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We present a new hierarchical model applied to the problem of image semantic segmentation, that is, the association to each pixel in an image with a category label (e.g. tree, cow, building, ...). This problem is usually addressed with a combination of an appearance-based pixel classification and a pixel context model. In our proposal, the images are initially over-segmented in dense patches. The...
In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function,...
A multiscale segmentation method is proposed for multispectral imagery of high resolution by combining an adapted watershed algorithm and a region merging algorithm. Before the preliminary segmentation by the adapted watershed algorithm, a filtering method and a method for getting rid of local minimum areas are imposed to avoid over-segmentation. The whole process can be divided into five steps as...
Image segmentation is an important step for classification and feature extraction of high resolution remote sensing image. The purpose of this study is to find an improved segmentation method suitable for high resolution remote sensing image. Firstly a region homogeneity indictor called H index was introduced. Then the optimized edge gradient was obtained based on the integration of Canny operator...
It's necessary to discuss the topology model of digital images for integrating remote sensing and geographic information system in higher levels. Based on cellular complex theories, a hierarchical image representation is presented that maintains both raster and vector representations of an object inside a same data structure and to translate between spatial concepts in image understanding and mining...
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