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Ph.DDOCTOR OF PHILOSOPH

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

Novel deep learning architectures for marine and aquaculture applications

Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Applications of Physically Accurate Deep Learning for Processing Digital Rock Images

Digital rock analysis aims to improve our understanding of the fluid flow properties of reservoir rocks, which are important for enhanced oil recovery, hydrogen storage, carbonate dioxide storage, and groundwater management. X-ray microcomputed tomography (micro-CT) is the primary approach to capturing the structure of porous rock samples for digital rock analysis. Initially, the obtained micro-CT images are processed using image-based techniques, such as registration, denoising, and segmentation depending on various requirements. Numerical simulations are then conducted on the digital models for petrophysical prediction. The accuracy of the numerical simulation highly depends on the quality of the micro-CT images. Therefore, image processing is a critical step for digital rock analysis. Recent advances in deep learning have surpassed conventional methods for image processing. Herein, the utility of convolutional neural networks (CNN) and generative adversarial networks (GAN) are assessed in regard to various applications in digital rock image processing, such as segmentation, super-resolution, and denoising. To obtain training data, different sandstone and carbonate samples were scanned using various micro-CT facilities. After that, validation images previously unseen by the trained neural networks are utilised to evaluate the performance and robustness of the proposed deep learning techniques. Various threshold scenarios are applied to segment the reconstructed digital rock images for sensitivity analyses. Then, quantitative petrophysical analyses, such as porosity, absolute/relative permeability, and pore size distribution, are implemented to estimate the physical accuracy of the digital rock data with the corresponding ground truth data. The results show that both CNN and GAN deep learning methods can provide physically accurate digital rock images with less user bias than traditional approaches. These results unlock new pathways for various applications related to the reservoir characterisation of porous reservoir rocks

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Upper airways segmentation using principal curvatures

Esta tesis propone una nueva técnica para segmentar las vías aéreas superiores. Esta propuesta permite la extracción de estructuras curvilíneas usando curvaturas principales. La propuesta permite la extracción de éstas estructuras en imágenes 2D y 3D. Entre las principales novedades se encuentra la propuesta de un nuevo criterio de parada en la propagación del algoritmo de realce de contraste (operador multi-escala de tipo sombrero alto). De la misma forma, el criterio de parada propuesto es usado para detener los algoritmos de difusión anisotrópica. Además, un nuevo criterio es propuesto para seleccionar las curvaturas principales que conforman las estructuras curvilíneas, que se basa en los criterios propuestos por Steger, Deng et. al. y Armande et. al. Además, se propone un nuevo algoritmo para realizar la supresión de nomáximos que permite reducir la presencia de discontinuidades en el borde de las estructuras curvilíneas. Para extraer los bordes de las estructuras curvilíneas, se utiliza un algoritmo de enlace que incluye un nuevo criterio de distancia para reducir la aparición de agujeros en la estructura final. Finalmente, con base en los resultados obtenidos, se utiliza un algoritmo morfológico para cerrar los agujeros y se aplica un algoritmo de crecimiento de regiones para obtener la segmentación final de las vías respiratorias superiores.This dissertation proposes a new approach to segment the upper airways. This proposal allows the extraction of curvilinear structures based on the principal curvatures. The proposal allows extracting these structures from 2D and 3D images. Among the main novelties is the proposal of a new stopping criterion to stop the propagation of the contrast enhancement algorithm (multiscale top-hat morphological operator). In the same way, the proposed stopping criterion is used to stop the anisotropic diffusion algorithms. In addition, a new criterion is proposed to select the principal curvatures that make up the curvilinear structures, which is based on the criteria proposed by Steger, Deng et. al. and Armande et. al. Furthermore, a new algorithm to perform the non-maximum suppression that allows reducing the presence of discontinuities in the border of curvilinear structures is proposed. To extract the edges of the curvilinear structures, a linking algorithm is used that includes a new distance criterion to reduce the appearance of gaps in the final structure. Finally, based on the obtained results, a morphological algorithm is used to close the gaps and a region growing algorithm to obtain the final upper airways segmentation is applied.Doctor en IngenieríaDoctorad