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AbstractThe mapping of habitats is the first step that is done in policies that target theenvironment, as well as in spatial planning and management. The biodiversityplans are always centered around habitats. Therefore, constant monitoring ofthese delicate species in terms of health, changes, and extinction is a must inbiodiversity plans. Human activities are constantly growing, resulting in theextinction of land and marine habitats. Land habitats are being destroyed using airpollution and the cutting of forests. At the same time, marine habitats are beingdestroyed due to acidification of ocean waters and waste materials from theindustries and pollution. The author has focused on aquatic habitats in thisdissertation, mainly coral reefs. An estimate of 27% of coral reef ecosystems havebeen destroyed, and a further 30% are at risk of being damaged in the comingyears. Coral reefs occupy 1% of the ocean floor, and yet they provide a home to30% of marine organisms. To analyze the health of these aquatic habitats, theyneed to be assessed through habitat mapping. Habitat mapping shows thegeographic distribution of different habitats within a particular area. Marinehabitats are typically mapped using camera imagery. The quality of underwaterimages suffers from the characteristics of the marine environment. This results inblurry images or containing particles that cover many parts of an image. Toovercome this, underwater image enhancement algorithms are used to preprocessimages beforehand. Now, there are many underwater image enhancementalgorithms that target different characteristics of the marine environment, butthere is no consensus among researchers about a single underwater technique thatcan be used for any marine dataset. In this dissertation, multiple experiments onvarious popular image enhancement techniques (seven) were conducted and usedto reach a decision about a single underwater approach for all datasets. Thedatasets include EILAT, EILAT2, RSMAS, and MLC08. Also, two state-of-the-artdeep convolutional neural networks for habitat mapping, i.e., DenseNet andMobileNet tested. Maximum results from the combination of Contrast LimitedAdaptive Histogram Equalization (CLAHE) achieved as underwater imageenhancement technique and DenseNet as deep convolutional network. / Not applicable

Positron emission tomography (PET) requires imaging times that last several minutes long. Therefore, when imaging areas that are prone to respiratory motion, blurring effects are often observed. This blurring can impair our ability to use these images for diagnostics purposes as well for treatment planning. While there are methods that are used to account for this effect, they often rely on adjustments to the imaging protocols in the form of longer scan times or subjecting the patient to higher doses of radiation. This dissertation explores an alternative approach that leverages state-of-the-art deep learning techniques to align the PET signal acquired at different points of the breathing motion. This method does not require adjustments to standard clinical protocols; and therefore, is more efficient and/or safer than the most widely adopted approach. To help validate this method, Monte Carlo (MC) simulations were conducted to emulate the PET imaging process, which represent the focus of our first experiment. The next experiment was the development and testing of our motion correction method. A clinical four-ring PET imaging system was modelled using GATE (v. 9.0). To validate the simulations, PET images were acquired of a cylindrical phantom, point source, and image quality phantom with the modeled system and the experimental procedures were also simulated. The simulations were compared against the measurements in terms of their count rates and sensitivity as well as their image uniformity, resolution, recovery coefficients, coefficients of variation, contrast, and background variability. When compared to the measured data, the number of true detections in the MC simulations was within 5%. The scatter fraction was found to be (31.1 ± 1.1)% and (29.8 ± 0.8)% in the measured and simulated scans, respectively. Analyzing the measured and simulated sinograms, the sensitivities were found to be 10.0 cps/kBq and 9.5 cps/kBq, respectively. The fraction of random coincidences were 19% in the measured data and 25% in the simulation. When calculating the image uniformity within the axial slices, the measured image exhibited a uniformity of (0.015 ± 0.005), while the simulated image had a uniformity of (0.029 ± 0.011). In the axial direction, the uniformity was measured to be (0.024 ± 0.006) and (0.040 ± 0.015) for the measured and simulated data, respectively. Comparing the image resolution, an average percentage difference of 2.9% was found between the measurements and simulations. The recovery coefficients calculated in both the measured and simulated images were found to be within the EARL ranges, except for that of the simulation of the smallest sphere. The coefficients of variation for the measured and simulated images were found to be 12% and 13%, respectively. Lastly, the background variability was consistent between the measurements and simulations, while the average percentage difference in the sphere contrasts was found to be 8.8%. The code used to run the GATE simulations and evaluate the described metrics has been made available (https://github.com/teaghan/PET_MonteCarlo). Next, to correct for breathing motion in PET imaging, an interpretable and unsupervised deep learning technique, FlowNet-PET, was constructed. The network was trained to predict the optical flow between two PET frames from different breathing amplitude ranges. As a result, the trained model groups different retrospectively-gated PET images together into a motion-corrected single bin, providing a final image with similar counting statistics as a non-gated image, but without the blurring effects that were initially observed. As a proof-of-concept, FlowNet-PET was applied to anthropomorphic digital phantom data, which provided the possibility to design robust metrics to quantify the corrections. When comparing the predicted optical flows to the ground truths, the median absolute error was found to be smaller than the pixel and slice widths, even for the phantom with a diaphragm movement of 21 mm. The improvements were illustrated by comparing against images without motion and computing the intersection over union (IoU) of the tumors as well as the enclosed activity and coefficient of variation (CoV) within the no-motion tumor volume before and after the corrections were applied. The average relative improvements provided by the network were 54%, 90%, and 76% for the IoU, total activity, and CoV, respectively. The results were then compared against the conventional retrospective phase binning approach. FlowNet-PET achieved similar results as retrospective binning, but only required one sixth of the scan duration. The code and data used for training and analysis has been made publicly available (https://github.com/teaghan/FlowNet_PET). The encouraging results provided by our motion correction method present the opportunity for many possible future applications. For instance, this method can be transferred to clinical patient PET images or applied to alternative imaging modalities that would benefit from similar motion corrections. When applied to clinical PET images, FlowNet-PET would provide the capability of acquiring high quality images without the requirement for either longer scan times or subjecting the patients to higher doses of radiation. Accordingly, the imaging process would likely become more efficient and/or safer, which would be appreciated by both the health care institutions and their patients. / Graduate

