An Industrial-Strength Pipeline for Recognizing Replacement Parts
Image classification and computer vision for search are rapidly emerging in today's technology and consumer markets. Partpic focuses on image search for replacement parts, and we present our industrial pipeline for such, with applications 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 parts, identification of customized specifications of parts (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 parts.
Dr. Nashlie H. Sephus specializes in writing deep learning and visual recognition algorithms as CTO at Partpic (Atlanta, Georgia). In 2014, she graduated with a PhD in Electrical and Computer Engineering at the Georgia Institute of Technology where her thesis topic was data mining/machine learning in digital signals using modulation spectral features. Nashlie then worked in New York City as an Associate at Exponent, an engineering consulting firm. She has prior experience working with companies such as GE, Delphi, and IBM. Nashlie graduated from undergrad at Mississippi State University and hails from Jackson, Mississippi.