2. Automatic Image Segmentation using Graph Cuts (Winter 2017)
Abstract: This project aims to segment image into forground and background using automated process and thus requiring no user participation. Probability of a pixel fitting to background and foreground is calculated using Gaussian Mixture Model. This model first uses k-means clustering to cluster closely related pixels to initialise the weights and means of the gaussians of two Gaussian Mixture Model and then ”fits” the needed gaussian mixture model according to training data i.e. image itself. Segmentation is binary into foreground and background but remain unclassified to either category, based on the probability of the pixel fitting to either gaussians. The probabilities were used to construct edge weights in the graph to which graph cut was done and the overall error function is minimised. The labelling as output from graph cut is the final classification of foreground and background.
3. Detection of rotation of convex object using 8 directional Chain Code, (Monsoon 2017)
Abstract: Developed an algorithm using directional Freeman Chain Code of Eight Directions. Implementation in C (C11) programming language used two input files (from same directory) and outputs to console whether the objects are rotations of other.
Test files, implemented code, report, presentation of the project is avilable at: