lec11 - SUV Color Space and Filtering Computer Vision I...

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1 CS252A, Fall 2010 Computer Vision I SUV Color Space and Filtering Computer Vision I CSE252A Lecture 11 ,© David Kriegman ,© David Kriegman Diffuse Surface ,© David Kriegman Transparent Film ,© David Kriegman Dielectric Surface ,© David Kriegman +
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2 ,© David Kriegman ,© David Kriegman Data-dependent. Rotational (hence, linear) Transformation. The S channel encodes the entire specular component and an unknown amount of diffuse component. Shading information is preserved. ,© David Kriegman S U V RGB ,© David Kriegman ,© David Kriegman ,© David Kriegman
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3 ,© David Kriegman CS252A, Fall 2010 Computer Vision I Image Filtering CS252A, Fall 2010 Computer Vision I (From Bill Freeman) CS252A, Fall 2010 Computer Vision I Noise Simplest noise model independent stationary additive Gaussian noise the noise value at each pixel is given by an independent draw from the same normal probability distribution Issues this model allows noise values that could be greater than maximum camera output or less than zero for small standard deviations, this isn’t too much of a problem - it’s a fairly good model independence may not be justified (e.g. damage to lens) may not be stationary (e.g. thermal gradients in the ccd) CS252A, Fall 2010 Computer Vision I Linear Filters General process: Form new image whose pixels are a weighted sum of original pixel values, using the same set of weights at each point.
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