使用最近邻分类器对图像进行分类。 59524
毕业论文关键词：图像预处理技术；LBP；CLBP；形状特征；矩特征
Abstract Image feature extraction technology is a concept of image processing, which can be performed by a computer for image feature extraction and analysis. Sometimes a certain point, a curve, the area in the image can be seen as a subset. According to different subsets, the methods of digital image extraction are different. Typical features include edge feature, characteristic shape, color, texture and other features. These features information plays an important role on the identification and classification of images. In practical problems, sample pictures mixing a lot of irrelevant information, image feature extraction may be subject to background illumination, size and other noise. Therefore, how to extract a good characterization of performance are subject to noise interference minimum characteristic parameter. In this paper, the selected sample images and experiments require gray-scale processing, and image smoothing. The paper mainly uses median filter and neighborhood average method for image preprocessing. On the basis of pre-processing, the paper is going to extract the feature of texture and shape about digital image. In the shape feature extraction, on the one hand, some of the basic parameters of the shape of sample images, including area, perimeter, circularity and shape complexity of comparing are measured; on the other hand, binding characteristics of moments and mean, variance, entropy, gradient analyzes the characteristics of the shape. In the texture feature extraction, using local binary patterns and completely local binary pattern to extract texture feature. Extracting Histogram of CLBP and LBP of sample images compares their similarity by computing histogram distance of the training image and test image. Equivalence mode selects LBP, LBP rotation invariant pattern, CLBP_S mode and CLBP_S_M four modes, using the nearest neighbor classifier to classify images.
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.youerw.com/fanyi/lunwen_64783.html