PixelSciences IEEE Student Projects Centre

A colour image Enhancement by the histogram equalization process is as same as a grayscale image enhancement by histogram equalization except that colour space conversion technique included. The input colour image has R (Red),G (Green) and B (Blue) components which forms rectangular or cube coordinate colour space. When doing histogram equalization on the input image, It has to apply histogram equalization to each components RGB colour space consequently, the enhanced image object's original colour will vary. The HSV colour space has H (Hue),S (Saturation) and V (Value) components which forms circular or spherical coordinate colour space. When doing histogram equalization on the HSV colour space, It has to apply histogram equalization to only V component of HSV colour space and the resultant enhanced image object's original colour is preserved. Test Image 1 - dining table Test Image 1 - Histogram of original and Enhanced image ...

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 ...