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Panorama Based on Light Field Images OUTPUT Introduction: Light field camera captures an array of images by microlens near the detector while traditional camera  just takes one 2D picture of a 3D scene via one single lens. Though the resolution is not as good as  traditional camera, light field device still brings a lot of advantages in many ways [1,2]. For example,  depth detection, post-focus, 4D feature detection or even animated thumbnail 3D camera shake are  feasible from processing light field images. In the recent years, panorama is also developed as a picture  containing a wide viewing angle which is usually achieved by stitching a couple of 2D images  together[3,4]. Our project will look into rendering panorama pictures by stitching individual light field  images which will both contains a wide viewing angle and has the ability to calculate a lot of depth  related characteristic via MTALAB Light Field Toolbox. Objective Achieve panorama based on light field imag

Cell Segmentation in Slide Images Output OVERVIEW The advent of whole slide images (i.e. digitally scanning pathology slides can help usher in a new era of quantitative analysis of tissue samples. Whereas traditional judgments are made subjectively by a pathologist, image processing and computer vision algorithms can help determine cell densities in neoplasmic tissues, quantify the amount of Her2 in breast cancer, and much more. One of the largest challenges in digital pathology is accurate cell segmentation. In tissue samples, cells are closely packed, and sometimes overlap since the tissue being imaged is actually a 3D specimen, of which we are taking a 2D image. We propose to develop an image processing algorithm for segmenting distinct cells in tissue samples. A number of algorithms have already been proposed for this, including Euclidean distance transform, watershed segmentation, a combination of morphology operators, Laplacian of Gaussian filters, MSERdetectors, an

Model based markerless tracking OUTPUT OVERVIEW For our project we would like to implement a markerless augmented reality application on a mobile device. We would like to use an android device for this project. The goal of the project is to implement a real time tracking system using a model of a simple object, like a rubik’s cube. The principal deliverable in this project is a model-based tracking system implemented on a mobile device. We plan to implement the tracking algorithm ourselves. We will not use any in-built tracking tools available in ARtoolkit. Milestones : Camera Calibration: The first step in the project is to calibrate the camera on the mobile device.Accurate camera calibration is essential for implementing a good tracking system. This can be done using the Camera Calibration plugin provided by the ARToolKit orMATLAB. Object tracking and camera pose estimation: Next we would like to estimate the camera’s pose by using a control object whose CAD model i