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

Rapid high-resolution mid-IR imaging for molecular spectral histopathological diagnosis of oesophageal cancers

This thesis is written as part of Marie-Curie international training network called Mid-TECH. Mid-TECH is devoted to improve mid-infrared (MIR) technologies and consists of 15 PhD projects across European universities. This thesis aims to evaluate new technologies and concepts developed by the project partners for their applicability in a biomedical setting. The clinical problem to diagnose oesophageal cancers serves as an example case for this. The thesis consists of three projects all aimed to further the understanding of MIR hyperspectral imaging. The first project discussed in chapter 5 demonstrates the use of an new design of the United States Airforce resolution test chart. The new test chart is developed to evaluate spatial resolution of MIR hyperspectral imaging systems. The use of different materials is discussed and the new iteration of the thes chart is evaluated using a state of the art MIR imaging system. The second project discussed in chapter 6 evaluates the technical differences and their practical implications of discrete frequency MIR imaging systems compared to continuum source systems. A comparison of the two system types is drawn for imaging paraffin embedded sections of oesophageal tissue. Furthermore the effect of chemically removing the paraffin from the sample is compared to a mathematical correction algorithm. The system performance is compared based on their ability to differentiate healthy from cancerous tissue. The third project discussed in chapter 7 evaluates the potential of a new MIR detection scheme called upconversion in combination with a novel MIR laser source. It is a prove of concept study demonstrating that those two technologies can be deployed to do hyperspectral imaging in the MIR.European Commissio

REAL-TIME FIBER OPTIC RAMAN SPECTROSCOPY FOR EARLY DIAGNOSIS OF PRECANCER AND CANCER IN VIVO IN THE UPPER GASTROINTESTINAL TRACT

Ph.DDOCTOR OF PHILOSOPH

Towards high fidelity mapping of global inland water quality using earth observation data

This body of work aims to contribute advancements towards developing globally applicable water quality retrieval models using Earth Observation data for freshwater systems. Eutrophication and increasing prevalence of potentially toxic algal blooms among global inland water bodies have become a major ecological concersn and require direct attention. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products. This study provides one of the first assessments of the state-ofthe-art for trophic status (chlorophyll-a) retrievals for small water bodies using Sentinel-3 Ocean and Land Color Imager (OLCI). Multiple fieldwork campaigns were undertaken for the collection of common aquatic biogeophysical and bio-optical parameters that were used to validate current atmospheric correction and chlorophyll-a retrieval algorithms. The study highlighted the difficulties of obtaining robust retrieval estimates from a coarse spatial resolution sensor from highly variable eutrophic water bodies. Atmospheric correction remains a difficult challenge to operational freshwater monitoring, however, the study further validated previous work confirming applicability of simple, empirically derived retrieval algorithms using top-of-atmosphere data. The apparent scarcity of paired in-situ optical and biogeophysical data for productive inland waters also hinders our capability to develop and validate robust retrieval algorithms. Radiative transfer modeling was used to fill this gap through the development of a novel synthetic dataset of top-of-atmosphere and bottom-of-atmosphere reflectances, which attempts to encompass the immense natural optical variability present in inland waters. Novel aspects of the synthetic dataset include: 1) physics-based, two-layered, size and type specific phytoplankton IOPs for mixed eukaryotic/cyanobacteria 6 assemblages, 2) calculations of mixed assemblage chl-a fluorescence, 3) modeled phycocyanin concentration derived from assemblage based phycocyanin absorption, 4) and paired sensor-specific TOA reflectances which include optically extreme cases and contribution of green vegetation adjacency. The synthetic bottom-of-atmosphere reflectance spectra were compiled into 13 distinct optical water types similar to those discovered using in-situ data. Inspection showed similar relationships and ranges of concentrations and inherent optical properties of natural waters. This dataset was used to calculate typical surviving water-leaving signal at top-of-atmosphere, as well as first order calculations of the signal-to-noise-ratio (SNR) for the various optical water types, a first for productive inland waters, as well as conduct a sensitivity analysis of cyanobacteria detection from top-of-atmosphere. Finally, the synthetic dataset was used to train and test four state-of-the-art machine learning architectures for multi-parameter retrieval and cross-sensor capability. Initial results provide reliable estimates of water quality parameters and inherent optical properties over a highly dynamic range of water types, at various spectral and spatial sensor resolutions. It is hoped the results of this work incrementally improves inland water Earth observation on multiple aspects of the forward and inverse modelling process, and provides an improvement in our capabilities for routine, global monitoring of inland water quality

