Machine vision
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Texture segmentation through eigen-analysis of the pseudo-Wigner distribution
Non-Linear Analysis
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Bayesian Fusion of Color and Texture Segmentations
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Recognizing Surfaces Using Three-Dimensional Textons
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Unsupervised segmentation of medical images using DCT coefficients
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
DCT histogram optimization for image database retrieval
Pattern Recognition Letters
Generalized Gaussian density for skin detection in DCT domai
Machine Graphics & Vision International Journal
A novel algorithm for segmentation of lung images
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Flaw detection of domed surfaces in LED packages by machine vision system
Expert Systems with Applications: An International Journal
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It is of utmost importance in multimedia processing to achieve still image segmentation, i.e., to partition images into regions of coherent color and texture. In this paper we propose a novel image segmentation method using a special visual descriptor. For each pixel p, the discrete cosine transform (DCT) of the block centered on p together with its location in the image is employed as its content descriptor thus resulting in a long vector vp??, referred to as situational DCT descriptors (SDDs). A scalar quantization step is then carried out on the DCT component of SDDs to reflect the fact that the human vision system is not of uniform discriminative sensitivity to details of different frequencies. Next the principal component analysis is conducted to drastically reduce the dimensionality of SDDs. The adaptive K-means algorithm is then performed to arrive at the region assignment for each pixel. The final partitioning results are obtained after performing the post-processing step. Encouraging empirical performance has been demonstrated.