PPT-Accelerating Spatially Varying Gaussian Filters

Jongmin Baek and David E Jacobs Stanford University Motivation Input Gaussian Filter Spatially Varying Gaussian Filter Accelerating Spatially Varying Gaussian Filters Accelerating. Overview of Filtering. Convolution. Gaussian filtering. Median filtering. Overview of Filtering. Convolution. Gaussian filtering. Median filtering. Motivation: Noise reduction. Given a camera and a still scene, how can you reduce noise?. 643 . Computer . Vision:. Template Matching, Image Pyramids and . Denoising. Jinxiang. . Chai. Today’s class. Template matching. Gaussian Pyramids. Laplacian Pyramids. Image denoising. Template matching. Greg Cox. Richard Shiffrin. Continuous response measures. The problem. What do we do if we do not know the functional form?. Rasmussen & Williams, . Gaussian Processes for Machine Learning. http://www.gaussianprocesses.org/. Mikhail . Belkin. Dept. of Computer Science and Engineering, . Dept. of Statistics . Ohio State . University / ISTA. Joint work with . Kaushik. . Sinha. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Photography:. Sampling + . Reconstruction. . . Motivation: Image . denoising. How can we reduce noise in a photograph?. Let’s replace each pixel with a . weighted. average of its neighborhood. The weights are called the . filter kernel. What are the weights for the average of a . They replace the value of an image pixel with a combination of its neighbors. Basic operations in images. Shift Invariant. Linear. Thanks to David Jacobs for the use of some slides. Consider 1D images. Mikhail . Belkin. Dept. of Computer Science and Engineering, . Dept. of Statistics . Ohio State . University / ISTA. Joint work with . Kaushik. . Sinha. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Transiting Exoplanets. Dainis Dravins. 1. , . Hans-Günter Ludwig. 2. ,. Erik Dahlén. 1. ,. Martin Gustavsson. 1. , . Hiva. Pazira. 1. . 1. . Lund Observatory, Sweden, . 2. . Landessternwarte. Ali Farhadi. Many slides from Steve Seitz and Larry . Zitnick. What is an image?. F. ( ) = . Image Operations. (functions of functions). F. ( ) = . Image Operations. (functions of functions). W. Wuensch. 11-12-2009. Accelerating structure assembly. Pulsed ΔT in accelerating structure. Beam-loading compensation. Reacting to breakdown. On/off/ramp mechanism. Dynamic vacuum. Refining design and 10% parameter consistency. and . James Haralambides. Department of . Mathematics and Computer Science . Barry University. 11300 NE 2. nd. Ave.. Miami Shores, FL 33161. Phone: (305) . 899-3035. We present an algorithm that enhances the blood vessels of retinal images to support medical diagnosis and clinical study. Accurate imagery of blood vessel features such as diameter, curvature, and color is detrimental to the diagnosis of diseases and the application of appropriate treatments. The objectives of this work are in two main directions: a) locate, identify, and amplify blood vessel boundaries and structures, and b) exploit hardware parallelism to increase algorithmic efficiency. . CSU Los Angeles. This talk can be found on my website:. www.calstatela.edu/faculty/ashahee/. These are the Gaussian primes.. The picture is from . http://mathworld.wolfram.com/GaussianPrime.html. Do you think you can start near the middle and jump along the dots with jumps of. – . 2. Introduction. Many linear inverse problems are solved using a Bayesian approach assuming Gaussian distribution of the model.. We show the analytical solution of the Bayesian linear inverse problem in the Gaussian mixture case..