| dc.description.abstract | Due to the emerging of computer technologies, the functions and quality of imaging devices, such as digital camera, digital camcorder, and scanner, have been continuously improved. Moreover, the cost of these devices is also rapidly reduced. The content conveyed by multimedia is thus more splendid and richer. The proper management of the image data is thereby more and more important. The features that describe image data are mainly represented by using color, shape, and texture. In this thesis, we will elaborate on the analysis of texture and its application in image analysis.
The main purpose of this dissertation is to adopt the concept of wavelet transform and apply it to defect detection and texture synthesis in texture images. In texture defect detection, the defects can be discriminated according to the distribution ranges of wavelet coefficients between the normal and defective parts of texture images. In traditional texture defect detection methods, the normal parts of texture images have to be trained in advance. In this thesis, we propose a novel method to automatically determine the training regions based on the characteristics exhibited by normal and defective texture images. In this way, the detection error can be reduced because of the avoiding of environmental changes.
In texture synthesis, texture edge features can be extracted according to the characteristics of wavelet transformation, that is different frequency bands will exhibit different information. By combining horizontal and vertical edge information, the basic blocks of textures can be built. Original images can be synthesized by the extracted basic blocks. Moreover, we utilize the proposed texture defect detection method to verify the synthesis results. | en_US |