logging in or signing up mishra DAS 1 aSGuest47225 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 13 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: June 04, 2010 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Decoupled Active Surface for Volumetric Image Segmentation : Decoupled Active Surface for Volumetric Image Segmentation A. Mishra, P. Fieguth, and D. Clausi Department of Systems Design Engineering, University of Waterloo Objective : Objective Background : Background Eulerian-Langragian frame work Bayesian framework Background (DAC) : Background (DAC) A. Mishra, P. Fieguth, D. Clausi, Decoupled Active Contour (DAC) for boundary identification, TPAMI, 2010 (preprint) Background (DAC) : Background (DAC) Background DAC : Background DAC Step 1 : Measurement (Viterbi search) Step 2: Generation of non-stationary prior Step 3: Linear Bayesian Estimation DAC to DAS (Non-trivial) : DAC to DAS (Non-trivial) 1. Representation: v(a, b) = [x(a, b), y(a, b), z(a, b)] is typically not a one-to-one map. DAC to DAS (Non-trivial) : DAC to DAS (Non-trivial) 2. Implementing the Viterbi algorithm in 3D is difficult. 3. Surface resampling is a much more difficult task than curve resampling. 4. Solving Bayesian Estimator using DAC’s Approach is computationally demanding for 3D problems DAS : DAS Step 1 : Measurement (IQRS not Viterbi search) Step 2: Generation of non-stationary prior Step 3: Linear Bayesian estimation (modified conjugate gradient) DAS : DAS Step1: Measurement (IQRS) : Step1: Measurement (IQRS) Step1: Measurement (IQRS) : Step1: Measurement (IQRS) Resampling : Resampling Resampling : Resampling DEMO_DAS_with_witout_sampling1.gif Bayesian Estimation : Bayesian Estimation Results : Results DEMO_DAS_VFC1.gif Results : Results DEMO_DAS_JB1.gif Slide 18: Thank You You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
mishra DAS 1 aSGuest47225 Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINT lite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 13 Category: Entertainment License: All Rights Reserved Like it (0) Dislike it (0) Added: June 04, 2010 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Decoupled Active Surface for Volumetric Image Segmentation : Decoupled Active Surface for Volumetric Image Segmentation A. Mishra, P. Fieguth, and D. Clausi Department of Systems Design Engineering, University of Waterloo Objective : Objective Background : Background Eulerian-Langragian frame work Bayesian framework Background (DAC) : Background (DAC) A. Mishra, P. Fieguth, D. Clausi, Decoupled Active Contour (DAC) for boundary identification, TPAMI, 2010 (preprint) Background (DAC) : Background (DAC) Background DAC : Background DAC Step 1 : Measurement (Viterbi search) Step 2: Generation of non-stationary prior Step 3: Linear Bayesian Estimation DAC to DAS (Non-trivial) : DAC to DAS (Non-trivial) 1. Representation: v(a, b) = [x(a, b), y(a, b), z(a, b)] is typically not a one-to-one map. DAC to DAS (Non-trivial) : DAC to DAS (Non-trivial) 2. Implementing the Viterbi algorithm in 3D is difficult. 3. Surface resampling is a much more difficult task than curve resampling. 4. Solving Bayesian Estimator using DAC’s Approach is computationally demanding for 3D problems DAS : DAS Step 1 : Measurement (IQRS not Viterbi search) Step 2: Generation of non-stationary prior Step 3: Linear Bayesian estimation (modified conjugate gradient) DAS : DAS Step1: Measurement (IQRS) : Step1: Measurement (IQRS) Step1: Measurement (IQRS) : Step1: Measurement (IQRS) Resampling : Resampling Resampling : Resampling DEMO_DAS_with_witout_sampling1.gif Bayesian Estimation : Bayesian Estimation Results : Results DEMO_DAS_VFC1.gif Results : Results DEMO_DAS_JB1.gif Slide 18: Thank You