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In this paper, object retrieval techniques based on texture segmentation are proposed for use in local region texture images. In order to perform the segmentation of a local region texture image in this study, three texture features were first extracted: coarseness, contrast and directionality. For image retrieval, texture features for visual perception (TFVP), colour histogram for K-mean (CHKM) and shape features for principal moments of inertia (SFPMI) features of the object were extracted after segmentation. TFVP, CHKM and SFPMI are the texture, colour features and shape features, respectively, of the object region in an image; they are used for image retrieval. In order to more precisely represent the accuracy of this proposed method, the image segmentation/ image retrieval results are compared with other methods. It was shown that the segmentation performance of the proposed method evaluated using misclassification error (ME), relative foreground area error (RAE), modified Hausdorff distance (MHD) and AVE is better than other methods. The results clearly show that the performances of the proposed method for local region texture image retrieval are significantly superior to those of the other methods for global region texture image retrieval. The object segmentation in this study can accurately segment the image object, thereby obtaining image retrieval results with much higher precision.

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This study's main intention is to propose the best-fit line detection, Region of Interest (ROI) detection and precision measuring method by using an object with an irregular edge in a high precision optical image. In general, an object with a smooth edge will actually display a very irregular and uneven gray scale distribution after being imaged by a high precision optical instrument, which may lead to a big problem if it is used in precision detection and measurement. Therefore, in this study we will individually propose the best-fit line detection, ROI detection conversion and the precision measuring method for irregular edges. The best-fit line detection is to propose two different best-fit line detection modes without affecting the computing time. The ROI detection method is to detect the region of most interest to the user and then to convert the regional line into a full-field image. For precision measurement, in this study we will propose a method for calculating the distance from the line to the edge and the distance between lines to achieve the measuring purpose.