This paper extends the use of stochastic watershed, recently introduced by Angulo and Jeulin [1], to unsupervised segmentation of multispectral images. Several probability density functions (pdf), derived from Monte Carlo simulations (M realizations of N rsandom markers), are used as a gradient for segmentation: a weighted marginal pdf a vectorial pdf and a probabilistic gradient. These gradien...

In this paper we present a segmentation technique based on tools from mathematical morphology which can be successfully used for automatic segmentation of diatom images. The proposed method belongs to the class of hybrid segmentation techniques, and is based on the morphological watershed from markers. The novelty of this method is the computation and selection of markers. A new connected opera...

Blood cell counting is a major field of study in biomedical engineering. In human blood cell segmentation cases, many methods are being studied and applied for obtaining better results. In this paper, we present an approach for counting different blood cells during blood smear test. The approach which is presented in this paper is by segmentation using morphological watershed transformation. Mo...

Watershed segmentation of spectral images is typically achieved by first transforming the high-dimensional input data into a scalar boundary indicator map which is used to derive the watersheds. We propose to combine a Random Forest classifier with the watershed transform and introduce three novel methods to obtain scalar boundary indicator maps from class probability maps. We further introduce...

Segmentation of a 3-dimensional (3D) polygonal mesh is a method of breaking the mesh down into \meaningful" connected subsets of vertices called regions. The method used here is based on the watershed segmentation scheme which appears prominently in the image segmentation literature and was later applied to the 3D segmentation problem. The watershed algorithm is based on a scalar value (curvatu...

This paper describes a new approach for watershed segmentation on triangular mesh. Common watershed approaches use descending or flooding processes on a connected vertex structure. Watershed transformation is not related to a structure in particular: it only requires connected elements and a height function. This method is widely used on 2D, 3D images as well as on 3D meshes. Here, a connected ...

The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis. Mathematical morphology is a very well established theory to process images. Segmentation by morphological means is based on watershed that considers an image as a topographic surface. Watershed requires input and marker image. The user can provide the lat...

Brain tumor extraction in magnetic resonance imaging (MRI) has becoming an emergent research area in the field of medical imaging system. Extraction involves detection, localization, tracking, enhancement and recognition of the tumor from the MR brain images. Brain tumor extraction helps in finding the exact size and location of tumor. The watershed transform is a popular and has interesting pr...

Edge preserving smoothing and image simplification is of fundamental importance in a variety of remote sensing applications during feature extraction and object detection procedures. The construction of a pre-processing filtering tool for edge detection and segmentation tasks is still an open matter. Towards this end, this paper brings together two advanced nonlinear scale space representations...

A new segmentation procedure for color images is detailed in this article. The segmentation still ends with the watershed function, but the whole series of pre-treatments applied to the color images is of interest. After a HLS transformation of the images, a color reduction is applied, followed by a median filtering. The gradient is then constructed from this resulting image. After this step, t...