Conversion Of 2d Image Into 3d Image Using Segmentation

Conversion of 2D Image into 3D Image Using Segmentation Prashant Dahiya1 â€Å"Computer Scientist at Adobe Systems, Noida† 1pdahiya@adobe.com Abstract—Image captured by two-dimensional camera contains no depth information. However depth information is needed in many applications, for example in robotic vision, satellite imaging and target tracking. To extract depth information from images Stereo matching is used. 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 details. Particle Swarm Optimization and K-means algorithms are used for image segmentation. Our main objective is to implement stereo matching algorithms on the segmented images, compare the results of K-means and PSO on the basis of objective parameters such as PSNR, execution time, density of disparity map and compression ratio and perform subjective analysis of reconstructed 3-D images. The compared results show that the Particle Swarm Optimization algorithm gives better 3-D reconstructed image. Keywords— K-means, PSNR, Particle Swarm Optimization, DSP, FPGA, ACO I. INTRODUCTION People can see the depth of the objects because they see the 3D world from two moderately dissimilar angles (one from right eye and other from left eye). Our brains then figure out how close things are by determining how far apart they are in the two images from our eyes. Our objective here is to do the similar thing using a computer. Initially aShow MoreRelatedA New Robust And Fully Automatic Technique For Calibration Of Three Dimensional ( 3d ) Freehand Ultrasound2324 Words   |  10 Pagesthree-dimensional (3D) freehand ultrasound. 3D Freehand ultrasound involves mounting a position sensor on a standard probe. The echo graphic B-scans can be localized in 3D, and can be compounded into dimensions. 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