DSP-FPGA Based Image Processing System

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6 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

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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