By organically fusing the methods of convolutional neural network (CNN) and recurrent neural network (RNN), this dissertation focuses on the application of optical character recognition and image classification processing. The first part of this dissertation presents an end-to-end novel receipt recognition system for capturing effective information from receipts (CEIR). The main contributions of this research part are divided into three parts. First, this research develops a preprocessing method for receipt images. Second, the modified connectionist text proposal network is introduced to execute text detection. Third, the CEIR combines the convolutional recurrent neural network with the connectionist temporal classification with maximum entropy regularization as a loss function to update the weights in networks and extract the characters from receipt. The CEIR system is validated with the scanned receipts optical character recognition and information extraction (SROIE) database. Furthermore, the CEIR system has strong robustness and can be extended to a variety of different scenarios beyond receipts. For the convolutional recurrent structure application of land use image classification, this dissertation comes up with a novel deep learning model for land use classification, the convolutional recurrent land use classifier (CRLUC), which further improves the accuracy in classifying remote sensing land use images. Besides, the convolutional fully-connected neural networks with hard sample memory pool structure (CFMP) is invented to tackle the remote sensing land use image classification tasks. The CRLUC and CFMP algorithm performances are tested in popular datasets. Experimental studies show the proposed algorithms can classify images with higher accuracy and fewer training episodes compared to popular image classification algorithms.

Demyelination is the process where the insulating layer of axons known as myelin is damaged. This affects the propagation of action potentials along axons which can have deteriorating consequences on the motor activity of an organism. Thus it is important to understand the biophysical effects of demyelination to improve the diagnostics of its diseases. We trained a Convolutional Neural Network (CNN) on Coherent anti-Stokes Raman scattering (CARS) microscope images of mice spinal cord inflicted with the demyelinating disease Experimental Autoimmune Encephalomyelitis (EAE). Our CNN was able to classify the images reliably based on clinical scores assigned to the mice. We then synthesized our own images of the spinal cord regions using a 2D Biased Random Walk. These images are simplified versions of the original CARS images and show homogenously myelinated axons, unlike the heterogeneous nerve fibres found in real spinal cords. The images were fed into the trained CNN as an attempt to develop a clinical connection to the biophysical effects of demyelination. We found that the trained CNN was indeed able to capture structural features related to demyelination which can allow us to constrain demyelination models such that they include the simulated parameters of the synthesized images.

Automation is fundamentally changing many industries and retail is no exception. Moonshopis a South African venture trying to solve the problem of autonomous grocery storesusing cameras and computer vision. This project is the continuation of a hackathon heldto explore different methods for Human Action Recognition in Moonshop’s stores.Throughout the project a pipeline for data processing has been developed and two typesof Graph-Convolutional Networks, CTR-GCN and ST-GCN, have been implementedand evaluated on the data produced by this pipeline. The resulting scores aren’t goodenough to call it a success. However, this is not necessarily a fault of the models. Rather,there wasn’t enough data to train on and the existing data was of varying to low quality.This makes it complicated to justly judge the models’ performances. In the future, moreresources should be spent on generating more and better data in order to really evaluatethe feasibility of using Human Action Recognition and Graph-Convolutional Networksat Moonshop.

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Background: Manufacturing and production of daily used products using recyclable materials took a steep incline over the past few years. The recyclable packages that are being considered for this thesis are Tetra Packs. Tetra packs are widely used for packaging liquid foods. A few recyclable methods are being used to recycle such tetra packs which use the barcode behind them to scan and give which recyclable method the particular tetra pack has to go through. In some cases, the barcode might get worn off due to excessive usage leading to a problem. Therefore there needs to be a research that has to be carried out to address this problem and find a solution to the same.  Objectives: The objectives to address and fulfill the aim of this thesis are : To find/create the necessary data set containing clear pictures of the tetra packs with visible recyclable logos. To draw bounding boxes around the objects i.e., logos for training the models. To test the data set by applying all four Deep Learning models. To compare each of the models on speed and the performance metrics i.e, mAP and IoU and identify the best algorithm among them.  Methods: To answer the research question we have chosen one research methodol- ogy which is Experiment.Results: YOLOv5 is considered as the best algorithm among the four algorithms we are comparing. Speed of YOLOv5, SSD and Faster-RCNN were found to be similar i.e, 0.2 seconds whereas Mask-RCNN was the slowest with the detection speed of 1.0 seconds. The mAP score of SSD is 0.86 which is the highest among the four followed by YOLOv5 at 0.771, Faster-RCNN at 0.67 and Mask-RCNN at 0.62. IoU score of Faster-RCNN is 0.96 which is the highest among the four followed by YOLOv5 at 0.95, SSD at 0.50 and Mask-RCNN at 0.321. On comparing all the above results YOLOv5 is concluded as the best algorithm among the four as it is relatively fast and accurate without any major draw-backs in any category.  Conclusions: Amongst the four algorithms Faster-RCNN, YOLO, SSD and Mask- RCNN, YOLOv5 is declared as the best algorithm after comparing all the models based on speed and the performance metrics mAP, IoU. YOLOv5 is considered as the best algorithm among the four algorithms we are comparing.