Grant: Applied Research and Development Grants - Level 1
Sheridan Institute of Technology and Advanced Learning
$25,000.00
Smart Pattern Detector for Clothing
EF 2017-18
Résumé
Traditional methods of tracking fashion trends in the clothing industry is a sophisticated and complex processthat is time consuming and expensive for professionals throughout the clothing design process. However,advancements in the automatic identification of some of these trends can help fashion design companies tomake this process more efficient and economical. Such automated identification would allow professionaldesigners to make more precise decisions regarding new designs faster and with greater confidence, allowingthem more time to focus on producing creative ideas rather than analyzing current or historical clothing trendsmanually. Gertex Hosiery Inc., the industry partner for this proposed applied research project, aims toimplement an Artificial Intelligence (AI) solution for recognizing patterns in recent fashion trends (colour, cut,etc.).The Sheridan College research team proposes to design, implement and test an algorithm to recognizethese patterns to assist the Gertex research and development (R&D) team in their continuous analysis of recenttrends. The proposed research will investigate the performance of available pattern recognition algorithms,such as neural networks and support vector machines, in identifying patterns related to clothing trends. Thisinvestigation will help the research team to identify the unique characteristics of clothing pattern recognition,which in turn will provide design guidelines for developing an appropriate algorithm. This algorithm could be acustomized implementation of one of the available pattern recognition algorithms or could be a combination ofmultiple pattern recognition algorithms.The applied research to be undertaken during this project aims to create a useful and novel software toolthat will enable Gertex's R&D team to identify specific buying trends automatically, thereby increasingproductivity. The proposed project will also develop HQP by engaging students to address real-worldproblems, which will increase their technical skills and their potential for contributing to future innovation.
Programme
Applied Research and Development Grants - Level 1
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
519826-2017
Name nom
England, Andrea
Department département
Head Office
Organization id
22156
Institution établissement
Sheridan Institute of Technology and Advanced Learning
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2017
Competition year année de concours
2017
Award amount
25000
Program id
CARD1
Program name en
Applied Research and Development Grants - Level 1
Program name fr
Subventions de recherche et développement appliquée - niveau 1
Group en
Research partnerships programs
Group fr
Programmes de partenariat
Committee code
1561
Committee name en
Applied Research and Development Grants
Committee name fr
Subventions de recherche et développement appliquée
Area of application code
517
Area of application group en
Manufacturing processes and products
Area of application group fr
Procédés et produits industriels
Area of application en
Consumer goods
Area of application fr
Produits de consommation
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Partie
1
Nb partie
1
Application title
Smart Pattern Detector for Clothing
Keyword
NSERC
Application summary
Traditional methods of tracking fashion trends in the clothing industry is a sophisticated and complex processthat is time consuming and expensive for professionals throughout the clothing design process. However,advancements in the automatic identification of some of these trends can help fashion design companies tomake this process more efficient and economical. Such automated identification would allow professionaldesigners to make more precise decisions regarding new designs faster and with greater confidence, allowingthem more time to focus on producing creative ideas rather than analyzing current or historical clothing trendsmanually. Gertex Hosiery Inc., the industry partner for this proposed applied research project, aims toimplement an Artificial Intelligence (AI) solution for recognizing patterns in recent fashion trends (colour, cut,etc.).The Sheridan College research team proposes to design, implement and test an algorithm to recognizethese patterns to assist the Gertex research and development (R&D) team in their continuous analysis of recenttrends. The proposed research will investigate the performance of available pattern recognition algorithms,such as neural networks and support vector machines, in identifying patterns related to clothing trends. Thisinvestigation will help the research team to identify the unique characteristics of clothing pattern recognition,which in turn will provide design guidelines for developing an appropriate algorithm. This algorithm could be acustomized implementation of one of the available pattern recognition algorithms or could be a combination ofmultiple pattern recognition algorithms.The applied research to be undertaken during this project aims to create a useful and novel software toolthat will enable Gertex's R&D team to identify specific buying trends automatically, thereby increasingproductivity. The proposed project will also develop HQP by engaging students to address real-worldproblems, which will increase their technical skills and their potential for contributing to future innovation.
