Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

MRI-based radiomics: Quantifying the stability and reproducibility of tumour heterogeneity in vivo and in a 3D printed phantom

Magnetic resonance imaging (MRI) is a key component in the oncology workflow. Radiomics analysis is a new approach that uses standard of care (SOC) magnetic resonance (MR) images to non-invasively characterise tumour heterogeneity. For radiomics to be reliable, the imaging features measured must be stable and reproducible. This thesis aims to quantify the stability and reproducibility of MRI-based radiomics in vivo and in a 3D printed phantom. Chapter 4 explores the feasibility of constructing a 3D printed phantom using an MRI visible material (‘red resin’). The study shows that the material used to construct an anthropomorphic skull phantom mimicked human cortical bone with a T2* of 411 ± 19 µs. The phantom material provided sufficient signal for tissue segmentation however was only visible with an ultrashort echo time sequence, not commonly used in SOC imaging. Chapter 5 investigates a high temperature resin (‘white resin’) where a texture object was developed for analysis. The ‘white resin’ was visible using SOC sequences. The interscanner repeatability measurements of the texture phantom demonstrated high reproducibility with 76% of texture features having an ICC > 0.9. In chapter 6, further texture and shape objects were developed and employed in a multi-centre study assessing inter and intrascanner variation of MRI-based radiomics. The phantom was stable over a period of 12 months, with a T1 and T2 of 150.7 ± 6.7 ms and 56.1 ± 3.9 ms, respectively. The study also found that histogram features were more stable (ICC > 0.8 for 67%) compared to texture (ICC > 0.8 for 58%) and shape texture (ICC > 0.8 for 0%) across the 8 scanners. In chapter 7, phantom measurements found that radiomics features were more sensitive to changes of image resolution and noise. The in vivo test-retest component of chapter 7 detected many unstable features not suitable for use in a radiomics prognostic model. In chapter 8, of the 83 features computed only 19 features had significant changes between the baseline, mid and post radiation treatment and may be informative to assess rectal cancer treatment response. When considering using radiomics analysis for SOC MRI scans, caution must be taken to ensure imaging protocols, imaging equipment including scanners and coils are consistent to improve intra and inter-institutional feature robustness. This can be achieved with regular quality assurance (QA) of imaging protocols using a suitable phantom and appropriate feature selection using phantom and in vivo datasets

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Advanced methods for relightable scene representations in image space

The realistic reproduction of visual appearance of real-world objects requires accurate computer graphics models that describe the optical interaction of a scene with its surroundings. Data-driven approaches that model the scene globally as a reflectance field function in eight parameters deliver high quality and work for most material combinations, but are costly to acquire and store. Image-space relighting, which constrains the application to create photos with a virtual, fix camera in freely chosen illumination, requires only a 4D data structure to provide full fidelity. This thesis contributes to image-space relighting on four accounts: (1) We investigate the acquisition of 4D reflectance fields in the context of sampling and propose a practical setup for pre-filtering of reflectance data during recording, and apply it in an adaptive sampling scheme. (2) We introduce a feature-driven image synthesis algorithm for the interpolation of coarsely sampled reflectance data in software to achieve highly realistic images. (3) We propose an implicit reflectance data representation, which uses a Bayesian approach to relight complex scenes from the example of much simpler reference objects. (4) Finally, we construct novel, passive devices out of optical components that render reflectance field data in real-time, shaping the incident illumination into the desired imageDie realistische Wiedergabe der visuellen Erscheinung einer realen Szene setzt genaue Modelle aus der Computergraphik für die Interaktion der Szene mit ihrer Umgebung voraus. Globale Ansätze, die das Verhalten der Szene insgesamt als Reflektanzfeldfunktion in acht Parametern modellieren, liefern hohe Qualität für viele Materialtypen, sind aber teuer aufzuzeichnen und zu speichern. Verfahren zur Neubeleuchtung im Bildraum schränken die Anwendbarkeit auf fest gewählte Kameras ein, ermöglichen aber die freie Wahl der Beleuchtung, und erfordern dadurch lediglich eine 4D - Datenstruktur für volle Wiedergabetreue. Diese Arbeit enthält vier Beiträge zu diesem Thema: (1) wir untersuchen die Aufzeichnung von 4D Reflektanzfeldern im Kontext der Abtasttheorie und schlagen einen praktischen Aufbau vor, der Reflektanzdaten bereits während der Messung vorfiltert. Wir verwenden ihn in einem adaptiven Abtastschema. (2) Wir führen einen merkmalgesteuerten Bildsynthesealgorithmus für die Interpolation von grob abgetasteten Reflektanzdaten ein. (3) Wir schlagen eine implizite Beschreibung von Reflektanzdaten vor, die mit einem Bayesschen Ansatz komplexe Szenen anhand des Beispiels eines viel einfacheren Referenzobjektes neu beleuchtet. (4) Unter der Verwendung optischer Komponenten schaffen wir passive Aufbauten zur Darstellung von Reflektanzfeldern in Echtzeit, indem wir einfallende Beleuchtung direkt in das gewünschte Bild umwandeln

