This paper describes the use of a genetic algorithm to solve a hydrology design problem determining an optimal prescription of Best Management Practices in a flood-prone watershed. The result is an interactive tool for hydrology engineers that can be used to explore the effects of water retention and infiltration devices as an alternative to traditional storm sewers. The model has proved capabl...

In this paper, a very fast, noise robust and accurate video object plane (VOP) extraction algorithm based on wavelet transform and watershed segmentation techniques is proposed. The proposed algorithm firstly, applies the wavelet transform on incoming frames and uses the approximation coefficient matrix of the first level through out the algorithm. This not only increases the speed of the algor...

The segmentation of images into meaningful and homogenous regions is a key method for image analysis within applications such as content based retrieval. The watershed transform is a well established tool for the segmentation of images. However, watershed segmentation is often not effective for textured image regions that are perceptually homogeneous. In order to segment such regions properly, ...

In this paper, we investigate the links between the flooding paradigm and the topological watershed. Guided by the analysis of a classical flooding algorithm, we present several notions that lead us to a better understanding of the watershed: minima extension, mosaic, pass value and separation. We first make a detailed examination of the effectiveness of the divide set produced by watershed alg...

Deforestation is a serious issue that most nations face today. Deforestation is primarily due to urbanization. Most nations that are presently under the scanner for deforestation had immense forest stretch. The need for settlements and development has triggered such a scenario. The proposed method segment the forest region with city and classify how much deforestation occurs. In this paper imag...

Image segmentation and its performance evaluation are very difficult but important problems in computer vision. A major challenge in segmentation evaluation comes from the fundamental conflict between generality and objectivity. The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects...

Imaging assays of cellular function, especially those using fluorescent stains, are ubiquitous in the biological and medical sciences. Despite advances in computer vision, such images are often analyzed using only manual or rudimentary automated processes. Watershed-based segmentation is an effective technique for identifying objects in images; it outperforms commonly used image analysis method...

This paper presents an integrated method for adaptive segmentation of brain tissues in three-dimensional (3-D) MRI (Magnetic Resonance Imaging) images. The method intends to do the volume segmentation in a slice-by-slice manner. Firstly, some slices in the volume are segmented using an automatic algorithm composed of watershed, fuzzy clustering (Fuzzy C-Means) and resegmentation. Then their adj...

A major drawback when using the watershed transformation as a segmentation tool comes from the over-segmentation of the image. Over-segmentation is produced by the great number of minima embedded in the image or in its gradient. A powerful technique has been designed to suppress over-segmentation by a primary selection of markers pointing out the regions or objects to be segmented in the image....

Abstract. Surface reconstruction from a set of noisy point measurements has been a well studied problem for several decades. Recently, variational and discrete optimization approaches have been applied to solve it, demonstrating good robustness to outliers thanks to a global energy minimization scheme. In this work, we use a recent approach embedding several optimization algorithms into a commo...