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Intelligent Microwave Detection of Surface and Sub-Surface Anomalies

Microwave near-field testing is a promising nondestructive testing method because of its unique capability to interrogate metallic surfaces and multi-layer dielectric structures. Due to today's need for lighter, stronger, and more-durable materials, enhanced dielectrics are increasingly being used to replace or coat metals. Consequently, conventional testing methods, with their limited penetration, are no longer adequate, but microwave testing sensors transmit signals that can penetrate into dielectrics and so detect surface and subsurface anomalies. Due to the growing use of microwave near-field sensors for different daily life applications, there is an ongoing need to improve their performance. Recently, artificial engineered electromagnetic materials (metamaterials) have been utilized to demonstrate strong localization and enhancement of electrical fields around sensing elements in order to improve probes sensitivities. Metamaterials are being used to enhance sensors design at the hardware level for better anomaly and flaw detection. Currently, microwave sensors are being used to capture large and complex information, but doing so requires better integration of signal processing methods. Implementing artificial intelligence algorithms to process information collected by microwave sensors can address the challenge associated with information complexity or obscure pattern changes. To address this gap in microwave near-field evaluation, this study integrates machine learning techniques with microwave near-field testing. Machine learning is a subset of artificial intelligence that denotes a set of methods that can automatically detect patterns in data to build a learning model. The learned model is then used for decision making about unseen data. Employing machine learning techniques for building classification models, this work combines machine learning algorithms with microwave near-field testing. In particular, it aims to build machine learning models that enhance flaw and anomaly detection in microwave near-field testing. The trained machine models can be integrated or embedded in a portable device or rack mounted microwave near-field testing equipment. The value of this approach is confirmed through numerical simulations and laboratory measurements

Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones

Tesis por compendio[ES] En el presente trabajo se realiza un análisis completo de las técnicas de iluminación y registro de imagen desarrollados hasta el momento y que permiten emplear la fluorescencia intrínseca de estructuras biológicas para aumentar la capacidad de identificación, detección y análisis de lesiones y anomalías que puedan presentarse. El trabajo se ha enfocado principalmente en a) el análisis, validación y desarrollo de técnicas de detección precoz de lesiones asociadas al Carcinoma Escamoso Epidermoide (oncología otorrinolaringológica), así como posibles lesiones precursoras y b) el análisis y desarrollo de una metodología que permita registrar imágenes de fluorescencia y cuantificar mediante la aplicación de técnicas de procesado de imagen la afección provocada por el Acné Vulgaris (dermatología). Se proponen nuevas formas de adquisición, registro y procesado de imágenes de fluorescencia que mejoran de forma objetiva la capacidad de detección y gestión de las anteriores patologías. El desarrollo de la Tesis ha dado lugar a varios resultados. Parte de los resultados se han estructurado en forma de artículos de investigación y trabajos publicados en revistas JCR. Así, la tesis se va a desarrollar por Compendio de Artículos, incluyéndose: a) Artículo de Investigación 1 publicado en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Artículo de Investigación 2 publicado en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Artículo de Investigación publicado en Congreso Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudio Observacional (modalidad de ensayo clínico para técnicas no invasivas) con DICTAMEN FAVORABLE para su realización con fecha 29 de Septiembre de 2022. El Estudio Observacional ha sido evaluado por los miembros del Comité Ético de Investigación con medicamentos del Departamento Arnau de Vilanova-Llíria. A causa de la pandemia causada por la COVID-19, la ejecución del trabajo se ha visto pospuesta y se iniciará en el último trimestre de 2022. Título: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De forma adicional a los trabajos publicados, se ha redactado en forma de review (susceptible de ser publicado) el estado del arte que ha permitido desarrollar el OBJETIVO ESPECÍFICO 3. Se adjunta como Artículo de Investigación susceptible de publicación en revista JCR. Título: Segmentation of acne vulgaris images algorithms. La ejecución del Estudio Observacional se plantea como la línea de investigación a seguir y que da continuidad a la investigación iniciada en la presente Tesis Doctoral. El documento de Tesis está estructurado en 7 capítulos y 11 Anexos. Para el desarrollo del presente trabajo se han planteado tres objetivos específicos. Cada artículo o trabajo publicado se corresponde con el desarrollo de cada uno de los tres objetivos específicos. Así, cada uno de los capítulos 3, 4 y 5 plantea el escenario, desarrollo y conclusiones obtenidas que han dado como resultado cada uno de los trabajos publicados de forma independiente.[CAT] En el present treball es realitza una anàlisi completa de les tècniques d'il·luminació i registre d'imatge desenvolupats fins al moment i que permeten emprar la fluorescència intrínseca d'estructures biològiques per a augmentar la capacitat d'identificació, detecció i anàlisi de lesions i anomalies que puguen presentar-se. El treball s'ha enfocat principalment en a) l'anàlisi, validació i desenvolupament de tècniques de detecció precoç de lesions associades al Carcinoma Escatós Epidermoide (oncologia otorrinolaringològica), així com possibles lesions precursores i b) l'anàlisi i desenvolupament d'una metodologia que permeta registrar imatges de fluorescència i quantificar mitjançant l'aplicació de tècniques de processament d'imatge l'afecció provocada per l'Acne Vulgaris (dermatologia). Es proposen noves formes d'adquisició, registre i processament d'imatges de fluorescència que milloren de manera objectiva la capacitat de detecció i gestió de les anteriors patologies. El desenvolupament de la Tesi ha donat lloc a diversos resultats. Part dels resultats s'han estructurat en forma d'articles d'investigació i treballs publicats en revistes JCR. Així, la tesi es desenvoluparà per Compendi d'Articles, incloent-se: a) Article d'Investigació 1 publicat en revista JCR. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Article d'Investigació 2 publicat en revista JCR. Hough Transform Sensitivivy Factor Calculation Model Applied to the Analysis of Acné Vulgaris Skin Lesions. c) Article d'Investigació publicat en Congrés Internacional. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Estudi Observacional (modalitat d'assaig clínic per a tècniques no invasives) amb DICTAMEN FAVORABLE per a la seua realització amb data 29 de Setembre de 2022. L'Estudi Observacional ha sigut avaluat pels membres del Comité Ètic d'Investigació amb medicaments del Departament Arnau de Vilanova-Llíria. A causa de la pandèmia causada per la COVID-19, l'execució del treball s'ha vist posposada i s'iniciarà en l'últim trimestre de 2022. Títol: ANÁLISIS DE IMÁGENES DE AUTOFLUORESCENCIA PARA SU USO POTENCIAL COMO SISTEMA NO INVASIVO EN LA DETECCIÓN DE LESIONES ORALES POTENCIALMENTE MALIGNAS. De manera addicional als treballs publicats, s'ha redactat en forma de review (susceptible de ser publicat) l'estat de l'art que ha permés desenvolupar l'OBJECTIU ESPECÍFIC 3. S'adjunta com a Article d'Investigació susceptible de publicació en revista JCR. Títol: Segmentation of acne vulgaris images algorithms. L'execució de l'Estudi Observacional es planteja com la línia d'investigació a seguir i que dona continuïtat a la investigació iniciada en la present Tesi Doctoral. El document de Tesi està estructurat en 7 capítols i 11 Annexos. Per al desenvolupament del present treball s'han plantejat tres objectius específics. Cada article o treball publicat es correspon amb el desenvolupament de cadascun dels tres objectius específics. Així, cadascun dels capítols 3, 4 i 5 planteja l'escenari, desenvolupament i conclusions obtingudes que han donat com a resultat cadascun dels treballs publicats de manera independent.[EN] In the present work, a complete analysis is made of the illumination and image recording techniques developed so far that allow the use of intrinsic fluorescence of biological structures to increase the capacity of identification, detection and analysis of lesions and anomalies that may occur. The work has focused mainly on a) the analysis, validation and development of techniques for the early detection of lesions associated with Squamous Epidermoid Carcinoma (otorhinolaryngological oncology), as well as possible precursor lesions, and b) the analysis and development of a methodology for recording fluorescence images and quantifying the condition caused by Acne Vulgaris (dermatology) through the application of image processing techniques. New ways of acquisition, registration and processing of fluorescence images are proposed to objectively improve the capacity of detection and management of the previous pathologies. The development of the Thesis has led to several results. Part of the results have been structured in the form of research articles and papers published in JCR journals. Thus, the thesis is going to be developed by Compendium of Articles, including: a) Research Article 1 published in JCR journal. Segmentation methods for acne vulgaris images: Proposal of a new methodology applied to fluorescence images. b) Research Article 2 published in JCR journal. Hough Transform Sensitivity Factor Calculation Model Applied to the Analysis of Acne Vulgaris Skin Lesions. c) Research Article published in International Congress. Analysis of segmentation methods for acne vulgaris images. Proposal of a new methodology applied to fluorescence images. d) Observational study (clinical trial modality for non-invasive techniques) with FAVORABLE OPINION for its realization on September 29, 2022. The Observational Study has been evaluated by the members of the Ethics Committee for Research with Medicines of the Arnau de Vilanova-Llíria Department. Due to the pandemic caused by COVID-19, the execution of the work has been postponed and will start in the last quarter of 2022. Title: ANALYSIS OF AUTOFLUORESCENCE IMAGES FOR POTENTIAL USE AS A NON-INVASIVE SYSTEM IN THE DETECTION OF POTENTIALLY MALIGNANT ORAL LESIONS. In addition to the published works, the state of the art that has allowed the development of SPECIFIC OBJECTIVE 3 has been written in the form of a review (susceptible of being published). It is attached as a Research Article susceptible of being published in a JCR journal. Title: Segmentation of acne vulgaris images algorithms. The execution of the Observational Study is proposed as the line of research to be followed and which gives continuity to the research initiated in the present Doctoral Thesis. The Thesis document is structured in 7 chapters and 11 Annexes. Three specific objectives have been set for the development of this work. Each article or published work corresponds to the development of each of the three specific objectives. Thus, each of the chapters 3, 4 and 5 presents the scenario, development and conclusions obtained that have resulted in each of the works published independently.Moncho Santonja, M. (2022). Aplicación de técnicas de iluminación y procesado de imagen para la detección y medición de lesiones [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/191027Compendi

