Mean shift texture surface detection based on WT and COM feature image selection

INTRODUCTION Visual inspection plays a vital role in assuring the quality of industrial products. Texture surface detection aims at detecting defects such as cracks, stains, broken points, etc. on texture surfaces. Due to the repetitive changes of texture gray values and structures, traditional detection methods based on intensity or edge detection are invalid, which makes texture surface detection one of the most intriguing problems during the past decades. Generally, most of previous a…     

基于自适应聚类和色差分析的烟草异物在线识别算法研究

提出一种利用聚类和色差分析的烟草异物在线识别算法,采用自适应ISODATA聚类取单元颜色特征,并分析烟叶和异物各分量的分布差异,将单元特征各分量根据直方图分布量化到HIS颜色空间中,从而得到样本学习结果并进行识别。实验结果表明,该算法的平均识别率达到95%,误识率在5%以下。     

A novel method for detection of hard exudates from fundus images based on SVM and improved FCM

Diabetic retinopathy (DR) is one of the most important causes of visual impairment. Automatic recognition of DR lesions, like hard exudates (EXs), in retinal images can contribute to the diagnosis and screening of the disease. To achieve this goal, an automatically detecting approach based on improved FCM (IFCM) as well as support vector machines (SVM) was established and studied. Firstly, color fundus images were segmented by IFCM, and candidate regions of EXs were obtained. Then, the SVM classifier is confirmed with the optimal subset of features and judgments of these candidate regions, as a result hard exudates are detected from fundus images. Our database was composed of 126 images with variable color, brightness, and quality. 70 of them were used to train the SVM and the remaining 56 to assess the performance of the method. Using a lesion based criterion, we achieved a mean sensitivity of 94.65 and a mean positive predictive value of 97.25 . With an image-based criterion, our approach reached a 100 mean sensitivity, 96.43 mean specificity and 98.21 mean accuracy. Furthermore, the average time cost in processing an image is 4.56 s. The results suggest that the proposed method can efficiently detect EXs from color fundus images and it could be a diagnostic aid for ophthalmologists in the screening for DR.