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DSP-FPGA Based Image Processing System Final Presentation. Jessica Baxter  Sam Clanton Simon Fung-Kee-Fung Almaaz Karachi  Doug Keen. Computer Integrated Surgery II May 3, 2001. Plan of Action. Project Description Implementation Overview Significance Results Future Directions.

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dsp fpga based image processing system final presentation

DSP-FPGA Based Image Processing SystemFinal Presentation

Jessica Baxter  Sam Clanton

Simon Fung-Kee-Fung

Almaaz Karachi  Doug Keen

Computer Integrated Surgery II

May 3, 2001

plan of action
Plan of Action
  • Project Description
  • Implementation Overview
  • Significance
  • Results
  • Future Directions
project overview
Project Overview
  • Objective: To develop a robust image processing system using adaptive edge detection, taking advantage of a DSP and FPGA hardware implementation to increase speed.
  • Deliverables
    • Minimum: Adaptive Edge Detection Software
    • Expected: Software Implemented in Hardware, Handling of Static Images
    • Maximum: Real-time Handling of Input
purpose
Purpose
  • Gain a better understanding of Genetic Algorithms for use in DSPs and FPGAs.
  • To develop a robust image processing system using adaptive edge detection, taking advantage of DSP and FPGA hardware
  • Edge Detection Optimization Software
  • Adaptive Edge Detection Software Implemented in Hardware, Handling of Static Image
  • Real-time Processing of Live Input
plan of action5
Plan of Action
  • Project Description
  • Implementation Overview
  • Significance
  • Results
  • Future Directions
ga method for adaptive image segmentation system software side
GA Method for 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

hardware assignment
Hardware Assignment
  • DSP’s serve as the main processor and FPGA’s provide support as co-processors.
  • The genetic algorithm (GA) is included in the DSP.
  • FPGA’s compute the image statistics and the segmentation of quality measure.
functional break up
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 – background extraction
  • Evaluation of Metrics of Population Fitness
  • CRT:
  • Output (including values of statistical evaluation parameters)
plan of action9
Plan of Action
  • Project Description
  • Implementation Overview
  • Significance
  • Results
  • Future Directions
significance
Significance
  • Leads to increases in
    • Reliability
    • Adaptability
    • Performance
  • Medical technology:
    • Demands:
      • High reliability and performance
    • Leads to
      • Development of failsafe, precise sensor systems for computer-integrated surgical applications
    • Retinal Applications
plan of action11
Plan of Action
  • Project Description
  • Implementation Overview
  • Significance
  • Results
  • Future Directions
demonstration

Demonstration

Genetic Algorithm

slide13

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
plan of action20
Plan of Action
  • Project Description
  • Implementation Overview
  • Significance
  • Results
  • Future Directions
work to date
Work to date
  • Developed a first draft of an edge detection optimization algorithm
  • Developed C and Matlab coding modules to be used for direct mapping into TI C67 DSP and Xilinx Virtex FPGA
future directions
Future Directions
  • Integrate with image capture device - Important for reaching the maximum goal of real-time visual processing
  • CRT: Output (including values of statistical evaluation parameters)
  • Integrate code into Xilinx and TI parts
  • Further develop ideas for potential collaboration with JHU Wilmer Eye Institute
acknowledgments
Acknowledgments
  • Dr. Charles Johnson-Bey
  • Co- Researchers – Morgan State Student - Nykia Jackson