Image processing techniques for acoustic image.

86 Pages·0.66 MB·en_US

by  Murphy, Brian P.;Cristi, Roberto.

:
NAVAL POSTGRADUATE SCHOOL
Monterey
California
,
THESIS
IMAGE PROCESSING TECHNIQUES
FOR ACOUSTIC IMAGES
by
Brian P. Murphy
June 1991
Thesis Advisor Roberto Cristi
Approved for public release; distribution is unlimited
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T
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11 TITLE (Include Security Classification)
IMAGE PROCESSING TECHNIQUES FOR ACOUSTIC IMAGES
12 PERSONAL AUTHOR(S/
MURPHY^ Brian P.
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Master's Thesis CPOV TO 1991 June 77
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author and do not reflect the official policy or position of the Depart-
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17 cosati codes 18 SUBJECT TEPMS. 'Continue on reverse if necessary and identify by block number)
FIELD GROUP SUB-GROUP image processing, segmentation, edge detection,
Kalman filtering, median filtering
19 ABSTRACT (Continue on reverse if necessary and identify by block number)
The primary goal of this research is to test the effectiveness of
various image processing techniques applied to acoustic images generated
in MATLAB. The simulated acoustic images have the same characteristics
as those generated by a computer model of a high resolution imaging
sonar. Edge Detection and Segmentation are the two image processing
techniques discussed in this study. The two methods tested are a modi-
fied version of the Kalman filtering and median filtering.
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CRISTI, Roberto 408-646-2223 EC/Cx
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Image Processing Techniques for Acoustic Images
by
Brian P. Murphy
Lieutenant, United States Navy
B.S., United States Naval Academy, 1984
Submitted in partial fulfillment of the
required of degree for
MASTER OF SCIENCE IN ELECTRICAL ENGINEERING
from the
NAVAL POSTGRADUATE SCHOOL
June 1991
A
11
ABSTRACT
The primary goal of this thesis research is to test the
effectiveness of various image processing techniques applied
to acoustic images generated in MATLAB. The simulated
acoustic images have the same characteristics as those
generated by a computer model of a high resolution imaging
sonar. Edge Detection and Segmentation are the two image
processing techniques discussed in this study. The two
methods tested are a modified version of Kalman filtering and
median filtering.
111
. . . ... .
TABLE OF CONTENTS
I INTRODUCTION 1
A. MOTIVATION FOR STUDY 1
II IMAGING BACKGROUND 3
A BACKGROUND 3
B. ACOUSTIC IMAGES 4
C. SIMULATED ACOUSTIC IMAGES 6
III IMAGE PROCESSING TECHNIQUES 14
A. GENERAL 14
B EDGE DETECTION 14
C. EDGE DETECTION BY DIFFERENTIATION 15
D. A KALMAN FILTER BASED EDGE DETECTION 18
1 First Order Case 19
.
2 Second Order Case 19
.
a Edge Detection 21
b. Segementation 23
E. MEDIAN FILTERING 24
IV. APPLICATION OF IMAGE PROCESSING TECHNIQUES 2
A GENERAL 25
B. SIMULATED ACOUSTIC IMAGE WITH GAUSSIAN
BACKGROUND NOISE 25
1 Edge Detection 25
.
2 Segmentation 32
.
3 Median Filtering 36
.
B. SIMULATED ACOUSTIC IMAGE WITH RAYLEIGH
BACKGROUND NOISE 42
1. Edge Detection 42
iv