DocumentCode
3756874
Title
An Industrial-Strength Pipeline for Recognizing Fasteners
Author
Nashlie Sephus;Sravan Bhagavatula;Palash Shastri;Erica Gabriel
Author_Institution
Partpic Inc., Atlanta, GA, USA
fYear
2015
Firstpage
781
Lastpage
786
Abstract
Image classification and computer vision for search are rapidly emerging in today´s technology and consumer markets. Specifically, startup companies have leveraged state-of-the-art image search capabilities in automating recognition of logos and titles, pop-up advertisements based on video content, and recommendations of products in the fashion industry. Partpic focuses on image search for replacement parts, and we present our industrial pipeline for such, with application to fasteners. We discuss how we have aimed to overcome issues such as acquiring enough training data, training and classification of many different types of fasteners, identification of customized specifications of fasteners (such as finish type, dimensions, etc.), establishing constraints for the user to take an good-enough image, and scalability of many pieces of data associated with thousands of fasteners.
Keywords
"Fasteners","Training","Databases","Imaging","Computer vision","Image segmentation","Image recognition"
Publisher
ieee
Conference_Titel
Machine Learning and Applications (ICMLA), 2015 IEEE 14th International Conference on
Type
conf
DOI
10.1109/ICMLA.2015.191
Filename
7424417
Link To Document