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Light Field Images for Background Removal OUTPUT OVERVIEW Standard edge detection or foreground/background separation techniques, such as Otzu’smethod, require color or intensity differences between the background and regions that need tobe separated. For example, green screens are routinely set up as the background in a scene so thatthere is a clear difference in color between the background and foreground.Light filed images, captured in 4D and passively containing depth information for thescene can be used to approximate this effect. Depth estimation in the scene alone could provide a metric to separate the foreground and background, but more sophisticated methods are available.Considering the edge detection from a single image from a single viewpoint and analyzing the depths from the light field around the edges in that image, occluded edges and intensity or color edges can be distinguished. Along with the rest of the depth information this can allow the foreground and b

InSAR-derived Active-Layer Thickness Distributions Background: Large-scale thawing of arctic permafrost has a poorly-understood feedback effect on global climate through the release of CO2 and methane. Active Layer Thickness (ALT) is the maximum annual depth of thaw of surface soils and is designated by the World Meteorological Organization (WMO) as an essential climate variable for monitoring the status of permafrost.Interferometric Synthetic Aperture Radar (InSAR) is a widely-used geophysical technique for measuring surface deformation at high spatial resolution (Rosen et al. 2000). In recent years, InSAR has been successfully used to measure ground deformation due to seasonal permafrost freeze/thaw cycles and invert this deformation signature for a spatially extensive and finely-sampled map of ALT (Liu et al. 2012; Schaefer et al. 2015). Proposal:  The ALT retrieval algorithm developed by Liu et al. 2012 generates continuous spatial solutions of ALT within an individua

Depth from De-focus for Mobile phone camera Output OVERVIEW Depth from Defocus (DFD) is a technique in which a depth image of a scene is reconstructed from multiple images with varying camera parameters from a single camera [1]. Parameters that affect defocus characteristics of an image are; distance to the focus plane, the focal length, and the depth of field which is controlled by the aperture size. I would like to explore and implement DFD methods on smartphones [2]. My aim is to display a captured scene with a some kind of 3D technique, i.e. parallax mapping. A use for this could be simple capturing of 3D photos of people or of sculptured art. Technical Details In the first stage I will implement a DFD method in MATLAB with image stacks taken by a stationary DSLR camera, i.e. no translation or parallax between images. This will give me a solid understanding of the mathematics behind the optics and the algorithms. In the second stage I will extend the above me

Image Enhancement using Machine Learning OUTPUT OVERVIEW Automatic image enhancement is an active field of research and is used widely in professional image processing software. For our project, we want to create an automatic image enhancement tool that learns a user’s preferences so that subsequent images can be automatically enhanced in a personalized way. Our work will be mainly derived from [1]. The parameters learned in [1] are associated with contrast and color correction. We will attempt to learn these parameters, and if time permits, experiment with other parameters as well. The first part of this project is to select an optimal subset of training images from a large set of images, using the optimization technique described in [1] and [3]. The subset will be around 15-20 training images. For each training image, there will be a large possible number of combinations of parameters (depending on how many we choose to learn). To reduce the subset of possible param