A New Approach to Synthetic Image Evaluation

This study is dedicated to enhancing the effectiveness of Optical Character Recognition (OCR) systems, with a special emphasis on Arabic handwritten digit recognition. The choice to focus on Arabic handwritten digits is twofold: first, there has been relatively less research conducted in this area compared to its English counterparts; second, the recognition of Arabic handwritten digits presents more challenges due to the inherent similarities between different Arabic digits.OCR systems, engineered to decipher both printed and handwritten text, often face difficulties in accurately identifying low-quality or distorted handwritten text. The quality of the input image and the complexity of the text significantly influence their performance. However, data augmentation strategies can notably improve these systems\u27 performance. These strategies generate new images that closely resemble the original ones, albeit with minor variations, thereby enriching the model\u27s learning and enhancing its adaptability. The research found Conditional Variational Autoencoders (C-VAE) and Conditional Generative Adversarial Networks (C-GAN) to be particularly effective in this context. These two generative models stand out due to their superior image generation and feature extraction capabilities. A significant contribution of the study has been the formulation of the Synthetic Image Evaluation Procedure, a systematic approach designed to evaluate and amplify the generative models\u27 image generation abilities. This procedure facilitates the extraction of meaningful features, computation of the Fréchet Inception Distance (LFID) score, and supports hyper-parameter optimization and model modifications

A Synergistic Framework Leveraging Autoencoders and Generative Adversarial Networks for the Synthesis of Computational Fluid Dynamics Results in Aerofoil Aerodynamics

In the realm of computational fluid dynamics (CFD), accurate prediction of aerodynamic behaviour plays a pivotal role in aerofoil design and optimization. This study proposes a novel approach that synergistically combines autoencoders and Generative Adversarial Networks (GANs) for the purpose of generating CFD results. Our innovative framework harnesses the intrinsic capabilities of autoencoders to encode aerofoil geometries into a compressed and informative 20-length vector representation. Subsequently, a conditional GAN network adeptly translates this vector into precise pressure-distribution plots, accounting for fixed wind velocity, angle of attack, and turbulence level specifications. The training process utilizes a meticulously curated dataset acquired from JavaFoil software, encompassing a comprehensive range of aerofoil geometries. The proposed approach exhibits profound potential in reducing the time and costs associated with aerodynamic prediction, enabling efficient evaluation of aerofoil performance. The findings contribute to the advancement of computational techniques in fluid dynamics and pave the way for enhanced design and optimization processes in aerodynamics.Comment: 9 pages, 11 figure

Comparative Evaluation and Implementation of State-of-the-Art Techniques for Anomaly Detection and Localization in the Continual Learning Framework

openThe capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field.The capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field

Integrating State-of-the-Art Approaches for Anomaly Detection and Localization in the Continual Learning Setting

openThe significant attention surrounding the application of anomaly detection (AD) in identifying defects within industrial environments using only normal samples has prompted research and development in this area. However, traditional AD methods have been primarily focused on the current set of examples, resulting in a limitation known as catastrophic forgetting when encountering new tasks. The inflexibility of these methods and the challenges posed by real-world industrial scenarios necessitate the urgent enhancement of the adaptive capabilities of AD models. Therefore, this thesis presents an integrated framework that combines the concepts of continual learning (CL) and anomaly detection (AD) to achieve the objective of anomaly detection in continual learning (ADCL). To evaluate the efficacy of the framework, a thorough comparative analysis is conducted to assess the performance of three specific methods for the AD task: the EfficientAD, Patch Distribution Modeling Framework (PaDiM) and the Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM). Moreover, the framework incorporates the use of replay techniques to enable continual learning (CL). In order to determine the superior technique, a comprehensive evaluation is carried out using diverse metrics that measure the relative performance of each method. To validate the proposed approach, a robust real-world dataset called MVTec AD is employed, consisting of images with pixel-based anomalies. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, offering a solid foundation for further advancements in this field of study.The significant attention surrounding the application of anomaly detection (AD) in identifying defects within industrial environments using only normal samples has prompted research and development in this area. However, traditional AD methods have been primarily focused on the current set of examples, resulting in a limitation known as catastrophic forgetting when encountering new tasks. The inflexibility of these methods and the challenges posed by real-world industrial scenarios necessitate the urgent enhancement of the adaptive capabilities of AD models. Therefore, this thesis presents an integrated framework that combines the concepts of continual learning (CL) and anomaly detection (AD) to achieve the objective of anomaly detection in continual learning (ADCL). To evaluate the efficacy of the framework, a thorough comparative analysis is conducted to assess the performance of three specific methods for the AD task: the EfficientAD, Patch Distribution Modeling Framework (PaDiM) and the Discriminatively Trained Reconstruction Anomaly Embedding Model (DRAEM). Moreover, the framework incorporates the use of replay techniques to enable continual learning (CL). In order to determine the superior technique, a comprehensive evaluation is carried out using diverse metrics that measure the relative performance of each method. To validate the proposed approach, a robust real-world dataset called MVTec AD is employed, consisting of images with pixel-based anomalies. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, offering a solid foundation for further advancements in this field of study

