Date of Award
January 2016
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Graphics Technology
First Advisor
Bedrich Benes
Committee Member 1
Esteban Fernandez-juricic
Committee Member 2
Tim McGraw
Committee Member 3
Innfarn Yoo
Abstract
Avian retinas contain special light filtering cones called photoreceptors. These photoreceptors help filter out specific wavelengths of light, giving birds a good range of distinction between colors. There are five distinct types of photoreceptors: red, yellow, transparent, colorless and principle. A specific photoreceptor can be identified by an organelle called an oil droplet. Dectecting and classifying the oil droplets is currently done by hand which can be a time consuming process. Using computer vision detecting and classifying the photoreceptors can be done automatically. The recent introduction of deep learning in computer vision has revolutionized automatic classification, producing classification results identical to what a human could do. Using deep learning the human element can be eliminated from oil droplet detection and classification. It can take days for a human to process and entire retina, but using deep learning a computer can perform the same take in a matter of minutes.
Recommended Citation
Collier, Edward Durham, "Avian Retinal Photoreceptor Detection and Classification using Convolutional Neural Networks" (2016). Open Access Theses. 1187.
https://docs.lib.purdue.edu/open_access_theses/1187