A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Multiple Salient Closed Contours from Real Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Background Removal in Image Indexing and Retrieval
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Color image segmentation based on the normal distribution and the dynamic thresholding
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Matching and retrieval based on the vocabulary and grammar of color patterns
IEEE Transactions on Image Processing
Segmenting a low-depth-of-field image using morphological filters and region merging
IEEE Transactions on Image Processing
A two-level strategy for segmenting center of interest from pictures
Expert Systems with Applications: An International Journal
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A new algorithm for automatic extraction of interesting objects is proposed in this paper. The proposed algorithm can be summarized in two steps. First, segmentation of color image discriminating interesting objects and backgrounds is performed. According to the research stating, 'humans perceive things by contracting them into three to four essential colors,' a color image is segmented into three regions utilizing k-mean algorithm, followed by the merger of the regions performed when their similarities exceeds the critical value that is drawn from the calculation of the histogram similarity. Second, identifying an interesting object out of the segmented image, generated upon the image composition theory, is performed. To have a good picture, it is important to adjust positions of interesting objects as the picture composition theory. Extracting objects is a retro-deduction process using a weighted mask based on the triangular composition of picture. To show merits of the proposed method, experiments are conducted over 400 images in comparison with recently proposed k-means connectivity constraint and graph-based image segmentation methods.