Honour Thesis Defence - Yonghong Chen
HONOURS THESIS DEFENCE
Jodrey School of Computer Science
ESTIMAGE: A MOBILE APPLICATION FOR IMAGE TO OBJECT COUNT ESTIMATION
YONG HONG CHEN
Tuesday, February 9, 2016 at 10:30 AM
Carnegie Hall – Room 410
Abstract: A major problem in the agricultural field is accurately estimating the yield of produce. Typically, farmers must wait to measure their crops after harvest using manual mechanical equipment. There is value in having better methods of yield estimation based on data that can be captured with inexpensive technology in the field such as a smart phone. We develop a smartphone application for the Android platform with access to a cloud-based machine learning (ML) service that can estimate the amount of crop on a bush or tree from an image. Relevant background on the Android platform and machine learning predictive models is reviewed. The development of an image-to-object-count estimation system called Estimage is presented. The Estimage system consists of an Android client application for user interaction, a PHP server application for requests handling, an Octave program for image normalization, and an open source ML software package called ilastik that applies a predictive model to an image. The functionality of the mobile client application is tested and the system satisfies all of the functional requirements and most of the non-functional requirements. The system is tested on the images of coins on a table, logs stacked in a pile, and blueberries on the bush. We show that the system is capable of accurately estimating the count of objects in the images that have objects of the same (size, color, and shape) as that used to train the model. The system is good at distinguishing object pixels from the image background pixels provided the background is consistent with that used to train the model.
Supervisor: Dr. Danny Silver
Second Reader: Dr. Jim Diamond
EVERYONE WELCOME