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Grant: Discovery Grants Program - Individual

Memorial University of Newfoundland

$15,000.00

Computer vision hardware and image analysis tools for automated inspection in the food processing industry

FY 2009-10

Summary
The growing demand for fast, accurate and reliable quality inspection tools for the food processing industry has been driven by a number of factors; more stringent government safety standards, rising labor costs, and increased consumer expectations for higher quality food products. In many food processing applications, human inspectors are simply too subjective and unreliable given the time allotted to perform the inspection task and the tedious nature of the work. For these reasons, computer vision is proving to be a rapid, objective and cost effective alternative to human inspection for a growing number of food products. However, significant technological challenges remain particularly with respect to the inspection of "natural foods" such as fish and poultry. The proposed research program addresses these challenges though the development of both computer vision hardware and image analysis tools specifically for the automated inspection of natural foods. The ultimate goal is the development of a rapid, objective, robust and cost effective automated inspection system that improves food quality and consumer safety. The proposed research program is based on the hypothesis that no single detection technology will ever adequately satisfy the requirements of the food processing industry. A more promising approach is to base the inspection on multiple images acquired using various regions of the EM spectrum (visible, near-infrared, ultraviolet, X-ray) combined with different illumination strategies (reflection, trans-illumination, fluorescence). This approach (i.e., real-time multispectral image fusion) can potentially provide the basis for an accurate and robust automated food inspection system. In order to achieve real-time performance, research will focus on implementing high level image processing functions in hardware with the long-term goal of developing an "intelligent" camera for automated inspection that incorporates an entire vision system within a single compact enclosure. The development of such advanced technology for food inspection is critical to Canada both for addressing increasingly sophisticated consumer demands and ensuring food safety and competitiveness in an increasingly global marketplace.
Program
Discovery Grants Program - Individual
Location
Newfoundland and Labrador, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
138061-2007
Name nom
Krouglicof, Nicholas
Department département
Engineering and Applied Science, Faculty of
Organization id
89
Institution établissement
Memorial University of Newfoundland
Province en
Newfoundland and Labrador
Province fr
Terre-Neuve-et-Labrador
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2009
Competition year année de concours
2007
Award amount
15000
Program id
RGPIN
Program nane en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
335
Committee name en
Electromag. & Electr. Systems (H)
Committee name fr
Électromagn. et syst. électr. (H)
Area of application code
515
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Instrumentation technology
Area of application fr
Technologie de l'instrumentation
Research subject code
2603
Research subject group en
Robotics
Research subject group fr
Robotique
Research subject en
Computer vision
Research subject fr
Vision artificielle
Partie
3
Nb partie
5
Application title
Computer vision hardware and image analysis tools for automated inspection in the food processing industry
Keyword
NA
Application summary
The growing demand for fast, accurate and reliable quality inspection tools for the food processing industry has been driven by a number of factors; more stringent government safety standards, rising labor costs, and increased consumer expectations for higher quality food products. In many food processing applications, human inspectors are simply too subjective and unreliable given the time allotted to perform the inspection task and the tedious nature of the work. For these reasons, computer vision is proving to be a rapid, objective and cost effective alternative to human inspection for a growing number of food products. However, significant technological challenges remain particularly with respect to the inspection of "natural foods" such as fish and poultry. The proposed research program addresses these challenges though the development of both computer vision hardware and image analysis tools specifically for the automated inspection of natural foods. The ultimate goal is the development of a rapid, objective, robust and cost effective automated inspection system that improves food quality and consumer safety. The proposed research program is based on the hypothesis that no single detection technology will ever adequately satisfy the requirements of the food processing industry. A more promising approach is to base the inspection on multiple images acquired using various regions of the EM spectrum (visible, near-infrared, ultraviolet, X-ray) combined with different illumination strategies (reflection, trans-illumination, fluorescence). This approach (i.e., real-time multispectral image fusion) can potentially provide the basis for an accurate and robust automated food inspection system. In order to achieve real-time performance, research will focus on implementing high level image processing functions in hardware with the long-term goal of developing an "intelligent" camera for automated inspection that incorporates an entire vision system within a single compact enclosure. The development of such advanced technology for food inspection is critical to Canada both for addressing increasingly sophisticated consumer demands and ensuring food safety and competitiveness in an increasingly global marketplace.
Memorial University of Newfoundland - $15,000 Grant (… | Canada Spends