Fish inspection system using a parallel neural network chip and the image knowledge builder application.(Report): An article from: AI Magazine
Book Details
Author(s)Anne Menendez, Guy Paillet
ISBN / ASINB002SSO120
ISBN-13978B002SSO123
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is an article from AI Magazine, published by American Association for Artificial Intelligence on March 22, 2008. The length of the article is 3842 words. The page length shown above is based on a typical 300-word page. The article is delivered in HTML format and is available immediately after purchase. You can view it with any web browser.
From the author: A generic image learning system, CogniSight, is being used for the inspection of fishes before filleting offshore. More than 30 systems have been deployed on seven fishing vessels in Norway and Iceland over the past three years. Each CogniSight system uses four neural network chips (a total of 312 neurons) based on a natively parallel, hard-wired architecture that performs real-time learning and nonlinear classification (RBF). These systems are trained by the ship crew using Image Knowledge Builder, a "show and tell" interface that facilitates easy training and validation. Fishers can reinforce the learning anytime when needed. The use of CogniSight has significantly reduced the number of crew members needed on the boats (by up to six persons), and the time at sea has been shortened by 15 percent. The fast and high return of investment (ROI) to the fishing fleet has significantly increased the market share of Pisces Industries, the company integrating CogniSight systems to its filleting machines.
Citation Details
Title: Fish inspection system using a parallel neural network chip and the image knowledge builder application.(Report)
Author: Anne Menendez
Publication:AI Magazine (Magazine/Journal)
Date: March 22, 2008
Publisher: American Association for Artificial Intelligence
Volume: 29 Issue: 1 Page: 21(8)
Article Type: Report
Distributed by Gale, a part of Cengage Learning
From the author: A generic image learning system, CogniSight, is being used for the inspection of fishes before filleting offshore. More than 30 systems have been deployed on seven fishing vessels in Norway and Iceland over the past three years. Each CogniSight system uses four neural network chips (a total of 312 neurons) based on a natively parallel, hard-wired architecture that performs real-time learning and nonlinear classification (RBF). These systems are trained by the ship crew using Image Knowledge Builder, a "show and tell" interface that facilitates easy training and validation. Fishers can reinforce the learning anytime when needed. The use of CogniSight has significantly reduced the number of crew members needed on the boats (by up to six persons), and the time at sea has been shortened by 15 percent. The fast and high return of investment (ROI) to the fishing fleet has significantly increased the market share of Pisces Industries, the company integrating CogniSight systems to its filleting machines.
Citation Details
Title: Fish inspection system using a parallel neural network chip and the image knowledge builder application.(Report)
Author: Anne Menendez
Publication:AI Magazine (Magazine/Journal)
Date: March 22, 2008
Publisher: American Association for Artificial Intelligence
Volume: 29 Issue: 1 Page: 21(8)
Article Type: Report
Distributed by Gale, a part of Cengage Learning
