dsp-fpga based image processing system final presentation jessica baxter sam clanton simon...
TRANSCRIPT
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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
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Plan of Action
• Project Description• Implementation Overview • Significance• Results• Future Directions
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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
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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
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Plan of Action
• Project Description• Implementation Overview • Significance• Results• Future Directions
![Page 6: DSP-FPGA Based Image Processing System Final Presentation Jessica Baxter Sam Clanton Simon Fung-Kee-Fung Almaaz Karachi Doug Keen Computer Integrated](https://reader035.vdocuments.mx/reader035/viewer/2022062715/56649db55503460f94aa5b52/html5/thumbnails/6.jpg)
GA Method for Adaptive Image Segmentation System: Software Side
1) Input image
2) Compute image statistics.
3) Segment the image using initial parameters.
4) Compute the segmentation quality measures
5) WHILE not <stop conditions> DO
a) Select individuals using the reproduction operator
b) Generate new population using the crossover and mutation operators
c) Segment the image using new parameters
d) Compute the segmentation quality measures
END
6) Update the knowledge base using the new knowledge structure
Figure: Bhanu, Lee
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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.
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Functional Break-Up:FPGA:
• Image Acquisition
• Basic Image Processing (ex. Brightness)
• Image Analysis – choosing and calculating statistical parameters
• Segmentation – background extraction
• Evaluation of Metrics of Population Fitness
DSP:
• Initiation of Genetic Algorithm
• Optimization
• Join – calls vector graphic file to align segmented pieces
CRT:
• Output (including values of statistical evaluation parameters)
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Plan of Action
• Project Description• Implementation Overview • Significance• Results • Future Directions
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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
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Plan of Action
• Project Description• Implementation Overview • Significance• Results • Future Directions
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Demonstration
Genetic Algorithm
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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
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Original Image
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Results
Image After Preprocessing
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Background Extraction Output
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Processed Output
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Extracted Image
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Another Example
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Plan of Action
• Project Description• Implementation Overview • Significance• Results• Future Directions
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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
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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
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Acknowledgments
• Dr. Charles Johnson-Bey
• Co- Researchers – Morgan State Student - Nykia Jackson
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Questions