With the availability of very high resolution multispectral imagery from sensors such as IKONOS and Quickbird, it is possible to identify small-scale features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult...

-Mathematical Morphology provides systematic approach to analyze geometric Characteristic of signal or images, has been applied to many application such as Edge Detection, Object segmentation, noise suppression. Image segmentation is one of the most important categories of image processing. The watersheds transformation for image segmentation using mathematical morphology is widely used. When w...

In this paper, we present a method of watershed-based region merging using ‘conflicting regions’ for segmentation of gray level images. It is obvious that both regions and edges in an image give important clues to segmentation in our visual system. So our method uses information from both regions and edges properly. We first obtain initial segments by applying watershed transformation to the im...

Mail sorting automation is yet a partially solved challenge. Due to no fixed positions of the semantic information in envelope images, part of this challenge is the automatic location of address blocks. In this paper we approach this problem by proposing a new postal envelope segmentation method based on 2-D histogram clustering and watershed transform. Segmentation task consists in detecting t...

Accurate segmentation of breast lesions is a crucial step in evaluating the characteristics of tumors. However, this is a challenging task, since breast lesions have sophisticated shape, topological structure, and variation in the intensity distribution. In this paper, we evaluated the performance of three unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion segmentati...

The goal of this paper is to investigate segmentation methods that eombine fast preproeessing algorithms using partial differential equations (PD Es) with a watershed transformation with region merging. VVeeonsider two well-founded PDE methods: a nonlinear isotropie diffusion filter that permits edge enhaneement, and a eonvex nonquadratie variational image restoration method whieh gives good de...

We present a robust region segmentation method based on a pseudo-distance map (PDM) that uses a watershed algorithm as a segmentation tool. The PDM is a regularized version of a Euclidean distance map (EDM) directly computed from the edgestrength function (ESF) of an input image without edge detection, which involves a thresholding operation. This unavoidably causes useful region boundary infor...

Brain medical images are generally prone to noise and also fraught with intensity heterogeneity within the tumor. Fuzzy and boundary discontinuity caused by the tumor also adversely affects the accuracy of the tumor segmentation. A method based on morphological structuring element map modification and markercontrolled watershed segmentation is proposed. Firstly, a structuring element map is con...

This paper proposes a new segmentation technique that combines multiresolution wavelet decompositions with the watershed transform. The wavelet transform is applied to the intensity image, producing detail and approximation coefficients. Gradient magnitudes of the approximation image at the coarsest resolution are computed, and an adaptive threshold is used to remove small gradient magnitudes. ...

⎯This paper presents an improved multi-object segmentation algorithm based on probabilistic labeling. First, a critical look is focused on utilizing vector calculus operator and combinational operator to rewrite Dirichlet integral into a matrix form, and boundary condition is defined to obtain the needed harmonic function. The only unique parameter β that dominantly affects the segmentation per...