PixelSciences IEEE Student Projects Centre

The K-means clustering algorithm has wide applications for data and document-mining, digital image processing and different engineering fields. In addition to that, the clustering algorithm is composed of simple algorithm steps and has fast convergence, however it is suffered by initial centroid selection while clustering an image. The initial centroid selection determines the clustering algorithm consistent results and performance. The good initial cluster-centroids lead to good clustering results, otherwise the bad initial cluster-centroids selection lead to bad results. In order to achieve K-means clustering's consistent result and performance, we propose initial cluster centroids selection by Genetic optimization algorithm. The GA (genetics algorithm) has efficient search operations (selection, crossover and mutation) for determination of global minima on selection of cluster centroid problems GA - population A set of solutions or chromosomes are called...

Introduction to K-means clustering-algorithm The K-means clustering algorithm comprised of three steps, they distance, minimum-distance cluster assignment and cluster centroid update. These three steps are repeatedly executed until convergence meet or number of iteration end. The K-means algorithm splits the given dataset (image) into K number of clusters or groups It assigns a member(pixel) into a cluster (group) based on minimum distance between the pixel and all cluster centroids The algorithm is not complex and iterative procedure steps The high speed convergence but stayed on local minimum at most of times Unsupervised Clustering The K-means algorithm has no training phase. The dataset (image pixels) to be clustered is not attached with class or target variables. Assumed K number of C...

It is shown below IEEE project titles of image processing on Image Enhancement, Image De-noising, Image Segmentation and Object Recognition. Image Enhancement A Dynamic Histogram Equalization for Image Contrast Enhancement. Automatic Image Equalization and Contrast Enhancement Using Gaussian Mixture Modelling. Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images. Contrast-Preserved Chroma Enhancement Technique using YCbCr Colour Space. Multi Segment Histogram Equalization for Brightness Preserving Contrast Enhancement. Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution. Image Denoising SUSAN controlled decay parameter adaption for non-local means image denoise. Turbulent-PSO-Based Fuzzy Image Filter With No-Reference Measures for High-Density Impulse Noise. Two-Direction Nonlocal Model ...