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Improve computation complexity of sobel edge detection using contract
anytime algorithmPresented By
Md Kamal Hossain Shajal - 12101073Md Asif Ibtehaz - 14341001
Md Assaduzzaman Ashique - 12301017SupervisorDr. Jia UddinAss professorBRAC UniversityDepartment of Computer Science & [email protected]
Presentation OutlineIntroductionProposed ModelExperimental SetupResult AnalysisConclusion
IntroductionWhat is Edge Detection
Definition Different Edge Detection Technique
Canny Edge Detection TechniqueSobel Edge Detection Technique
Why Edge Detection is Important Medical Image processing (Detect
the edge of lungs CT scan) Shape and object recognition Traffic Management Line detection from blurry image
Continue Replacing Sniko Technology
Input Image Output Image
Proposed Model
Edge detection (Sobel)
Gray Scale Conversion
Image Input
Output
Proposed Model
Conventional Implementation of Sobel in Java
Output for a 1920 x 1024 Input Image in Java
Introducing Sobel in Parallel Computing
Edge detection
(Sobel)
Gray Scale Conversion
Image InputCPU
GPU
OutputCPU
Proposed Model (Sobel in Parallel Computing)
Introducing Anytime Algorithm What is Anytime Algorithm Types of Anytime Algorithm
Contract Anytime Algorithm Interruptible Anytime Algorithm
Contract Anytime Algorithm
Input Quick Process
Half Process
Full process Output
Less
More
Low HighContract Anytime Algorithm
Sobel Edge Detection Technique using Contract Anytime Algorithm in Parallel Computing
Edge detection (Sobel) Contract
Anytime Algorithm for Sobel
Gray Scale Conversion
Image InputContract Anytime
Algorithm to convert in gray scale
Output
Proposed Model
CPU
GPU
CPU
Experimental Environment Software Specification
Visual Studio 2013 CUDA ToolKit 7.5 .NET C++ JAVA 7 Eclipse MARS Create A Graph (online tool) OpenGL
Hardware Specification AMD FX5320 CPU RAM 8 GB NVIDIA GTX 550 TI
GTX 550ti GPU Engine SpecsParameters Value
CUDA Cores 192
Graphics Clock (MHz) 900
Processor Clock (MHz) 1800
Texture Fill Rate (billion/sec) 28.8
Total amount of shared memory per block 49152 bytes
Maximum number of threads per block 1024
CUDA Driver Version / Runtime Version 7.5 / 7.5
TABLE GTX 550ti GPU Engine Specs
Result Analysis – Implementation using Java
Output for Input Image Higher than 1920 pixel
Sobel Edge Detection Implementation using Parallel Computing
Output of [Image 1] for block dimension 16.
Output of [Image 1] for block dimension 32.
Sample Image 1
Implementing Contact Anytime Algorithm in Sobel Edge Detection (16 block)
(a) Test Image 2 (b) Test 1(quick)
(c) Test 2(half)
(d) Test 3(full) Three contract-time process test for 16 block dimension
Test (b, c, and d)
Implementing Contact Anytime Algorithm in Sobel Edge Detection (32 block)
(a) Test Image 1 (b) Test 1(quick)
(c) Test 2(half)
(d) Test 3(full) Three contract-time process test for 32 block dimension
Test (b, c, and d)
Time Comparison between Java and Sobel Anytime Algorithm
Time Comparison of 1920 x 1024 Input Image
Time Comparison of 4096 x 4096 Input Image
Time Comparison (Sobel with Anytime 16 block and 32 block)
Process versus Time for 16 and 32 block dimension.
Overall Comparison
PROCESSCPU – GPU
TIME (MS)
THREADS PER
BLOCKBLOCK SIZE
JAVA
EXECUTION
TIME (MS)
SPEED
UP
(AGAI
NST
JAVA)
16 BLOCK
DIMENSION
QUICK 119.899
256 8192 480
4.003
X
HALF 127.809 3.7X
FULL 127.992 3.75X
32 BLOCK
DIMENSION
QUICK 138.227
1024 2048 480
3.47X
HALF 141.027 3.4X
FULL 141.601 3.38XResult comparison for an 1920 x 1024 Input Image
Conclusion Future Work
Real time using contract-based anytime algorithm
Interruptible anytime algorithm. Limitation
CUDA 7.0 Toolkit Visual Studio Extension Error Tesla Architecture
Appendix
Parallel Computing Anytime Algorithm Experiment
Parallel Computing
Threads per Warp 32
Warps per Multiprocessor 64
Threads per Multiprocessor 2048
Thread Blocks per Multiprocessor 16
Total # of 32-bit registers per Multiprocessor 65536
Register allocation unit size 256
Register allocation granularity warp
Registers per Thread 255
Shared Memory per Multiprocessor (bytes) 49152
Shared Memory Allocation unit size 256
Warp allocation granularity 4
Maximum Thread Block Size 1024
Source. NVIDIA Occupancy calculator
Source. NVIDIA Understanding parallel computing
Source. NVIDIA NVIDIA GPGPU
Anytime Algorithm
Figure 6 page 15
Result ← INITIALIZATION-STEP (Input(x, y)) REGISTER-RESULT (Result) x ← 0; y ← 0; While (x < h) { While (y < w) {
Output(x, y) ← Input(x, y);
y ← y + 2; } SIGNAL (TERMINATION) HALT } w ← w/2; h ← h/2;