Detecting Invasive Insects Using Uncewed Aerial Vehicles and Variational Autoencoders

In this thesis, we use machine learning techniques to address limitations in our ability to monitor pest insect migrations. Invasive insect populations, such as the brown marmorated stink bug (BMSB), cause significant economic and environmental damages. In order to mitigate these damages, tracking BMSB migration is vital, but it also poses a challenge. The current state-of-the-art solution to track insect migrations is called mark-release-recapture. In mark-release-recapture, a researcher marks insects with a fluorescent powder, releases them back into the wild, and searches for the insects using ultra-violet flashlights at suspected migration destination locations. However, this involves a significant amount of labor and has a low recapture rate. By automating the insect search step, the recapture rate can be improved, reducing the amount of labor required in the process and improving the quality of the data. We propose a solution to the BMSB migration tracking problem using an unmanned aerial vehicle (UAV) to collect video data of the area of interest. Our system uses an ultra violet (UV) lighting array and digital cameras mounted on the bottom of the UAV, as well as artificial intelligence algorithms such as convolutional neural networks (CNN), and multiple hypotheses tracking (MHT) techniques. Specifically, we propose a novel computer vision method for insect detection using a Convolutional Variational Auto Encoder (CVAE). Our experimental results show that our system can detect BMSB with high precision and recall, outperforming the current state-of-the-art. Additionally, we associate insect observations using MHT, improving detection results and accurately counting real-world insects

Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers

The launch of ChatGPT has garnered global attention, marking a significant milestone in the field of Generative Artificial Intelligence. While Generative AI has been in effect for the past decade, the introduction of ChatGPT has ignited a new wave of research and innovation in the AI domain. This surge in interest has led to the development and release of numerous cutting-edge tools, such as Bard, Stable Diffusion, DALL-E, Make-A-Video, Runway ML, and Jukebox, among others. These tools exhibit remarkable capabilities, encompassing tasks ranging from text generation and music composition, image creation, video production, code generation, and even scientific work. They are built upon various state-of-the-art models, including Stable Diffusion, transformer models like GPT-3 (recent GPT-4), variational autoencoders, and generative adversarial networks. This advancement in Generative AI presents a wealth of exciting opportunities and, simultaneously, unprecedented challenges. Throughout this paper, we have explored these state-of-the-art models, the diverse array of tasks they can accomplish, the challenges they pose, and the promising future of Generative Artificial Intelligence

The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection

Artificial intelligence represents a new frontier in human medicine that could save more lives and reduce the costs, thereby increasing accessibility. As a consequence, the rate of advancement of AI in cancer medical imaging and more particularly tissue pathology has exploded, opening it to ethical and technical questions that could impede its adoption into existing systems. In order to chart the path of AI in its application to cancer tissue imaging, we review current work and identify how it can improve cancer pathology diagnostics and research. In this review, we identify 5 core tasks that models are developed for, including regression, classification, segmentation, generation, and compression tasks. We address the benefits and challenges that such methods face, and how they can be adapted for use in cancer prevention and treatment. The studies looked at in this paper represent the beginning of this field and future experiments will build on the foundations that we highlight