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Classifiers and machine learning techniques for image processing and computer vision

Orientador: Siome Klein GoldensteinTese (doutorado) - Universidade Estadual de Campinas, Instituto da ComputaçãoResumo: Neste trabalho de doutorado, propomos a utilizaçãoo de classificadores e técnicas de aprendizado de maquina para extrair informações relevantes de um conjunto de dados (e.g., imagens) para solução de alguns problemas em Processamento de Imagens e Visão Computacional. Os problemas de nosso interesse são: categorização de imagens em duas ou mais classes, detecçãao de mensagens escondidas, distinção entre imagens digitalmente adulteradas e imagens naturais, autenticação, multi-classificação, entre outros. Inicialmente, apresentamos uma revisão comparativa e crítica do estado da arte em análise forense de imagens e detecção de mensagens escondidas em imagens. Nosso objetivo é mostrar as potencialidades das técnicas existentes e, mais importante, apontar suas limitações. Com esse estudo, mostramos que boa parte dos problemas nessa área apontam para dois pontos em comum: a seleção de características e as técnicas de aprendizado a serem utilizadas. Nesse estudo, também discutimos questões legais associadas a análise forense de imagens como, por exemplo, o uso de fotografias digitais por criminosos. Em seguida, introduzimos uma técnica para análise forense de imagens testada no contexto de detecção de mensagens escondidas e de classificação geral de imagens em categorias como indoors, outdoors, geradas em computador e obras de arte. Ao estudarmos esse problema de multi-classificação, surgem algumas questões: como resolver um problema multi-classe de modo a poder combinar, por exemplo, caracteríisticas de classificação de imagens baseadas em cor, textura, forma e silhueta, sem nos preocuparmos demasiadamente em como normalizar o vetor-comum de caracteristicas gerado? Como utilizar diversos classificadores diferentes, cada um, especializado e melhor configurado para um conjunto de caracteristicas ou classes em confusão? Nesse sentido, apresentamos, uma tecnica para fusão de classificadores e caracteristicas no cenário multi-classe através da combinação de classificadores binários. Nós validamos nossa abordagem numa aplicação real para classificação automática de frutas e legumes. Finalmente, nos deparamos com mais um problema interessante: como tornar a utilização de poderosos classificadores binarios no contexto multi-classe mais eficiente e eficaz? Assim, introduzimos uma tecnica para combinação de classificadores binarios (chamados classificadores base) para a resolução de problemas no contexto geral de multi-classificação.Abstract: In this work, we propose the use of classifiers and machine learning techniques to extract useful information from data sets (e.g., images) to solve important problems in Image Processing and Computer Vision. We are particularly interested in: two and multi-class image categorization, hidden messages detection, discrimination among natural and forged images, authentication, and multiclassification. To start with, we present a comparative survey of the state-of-the-art in digital image forensics as well as hidden messages detection. Our objective is to show the importance of the existing solutions and discuss their limitations. In this study, we show that most of these techniques strive to solve two common problems in Machine Learning: the feature selection and the classification techniques to be used. Furthermore, we discuss the legal and ethical aspects of image forensics analysis, such as, the use of digital images by criminals. We introduce a technique for image forensics analysis in the context of hidden messages detection and image classification in categories such as indoors, outdoors, computer generated, and art works. From this multi-class classification, we found some important questions: how to solve a multi-class problem in order to combine, for instance, several different features such as color, texture, shape, and silhouette without worrying about the pre-processing and normalization of the combined feature vector? How to take advantage of different classifiers, each one custom tailored to a specific set of classes in confusion? To cope with most of these problems, we present a feature and classifier fusion technique based on combinations of binary classifiers. We validate our solution with a real application for automatic produce classification. Finally, we address another interesting problem: how to combine powerful binary classifiers in the multi-class scenario more effectively? How to boost their efficiency? In this context, we present a solution that boosts the efficiency and effectiveness of multi-class from binary techniques.DoutoradoEngenharia de ComputaçãoDoutor em Ciência da Computaçã