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个人简介
I am a computer science graduate student at Carnegie Mellon University studying computational and biological vision. I am interested in the statistics of natural scenes and the use of these statistics to help infer scene properties from images. My current work is the inference of 3D shape from single images. Most existing approaches to shape inference work by starting with theoretical, physics-based models of image formation and then inverting these models. Unfortunately, inverting the image formation process is highly underconstrained. This forces us to revert to oversimplified models of image formation which may be unrealistic in natural scenes. Various assumptions about image formation parameters have to be made, such as Lambertian (matte) surface reflectance, uniform albedo, single point illumination, infinitely distant illumination, smooth 3D surface shape, the absence of shadows, the absence of interfacet reflection, and others. However, these assumptions are often violated in the real world, and this leads to poor generalization for these algorithms.
I believe that, in addition to clarifying the relative merit of these assumptions, a solid understanding of the statistics of natural scenes will uncover new, exploitable regularities in image formation that are not obvious from physical models. One of the earliest discoveries in our database was a direct anticorrelation between distance and brightness: darker image regions are more likely to be further away. That brighter objects appear closer was first observed by da Vinci. Our database provides the first evidence that this relationship holds in natural scenes. We believe that this trend is attributable to shadows: image regions that lie within object interiors, crevises, or concavities are farther from the observer than object exteriors, and the object interiors are more likely to lie in shadow. Additional exploitable statistical trends may result from regularities in the 3D shape of objects, regularities in their spatial relationships, regularities in the location and orientation of the observer, regularities in illumination conditions, etc. Despite the potential usefulness of statistical models, and the growing success of statistical methods in vision, few studies have been made into the statistical relationship between images and range images.
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2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)no. 1 (2019): 247-254
MEDICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2014)
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MEDICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2014)
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mag(2014)
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Computer Vision and Pattern Recognitionno. 1 (2013): 1674-1681
semanticscholar(2012)
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