Biologically Inspired Machine Vision Steven F. Barrett & Cameron H.G. Wright/University of Wyoming ACES 6.304

Contact Name: 
Jenna Whitney
Date: 
Feb 9, 2007 10:00am - 10:00pm

Talk Announcement

Speaker/Affiliation: Ste

ven F. Barrett Ph.D. P.E. Electrical and Computer Engineering Cameron H.

G. Wright Ph.D. P.E. Electrical and Computer Engineering Department of E

lectrical and Computer Engineering
University of Wyoming

Date/Tim

e: Friday February 9 2007 10:00 a.m. - 11:00 a.m.

Location: AC

ES 6.304

Host: Dr. Chandrajit Bajaj

Talk Title: Biolog

ically Inspired Machine Vision

Talk Abstract:
The early vision sy

stem of insects as well as many higher level organisms exhibit interesting

phenomena and features such as analog
preprocessing parallel structure
and sub-pixel resolution. Early vision is defined as the vision processe

s that occur within the first few
cellular synapses beyond the photorece

ptor layer. These features allow for the rapid extraction of image primitiv

es: object edges
boundaries image segmentation and movement paramete

rs. This analog parallel approach to vision provides advantages over curr

ent
digital-based imaging system. The same type of object information c

an be extracted with a digital-based system; however extraction
usuall

y requires multiple passes of image processing techniques that must be exha

ustively applied pixel-by-pixel to an image. We
propose a new approach

to the challenge of vision sensor development which takes its inspiration f

rom the obvious success of biological
vision systems. This project will
use a similar evolutionary system-level development that has resulted in

robust adaptable vision for so
many biological organisms. In this biol

ogically-based systems approach the sensor (the eye) and the computational
subsystem (the
visual cortex) will be developed together. The sensor d

esign and the computational algorithm design will be made to evolve togethe

r as
a synergistic mutually optimized pair; we believe this will grea

tly increase the probability that successful computer vision will be achiev

ed
for a wide variety of medical commercial and military applications

. This project will advance the state of science in several ways. First greater understanding of biological vision will be a benefit. The preproc

essing in retinal neural layers and the final processing in the visual

cortex is only partially understood today. By creating analog circuitry th

at accomplishes some of the preprocessing and computational
algorithms

that implement the final processing more complete knowledge of biological

vision will be obtained. Second a more capable
and robust computer vis

ion system suitable for intelligent navigation to be achievable for a wide

variety of mobile applications such as
autonomous wheelchairs and robot
movement in hazardous areas is expected. Furthermore we believe this res

earch will provide the foundation for the development of a vision prostheti

c system.

Steven F. Barrett received the BS Electronic Engineering T

echnology from the University of Nebraska at Omaha in 1979 the M.E.E.E. fr

om the University of Idaho at Moscow in 1986 and the Ph.D. from The Univer

sity of Texas at Austin in 1993. He was formally an active duty faculty me

mber with the United States Air Force Academy Colorado and is now an Assoc

iate Professor of Electrical and Computer Engineering University of Wyomin

g. He is a member of IEEE (senior) and Tau Beta Pi (chief faculty advisor)

. His research interests include digital and analog image processing comp

uter-assisted laser surgery and embedded controller systems. He is a regis

tered Professional Engineer in Wyoming and Colorado. He co-wrote with Dr.

Daniel Pack 68HC12 Microprocessor: Theory and Application Prentice-Hall 2

002 second edition in press for release in early 2007; Embedded Systems D

esign and Applications with the 68HC12 and HS12 Prentice-Hall 2005; and

Microcontroller Fundamentals for Engineers and Scientists Morgan-Claypool

Publishers 2006. In 2004 Barrett was named Wyoming Professor of the Year
by the Carnegie Foundation for the Advancement of Teaching. Email: steveb

%40uwyo.edu