Titlebook: Image Analysis and Recognition; International Confer Aurélio Campilho,Mohamed Kamel Conference proceedings 2004 Springer-Verlag Berlin Heid

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Hierarchical Regions for Image Segmentationomputational speed have been widely applied to image segmentation. In this paper, we propose a hierarchical region-based approach to the GRF. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a far more natural model in segmenting real images

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Efficiently Segmenting Images with Dominant Setshe computational loads of this approach, applications to large problems such as high resolution imagery have been unfeasible. In this paper we provide a method that substantially reduces the computational burden of the dominant set framework, making it possible to apply it to very large grouping pro

Folklore

Color Image Segmentation Using Energy Minimization on a Quadtree Representationinformation in an iterative minimization process of an energy function. The process has been applied to fruit images to distinguish the different areas of the fruit surface in fruit quality assessment applications. Due to the unsupervised nature of the procedure, it can adapt itself to the huge vari

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Segmentation Using Saturation Thresholding and Its Application in Content-Based Retrieval of Imageslts of our analysis. In our method, features are extracted either by choosing the hue or the intensity as the dominant property based on the saturation value of a pixel. We perform content-based image retrieval by object-level matching of segmented images. A freely usable web-enabled application has

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Spatial Discriminant Function with Minimum Error Rate for Image Segmentationaximum likelihood method assigns pixels based on the underlying distributions in image, it is inevitable to make decision errors when there are overlapping areas between the underlying distributions. However, this overlapping area can be minimized by a conversion of distributions which is proposed i

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Detecting Foreground Components in Grey Level Images for Shift Invariant and Topology Preserving Pyrle threshold is generally not enough to separate foreground components, perceived as individual entities. Our process is based on iterated identification and removal of pixels causing merging of foreground components with different grey levels. This is the first step to generate a pyramid which, wit

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Pulling, Pushing, and Grouping for Image Segmentationd as a functional optimisation process. Our computational function has three terms, the first pulls similar visual cues together, the second pushes different visual cues apart, and the third groups spatially adjacent visual cues without regarding their visual properties. An efficient numerical algor

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