ADAPTIVE IMAGE ENCODING AND CLASSIFICATION USING NEURAL NETWORKS

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: N/A
Agency Tracking Number: 10502
Amount: $49,629.00
Phase: Phase I
Program: SBIR
Awards Year: 1989
Solicitation Year: N/A
Solicitation Topic Code: N/A
Solicitation Number: N/A
Small Business Information
Netrologic Inc
4241 Jutland Dr, San Diego, CA, 92117
DUNS: N/A
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Richard S Cigledy
 () -
Business Contact
Phone: () -
Research Institution
N/A
Abstract
WE INTEND TO EXPLORE THE VIABILITY OF NEURAL NETWORKS FOR IMAGE COMPRESSION AND PATTERN CLASSIFICATION IN AN INTEGRATED SYSTEM. THIS IS A NEW APPROACH TO IMAGE COMPRESSION THAT HAS SEVERAL ADVANTAGES OVER STANDARD APPROACHES. SO FAR, NEURAL NETWORKS HAVE BEEN SHOWN TO BE COMPARABLE TO STANDARD TECHNIQUES (COTTRELL, MUNRO & ZIPSER,1987) FOR THE TASK OF IMAGE COMPRESSION. THEY SHOWED THAT A 3 LAYER BACK PROPAGATION NETWORK (RUMELHART, HINTON & WILLIAMS, 1986) WITH ESSENTIALLY NO TUNING COULD ACHIEVE LEVELS OF COMPRESSION ON THE ORDER OF 1 BIT PER PIXEL (BPP). WE INTEND TO EXTEND THAT WORK IN AN ATTEMPT TO ACHIEVE COMPRESSION RATES BELOW 1 BPP AND INVESTIGATE ITS USEFULNESS IN NASA APPLICATIONS. IF THE ALGORITHM CAN BE EMBEDDED IN HARDWARE, THE POTENTIAL ADVANTAGES FOR SPACE-BASED APPLICATIONS ARE: A COMPRESSION DEVICE THAT ADAPTS TO THE CURRENT ENVIRONMENTAL AND HARDWARE CONDITIONS AND OPERATES IN REAL TIME, LESS SENSITIVITY TO CHANNEL ERRORS, A RECONFIGURABLE PATTERN CLASSIFIER THAT CAN BE TRAINED IN SITU.

* information listed above is at the time of submission.

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