Infrastructure for machine learning and computer vision

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe infrastructure surrounding machine learning projects is of utmost importance: Machine learning projects require data acquisition mechanisms, software for data processing, as well as a benchmarking platform for evaluating performance of machine learning algorithms over time. In this report we describe our work aimed at developing such infrastructure for a Europe based computer vision startup specializing in human behaviour tracking. We discuss three projects comprising the work. One dedicated to creating a machine learning dataset for human behaviour monitoring, another to developing a screen-camera calibration tool, and third to setting up a benchmarking platform. The projects were integrated with the core technology of the startup, and will continue to be applied in the future.A infraestrutura para projetos de machine learning é de extrema importância para o desenvolvimento da tecnologia: exigem-se mecanismos de aquisição de dados, software para processamento de dados e uma plataforma de benchmarking para avaliar o desempenho de algoritmos de machine learning ao longo do tempo. No presente relatório, descreve-se o trabalho destinado a desenvolver essa infraestrutura para uma Startup Europeia de computer vision, especializada em rastreamento de comportamento humano atraves de câmeras de videos. Enfoca-se em três projetos que compõem o trabalho: o primeiro, dedicado à criação de um conjunto de dados de machine learning para monitoramento de comportamento humano; o segundo, sobre o desenvolvimento de uma ferramenta de calibração de câmeras e ecrã; e o terceiro, relata a criação de uma plataforma de benchmarking. Tais projetos foram integrados com a tecnologia central da Startup e serão aplicados no futuro

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our result

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have shown impressive performance in several application domains, ranging from computer vision to cybersecurity. However, it has also been shown that adversarial input perturbations carefully crafted either at training or at test time can easily subvert their predictions. The vulnerability of machine learning to such wild patterns (also referred to as adversarial examples), along with the design of suitable countermeasures, have been investigated in the research field of adversarial machine learning. In this work, we provide a thorough overview of the evolution of this research area over the last ten years and beyond, starting from pioneering, earlier work on the security of non-deep learning algorithms up to more recent work aimed to understand the security properties of deep learning algorithms, in the context of computer vision and cybersecurity tasks. We report interesting connections between these apparently-different lines of work, highlighting common misconceptions related to the security evaluation of machine-learning algorithms. We review the main threat models and attacks defined to this end, and discuss the main limitations of current work, along with the corresponding future challenges towards the design of more secure learning algorithms.Comment: Accepted for publication on Pattern Recognition, 201