Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
A New Contour-Based Approach to Object Recognition for Assembly Line Robots
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Generating segmented meshes from textured color images
Journal of Visual Communication and Image Representation
Generating segmented quality meshes from images
Journal of Mathematical Imaging and Vision
Image segmentation using topological persistence
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
On digital image representation by the delaunay triangulation
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A proper choice of vertices for triangulation representation of digital images
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part II
Edge Detection by Adaptive Splitting
Journal of Scientific Computing
Segmentation of Natural Images by Texture and Boundary Compression
International Journal of Computer Vision
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In this paper, an adaptive split-and-merge segmentation method is proposed. The splitting phase of the algorithm employs the incremental Delaunay triangulation competent of forming grid edges of arbitrary orientation and position. The tessellation grid, defined by the Delaunay triangulation, is adjusted to the semantics of the image data by combining similarity and difference information among pixels. Experimental results on synthetic images show that the method is robust to different object edge orientations, partially weak object edges and very noisy homogeneous regions. Experiments on a real image indicate that the method yields good segmentation results even when there is a quadratic sloping of intensities particularly suited for segmenting natural scenes of man-made objects.