Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019).

Traditional methods of tracking fashion trends in the clothing industry is a sophisticated and complex processx000D
that is time consuming and expensive for professionals throughout the clothing design process. However,x000D
advancements in the automatic identification of some of these trends can help fashion design companies tox000D
make this process more efficient and economical. Such automated identification would allow professionalx000D
designers to make more precise decisions regarding new designs faster and with greater confidence, allowingx000D
them more time to focus on producing creative ideas rather than analyzing current or historical clothing trendsx000D
manually. Gertex Hosiery Inc., the industry partner for this proposed applied research project, aims tox000D
implement an Artificial Intelligence (AI) solution for recognizing patterns in recent fashion trends (colour, cut,x000D
etc.).x000D
The Sheridan College research team proposes to design, implement and test an algorithm to recognizex000D
these patterns to assist the Gertex research and development (R&D) team in their continuous analysis of recentx000D
trends. The proposed research will investigate the performance of available pattern recognition algorithms,x000D
such as neural networks and support vector machines, in identifying patterns related to clothing trends. Thisx000D
investigation will help the research team to identify the unique characteristics of clothing pattern recognition,x000D
which in turn will provide design guidelines for developing an appropriate algorithm. This algorithm could be ax000D
customized implementation of one of the available pattern recognition algorithms or could be a combination ofx000D
multiple pattern recognition algorithms.x000D
The applied research to be undertaken during this project aims to create a useful and novel software toolx000D
that will enable Gertex's R&D team to identify specific buying trends automatically, thereby increasingx000D
productivity. The proposed project will also develop HQP by engaging students to address real-worldx000D
problems, which will increase their technical skills and their potential for contributing to future innovation.