MSc Thesis Proposal Announcement of Roisul Islam Rumi:"An End-to-End Hybrid Approach to Automatic and Semi-Automatic Image Annotation"

 

Date: Monday, April 3rd, 2023

Time:  10:30am – 12:00pm

Location: Essex Hall, Room 122

Reminders: 1. Two-part attendance mandatory (sign-in sheet, QR Code) 2. Arrive 5-10 minutes prior to event starting - LATECOMERS WILL NOT BE ADMITTED. Note that due to demand, if the room has reached capacity, even if you are "early" admission is not guaranteed. 3. Please be respectful of the presenter by NOT knocking on the door for admittance once the door has been closed whether the presentation has begun or not (If the room is at capacity, overflow is not permitted (ie. sitting on floors) as this is a violation of the Fire Safety code). 4. Be respectful of the decision of the advisor/host of the event if you are not given admittance. The School of Computer Science has numerous events occurring soon.

 

 

This paper presents a new end-to-end hybrid machine learning (ML) and deep learning (DL) approach for automatic image annotation (AIA) and semi-automatic image annotation (SAIA) in industrial assembly line setups. Image annotation refers to adding descriptive labels or tags to an image to provide information about the objects and features present in the image. On a high level, the proposed system uses the following steps to annotate images. The first step involves using an ML algorithm, Haar cascade, to split an image into smaller regions of interest (ROI) based on the object of interest, in our case, the connectors of a car’s wire harness. The second step involves using a DL model, specifically a convolutional neural network (CNN) ResNet-50, to classify the ROI images. Our proposed system can do this work both automatically and semiautomatically. In AIA, the users will only provide an unannotated image for annotation. In SAIA, the users can specify which connectors they want to annotate by providing only the class label of the connector(s). This work stands out as unique as no other work was set up in the same industrial setting as ours, producing outstanding results surpassing other state-of-the-art image annotation models. The custom hybrid ML and DL framework, as well as building a unique custom dataset related to the automobile industry assembly line, are significant contributions of this work.

 

Keywords: Image Annotation, Machine Learning, Deep Learning, Computer Vision.

 

 

Internal Reader: Dr. Boubakeur Boufama

External Reader: Dr. Narayan Kar   

Advisor(s): Dr. Dan Wu, Dr. Ziad Kobti