PPT-Edge Detection Enhancement Using Gibbs Sampler

Author Michael Sedivy Introduction Edge Detection in Image Processing MCMC and the Use of Gibbs Sampler Input Results ConclusionFuture Work References Edge Detection Detecting Edges in images is a complex task but it useful in other image processing problems. Alex Wade. CAP6938 Final Project. Introduction. GPU based implementation of . A Computational Approach to Edge Detection. by John Canny. Paper presents an accurate, localized edge detection method. Purpose. CSE . 576. Ali Farhadi. Many slides from Steve Seitz and Larry . Zitnick. Edge. Attneave's. Cat (1954) . Edges are caused by a variety of factors. depth discontinuity. surface color discontinuity. illumination discontinuity. Winter in . Kraków. photographed by . Marcin. . Ryczek. Edge detection. Goal: . Identify sudden changes (discontinuities) in an image. Intuitively, most semantic and shape information from the image can be encoded in the edges. Van Gael, et al. ICML 2008. Presented by Daniel Johnson. Introduction. Infinite Hidden Markov Model (. iHMM. ) is . n. onparametric approach to the HMM. New inference algorithm for . iHMM. Comparison with Gibbs sampling algorithm. From Colored Fields to Thin Junction Trees. Yucheng. Low. Arthur . Gretton. Carlos . Guestrin. Joseph Gonzalez. Gibbs Sampling [. Geman. & . Geman. , 1984]. Sequentially. for each variable in the model. SUQ13. January 7, 2013. Some implementation . issues with . iterative Gaussian samplers . in finite precision. Outline. Iterative linear solvers and Gaussian samplers …. Convergence theory is the same. From Colored Fields to Thin Junction Trees. Yucheng. Low. Arthur . Gretton. Carlos . Guestrin. Joseph Gonzalez. Inference:. Inference:. Graphical. Model. Sampling as an Inference Procedure. Suppose we wanted to know the probability that coin lands “heads”. Source: D. Lowe, L. Fei-Fei. Canny edge detector. Filter image with x, y derivatives of Gaussian . Find magnitude and orientation of gradient. Non-maximum suppression:. Thin multi-pixel wide “ridges” down to single pixel width. Winter in . Kraków. photographed by . Marcin. . Ryczek. Edge detection. Goal: . Identify sudden changes (discontinuities) in an image. Intuitively, most semantic and shape information from the image can be encoded in the edges. Alex Wade. CAP6938 Final Project. Introduction. GPU based implementation of . A Computational Approach to Edge Detection. by John Canny. Paper presents an accurate, localized edge detection method. Purpose. . Szymon Rusinkiewicz. Convolution: . how to derive discrete 2D convolution. 1-dimensional. 2-dimensional. Discrete. Where f(i,j) is any given image, g(i,j) is a mask, . h(i,j) is an new image obtained.. hindcast . results and its preliminary evaluation in the South China Sea. Shihe Ren. a. , Xueming Zhu. a. , and Drevillon Marie. b. a. . National Marine Environmental Forcasting Center, Beijing, China. What is Edge Detection?. Identifying points/Edges . in a digital image at which the image brightness changes sharply . or . has . discontinuities. . - Edges are significant local changes of intensity in an image.. Edge. Attneave's. Cat (1954) . 2. Edges are caused by a variety of . factors.. depth discontinuity. surface color discontinuity. illumination discontinuity. surface normal discontinuity. Origin of edges.