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. A Boas and A G Yodh Department of Physics University of Pennsylvania Philadelphia Pennsylvania 19104 Received June 21 1996 accepted August 26 1996 revised September 16 1996 The diffusion of correlation is used to detect localize and characterize dyn Pavel. Gurevich, Sergey . Tikhomirov. Free University of Berlin. Roman . Shamin. . Shirshov. Institute of . Oceanology. , RAS, Moscow. Wittenberg,. December 12, 2011. Example: metabolic processes . 643 . Computer . Vision:. Template Matching, Image Pyramids and . Denoising. Jinxiang. . Chai. Today’s class. Template matching. Gaussian Pyramids. Laplacian Pyramids. Image denoising. Template matching. Photography:. Sampling + . Reconstruction. . . 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. Computer Vision. Filtering and Edge Detection. Connelly Barnes. Slides from Jason Lawrence, . Fei. . Fei. Li, Juan Carlos . Niebles. , Misha . Kazhdan. , Allison Klein, Tom . Funkhouser. , Adam Finkelstein, David . , 2017. Yong Jae Lee. UC Davis. Announcements. PS0 out today; due 4/14 Friday at 11:59 pm. Carefully read course website. Sign-up for piazza. 2. Plan for today. Image formation. Image noise. Linear filters. Fast Filtering. Problems in Computer Vision. Computer Vision in One Slide. 1) Extract some features from some images . 2) Use these to formulate some . (hopefully linear) constraints. 3) Solve a . system of . . for . blood. . vessels. . give. . incomplete. . visualizations. Our. . Method. 2. We. . create. . spatially. . varying. . Transfer. . Functions. ,. adapted. . to. . blood. . vessel. . Prof. Kristen . Grauman. UT-Austin. …. Announcements. Office hours . Mon-Thurs 5-6 pm. Mon: Yong Jae, PAI 5.33. Tues/Thurs: Shalini, PAI 5.33. Wed: Me, ACES 3.446. cv-spring2011@cs.utexas.edu. for assignment questions outside of office hours. 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). Fouhey. Winter 2019, University of Michigan. http://web.eecs.umich.edu/~fouhey/teaching/EECS442_W19/. Note: I’ll ask the front row on the right to participate in a demo. All you have to do is say a number that I’ll give to you. If you don’t want to, it’s fine, but don’t sit in the front. . Fouhey. .. Let’s Take An Image. Let’s Fix Things. Slide Credit: D. Lowe. We have noise in our image. Let’s replace each pixel with a . weighted. average of its neighborhood. Weights are . filter kernel. 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. .