DSP-FPGA Based Image Processing System Checkpoint Presentation

1. DSP-FPGA Based Image Processing SystemCheckpoint Presentation Jessica Baxter  Sam Clanton Simon Fung-Kee-Fung Almaaz Karachi  Doug Keen Computer Integrated Surgery II April.19.2001

2. Plan of Action • Project Description and Deliverables • Implementation Overview • Progress to Date • Problems Encountered • Dependencies

3. Project Overview • Objective: To develop a robust image processing system using adaptive image segmentation, taking advantage of a DSP and FPGA hardware implementation to increase speed. • Deliverables: • Minimum: Adaptive Image Segmentation Software • Expected: Software Implemented in Hardware, Handling of Static Images • Maximum: Real-time Handling of Live Input

4. Plan of Action • Project Description and Deliverables • Implementation Overview • Progress to Date • Problems Encountered • Dependencies

5. Adaptive Image Segmentation System: Software Side • Input image • Compute image statistics. • Segment the image using initial parameters. • Compute the segmentation quality measures • WHILE not <stop conditions> DO • Select individuals using the reproduction operator • Generate new population using the crossover and mutation operators • Segment the image using new parameters • Compute the segmentation quality measures END • Update the knowledge base using the new knowledge structure Figure: Bhanu, Lee

6. Hardware Assignment • The DSPs will serve as the main processor and the FPGAs will provide support as co-processors.

7. Functional Break-Up: • DSP: • Initiation of Genetic Algorithm • Optimization • Join – calls vector graphic file to align segmented pieces FPGA: • Image Acquisition • Basic Image Processing (ex. Brightness) • Image Analysis – choosing and calculating statistical parameters • Segmentation – must also create a vector graphic file for the segmented data • Evaluation of Metrics of Population Fitness • CRT: • Output (including values of statistical evaluation parameters)

8. Back to the Plan • Project Description and Deliverables • Implementation Overview • Progress to Date • Problems Encountered • Dependencies

9. Key Dates • March 5th - Topical research should be completed and we should have a hashed out algorithm approved by Dr. Bey  • March 12th – Development Platforms determined, and development framework in place. • March 19th – CHANGE OF PROJECT OBJECTIVE – Combining of DSP and FPGA  implementations • April 2nd – Assign programming responsibilities to each individual and hash out new algorithm • April 9th – Troubleshoot the programming outline. • April 16th – Outline of code, Interfaces between functions • April 23rd – Integrate program components, test, and debug • April 25th – Drive to hardware and test on static images • April 27th – Refined

10. In- From Preprocess Image Struct Height Width Raw Data Output- To GA Array Statistics Image Analysis: The Ins and Outs

11. Mean Variance Centroid Skewness Energy Entropy Kurtosis Statistics

12. How? • N(v) is histogram • P(v) = N(v)/S • S = Image Size

13. Analysis Problems MATLAB Test Data Status 95% done To do: More testing Further Improvements More Parameters Even More Parameters Progress Status for Image Analysis

14. Evaluation • Measure overall quality of image segmentation • Compare edginess of foreground with edginess of background

15. Best Image Processing • Optimization problem • “Twiddling Knobs” Approach • Genetic Algorithm Approach Figure: Bhanu, Lee

16. GA method for Image Segmentation Figure: Bhanu, Lee

17. Flow of Genetic Adaptation Cycle • Cycle: • Segmentation • Evaluation • Reproduction • Recombination • Cycle continues until acceptable segmentation results are achieved • Long term pop. Is then modified in order to retain the information “learned” during the GA process Figure: Bhanu, Lee

18. Evolution of Segmentation System Figure: Bhanu, Lee

19. Background Extraction • Extract background from input image to isolate areas that contain useful information • Use algorithm presented in: Rodriguez, Arturo A., Mitchell, O. Robert. “Robust statistical method for background extraction in image segmentation” Stochastic and Neural Methods in Signal Processing, Image Processing, and Computer Vision. Vol. 1569, 1991 • Output to evaluation module

20. Background Extraction

21. Inputs Image Parameters necessary for background extraction algorithm Parameter indicating edge detection algorithm and that algorithm’s parameters Output Three-Layer Image Array 1st Layer: Original Image 2nd Layer: Background/Foreground Image (black=background, white=foreground) 3rd Layer: Edge image Background Extraction

22. Background Extraction: Progress/Difficulties • Many algorithmic details left out in research paper • Mostly implemented • Debugging phase

23. Once Again…The Plan • Project Description and Deliverables • Implementation Overview • Progress to Date • Problems Encountered • Dependencies

24. Problems Encountered • Change in Project Objective: Integration of both hardware types into one system rather creating 2 hardware systems • Conversion of data types from C  Matlab • Testing of quality measure statistics during image analysis • Determination of the Threshold for Stopping the GA (i.e. Fitness Evaluation) is rather subjective • Porting to Hardware (also, compatibility of code with hardware capabilities)

25. And Finally… • Project Description and Deliverables • Implementation Overview • Progress to Date • Problems Encountered • Dependencies

26. Dependencies Solved! • DSP and FPGA hardware obtained from TI and Xilinx, respectively • Xilinx software obtained to drive code down to hardware level Still Waiting On… • Image Capture Device - Important for reaching the maximum goal of real-time visual processing • Assembly of hardware components

27. fin