Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
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Image Segmentation Using Local Variation
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The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
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Image Segmentation by Tree Pruning
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Image Segmentation Using Adaptive Tree-structured Wavelet Transform
CGIV '09 Proceedings of the 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization
Spatially coherent clustering using graph cuts
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A fast directed tree based neighborhood clustering algorithm for image segmentation
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Evaluating minimum spanning tree based segmentation algorithms
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review
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A review on MR vascular image processing algorithms: acquisition and prefiltering: part I
IEEE Transactions on Information Technology in Biomedicine
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In this paper we propose a computational efficient approach for image segmentation based on texture analysis, a 2D discrete cosine transform (DCT) is utilized to extract texture features in each image block. We first split the input image into M×N blocks, calculate the distances between neighbor blocks by a set of largest energy signatures from DCT for each block. Then we merge blocks with smallest distances to form larger regions. The process will repeat until we got desired number of regions. Experimental results show that our proposed method outperforms the existing image segmentation method, especially on efficiency aspect.