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ADAPTIVE IMAGE ENCODING AND CLASSIFICATION USING NEURAL NETWORKS

Award Information

Agency:
National Aeronautics and Space Administration
Branch:
N/A
Award ID:
10502
Program Year/Program:
1989 / SBIR
Agency Tracking Number:
10502
Solicitation Year:
N/A
Solicitation Topic Code:
N/A
Solicitation Number:
N/A
Small Business Information
Netrologic, Inc.
5080 Shorehame Pl, STE 201 San Diego, CA 92122
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Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
 
Phase 1
Fiscal Year: 1989
Title: ADAPTIVE IMAGE ENCODING AND CLASSIFICATION USING NEURAL NETWORKS
Agency: NASA
Contract: N/A
Award Amount: $49,629.00
 

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.

Principal Investigator:

Richard S Cigledy
0

Business Contact:

Small Business Information at Submission:

Netrologic Inc
4241 Jutland Dr San Diego, CA 92117

EIN/Tax ID:
DUNS: N/A
Number of Employees: N/A
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No