1. COMPUTER ASSISTED MINIMAL INVASIVE SURGERY TOWARDS GUIDED MOTOR CONTROL Vinay B Gavirangaswamy

2. Canny edge detection algorithm Gaussian Convolution to smooth the image Sobel Filtering to find gradient magnitude and gradient direction Non-maximum suppression Hysteresis and connected components analysis

3. Output OriginalSingle Threaded

4. Output (contd.) OriginalMulti-Threaded (OpenMP)

5. Output (contd.) OriginalMulti-Threaded (GPU-CUDA)

6. Performance Analysis on OpenMP

7. Performance Analysis on OpenMP (contd.) Speedup of Canny AlgorithmEfficiency of Canny Algorithm

8. CS6260 Project Implementation Canny Edge Algorithm Performance on CUDA

9. Canny Edge Detection Performance on CUDA With Different Block Size 128x128256x256512x5121024x10242048x2048 110000400001400005300002150000 27000060000800007000090000 4700005000060000 80000 860000 70000 1660000 500006000070000 325000060000 80000 RuntimesRuntimes serial vs. parallel

10. Canny Edge Detection Performance on CUDA With Different Block Size (Contd.) 128x128256x256512x5121024x10242048x2048 111111 2 0.142857 14 0.666666 671.757.57142857 23.888888 9 4 0.142857 140.8 2.333333 338.8333333326.875 8 0.166666 67 0.666666 67 2.333333 338.83333333 30.714285 7 16 0.166666 67 0.666666 672.88.83333333 30.714285 7 320.2 0.666666 67 2.333333 338.8333333326.875 SpeedupSpeedup serial vs. parallel

11. Canny Edge Detection Performance on CUDA With Different Block Size (Contd.) 128x128256x256512x5121024x10242048x2048 111111 2 0.071428 57 0.333333 330.8753.78571429 11.944444 4 4 0.035714 290.2 0.583333 332.208333336.71875 8 0.020833 33 0.083333 33 0.291666 671.10416667 3.8392857 1 16 0.010416 67 0.041666 670.1750.55208333 1.9196428 6 320.00625 0.020833 33 0.072916 670.27604167 0.8398437 5 EfficiencyEfficiency serial vs. parallel

12. Canny Edge Detection Performance on CUDA With Different #Threads 128x128256x256512x5121024x10242048x2048 110000040000014000005300000215000000 24300001750000701000028030000112540000 421000088000035000001411000056510000 81100004300001760000709000028280000 1650000220000880000353000014140000 322000011000045000017800007120000 RuntimesRuntimes serial vs. parallel

13. Canny Edge Detection Performance on CUDA With Different #Threads (Contd.) 128x128256x256512x5121024x10242048x2048 111111 2 0.232558 14 0.228571 43 0.199714 690.18908313 1.9104318 5 4 0.476190 48 0.454545 450.40.37562013 3.8046363 5 8 0.909090 91 0.930232 56 0.795454 550.74753173 7.6025459 7 162 1.818181 82 1.590909 091.50141643 15.205091 9 325 3.636363 64 3.111111 112.97752809 30.196629 2 SpeedupSpeedup serial vs. parallel

14. Canny Edge Detection Performance on CUDA With Different #Threads (Contd.) 128x128256x256512x5121024x10242048x2048 111111 2 0.116279 07 0.114285 71 0.099857 350.09454156 0.9552159 2 4 0.119047 62 0.113636 360.10.09390503 0.9511590 9 8 0.113636 36 0.116279 07 0.099431 820.09344147 0.9503182 5 160.125 0.113636 36 0.099431 820.09383853 0.9503182 5 320.15625 0.113636 36 0.097222 220.09304775 0.9436446 6 EfficiencyEfficiency serial vs. parallel

15. Markov Chain Weather Model

16. Simple Markov Model of Weather

17. Prediction Based on State Transition Probability  If we want to know probability of the sequence SUNNY SUNNY SUNNY SUNNY SUNNY  Take initial probablity of SUNNY day i.e. on a any given day probability that it will be SUNNY is 0.30  And for use to get another SUNNY day after a SUNNY day is 0.42  So, by using Markov Chain we can say prbability of getting 5 consecutive SUNNY day is

18. Challenges Faced During OpenMP Missing Edges Medical images consist of nerves and arteries which should be treaded as an edge however their gradient magnitude varies relative to region in image. Solution : Adaptive Thresholding False Edges in Parallel Implementation

19. Summary  Canny and Markov Chain Model is a simple and efficient way to perform edge detection however canny performs poorly with images taken during laparoscopy (good to get started)  Future work  Contribute improvements to MIS learning methodology.

20. REFERENCES  Image Convolution with CUDA – Victor Podlozhnyuk, sdkfeedback@nvidia.com sdkfeedback@nvidia.com  Performance Evaluation of Feature Extraction Algorithm on GPGPU – Namdev Sawant Dept. of Computer Science and Engg. Dinesh Kulkarni Dept. of Information Technology, 2011 International Conference on Communication Systems and Network Technologies  Canny Edge Detection on NVIDIA CUDA - Yuancheng “Mike” Luo and Ramani Duraiswami, Perceptual Interfaces and Reality Laboratory, Computer Science & UMIACS, University of Maryland, College Park  Cuda-grayscale – Karlphil, karlphil...@gmail.comkarlphil...@gmail.com  Rich, E.A. 2007. Automata, computability and complexity: Theory and applications, Upper Saddle River, NJ: Prentice Hall Rich, E.A.  Special thanks to Jason and Vasilije! Special thanks to Jason and Vasilije!