2D to 3D Image Conversion and Disparity Map Estimation Using PSO Algorithms

Abstract

Image captured by two-dimensional camera contains no depth information. However in many applications we need depth information, for example such as in satellite imaging, robotic vision and target tracking. Stereo matching is used to extract depth information from images. The main aim of our project is to use stereo matching algorithms to plot the disparity map of segmented images which gives the depth information. Particle Swarm Optimization (PSO) algorithms are used for image segmentation. Our objective is to implement stereo matching algorithms on the segmented images and perform subjective analysis of reconstructed 3-D images. For some applications, such as image recognition or stereo vision, whole images cannot be processed, as it not only increases the computational complexity, but it also requires more memory. Thus, segmentation-based stereo matching algorithm should be used. This paper presents two novel methods for segmentation of images based on the Particle Swarm Optimization (PSO) and Darwinian Particle Swarm Optimization (DPSO).