1 / 27

DSP-FPGA Based Image Processing System Checkpoint Presentation

DSP-FPGA Based Image Processing System Checkpoint Presentation. Jessica Baxter  Sam Clanton Simon Fung-Kee-Fung Almaaz Karachi  Doug Keen. Computer Integrated Surgery II April.19.2001. Plan of Action. Project Description and Deliverables Implementation Overview Progress to Date

hbeverly
Download Presentation

DSP-FPGA Based Image Processing System Checkpoint Presentation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related