A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
On minimum variance thresholding
Pattern Recognition Letters
Object segmentation using graph cuts based active contours
Computer Vision and Image Understanding
Automatic seeded region growing for color image segmentation
Image and Vision Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Scale-Based Connected Coherence Tree Algorithm for Image Segmentation
IEEE Transactions on Image Processing
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This study aims to segment objects within images of porcelain artifacts to help users retrieve the images in an efficient and convenient manner. Through digital archiving, a tremendous number of porcelain images have been created. To avoid interference due to the image's background during the retrieval process, it is necessary to segment objects in advance to accommodate high-precision image retrieval. In the proposed segmentation process, four texture features, including coarseness, contrast, directionality, and gradient, are first obtained. The morphological processing, which involves PCA (principal component analysis), Otsu's method, and object filter for opening and closing operation, is applied. Finally, regarding the objects selected by object filter, boundary extraction and watershed segmentation are performed to segment the porcelain objects from the background. In our image segmentation experiment using images of Chinese porcelain from various dynasties, featuring various shapes and colors, complete and accurate segmentation results are produced. The results can be used as a reference for future identification of the era to which the artifacts belong, and also to lay a foundation for future development of porcelain image retrieval techniques as a benefit to academic research.