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
•
Problems Encountered
•
Dependencies
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
Plan of Action
•
Project Description and Deliverables
•
Implementation Overview
•
Progress to Date
•
Problems Encountered
•
Dependencies
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
Hardware Assignment
•
The DSPs will serve as the main processor
and the FPGAs will provide support as co
-
processors.
Functional Break
-
Up:
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
DSP:
•
Initiation of Genetic Algorithm
•
Optimization
•
Join
–
calls vector graphic file to
align segmented pieces
CRT:
•
Output (including values of
statistical evaluation parameters)
Back to the Plan
•
Project Description and Deliverables
•
Implementation Overview
•
Progress to Date
•
Problems Encountered
•
Dependencies
Key Dates
•
March 5
th
-
Topical research should be completed and we
should have a hashed out algorithm approved by Dr. Bey
•
March 12
th
–
Development Platforms determined, and
development framework in place.
•
March 19
th
–
CHANGE OF PROJECT OBJECTIVE
–
Combining
of DSP and FPGA
implementations
•
April 2
nd
–
Assign programming responsibilities to each
individual and hash out new algorithm
•
April 9
th
–
Troubleshoot the programming outline.
•
April 16
th
–
Outline of code, Interfaces between functions
•
April 23
rd
–
Integrate program components, test, and debug
•
April 25
th
–
Drive to hardware and test on static images
•
April 27
th
–
Refined
Image Analysis:
The Ins and Outs
•
In
-
From Preprocess
–
Image Struct
•
Height
•
Width
•
Raw Data
•
Output
-
To GA
–
Array
•
Statistics
Statistics
•
Mean
•
Variance
•
Centroid
•
Skewness
•
Energy
•
Entropy
•
Kurtosis
•
N(v) is
histogram
•
P(v) = N(v)/S
•
S = Image
Size
How?
Progress Status for Image Analysis
•
Analysis Problems
–
MATLAB
–
Test Data
•
Status
–
95% done
–
To do: More testing
•
Further Improvements
–
More Parameters
–
Even More Parameters
Evaluation
•
Measure overall quality of image
segmentation
•
Compare edginess of foreground with
edginess of background
Fitness
E
E
E
E
E
E
otherwise
background
foreground
f
b
foreground
background
Best Image Processing
•
Optimization problem
•
“Twiddling Knobs” Approach
•
Genetic Algorithm Approach
Figure: Bhanu, Lee
GA method for Image
Segmentation
Figure: Bhanu, Lee
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
Evolution of Segmentation
System
Figure: Bhanu, Lee
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
Background Extraction
Background Extraction
Inputs
•
Image
•
Parameters necessary
for background
extraction algorithm
•
Parameter indicating
edge detection
algorithm and that
algorithm’s parameters
Output
•
Three
-
Layer Image
Array
–
1
st
Layer: Original Image
–
2
nd
Layer:
Background/Foreground
Image
(black=background,
white=foreground)
–
3
rd
Layer: Edge image
Background Extraction:
Progress/Difficulties
•
Many algorithmic details left out in research
paper
•
Mostly implemented
•
Debugging phase
Once Again…The Plan
•
Project Description and Deliverables
•
Implementation Overview
•
Progress to Date
•
Problems Encountered
•
Dependencies
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)
And Finally…
•
Project Description and Deliverables
•
Implementation Overview
•
Progress to Date
•
Problems Encountered
•
Dependencies
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
fin
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