Optimization of Automatic Target Recognition with a Reject Option Using Fusion and Correlated Sensor Data

This dissertation examines the optimization of automatic target recognition (ATR) systems when a rejection option is included. First, a comprehensive review of the literature inclusive of ATR assessment, fusion, correlated sensor data, and classifier rejection is presented. An optimization framework for the fusion of multiple sensors is then developed. This framework identifies preferred fusion rules and sensors along with rejection and receiver operating characteristic (ROC) curve thresholds without the use of explicit misclassification costs as required by a Bayes\u27 loss function. This optimization framework is the first to integrate both vertical warfighter output label analysis and horizontal engineering confusion matrix analysis. In addition, optimization is performed for the true positive rate, which incorporates the time required by classification systems. The mathematical programming framework is used to assess different fusion methods and to characterize correlation effects both within and across sensors. A synthetic classifier fusion-testing environment is developed by controlling the correlation levels of generated multivariate Gaussian data. This synthetic environment is used to demonstrate the utility of the optimization framework and to assess the performance of fusion algorithms as correlation varies. The mathematical programming framework is then applied to collected radar data. This radar fusion experiment optimizes Boolean and neural network fusion rules across four levels of sensor correlation. Comparisons are presented for the maximum true positive rate and the percentage of feasible thresholds to assess system robustness. Empirical evidence suggests ATR performance may improve by reducing the correlation within and across polarimetric radar sensors. Sensitivity analysis shows ATR performance is affected by the number of forced looks, prior probabilities, the maximum allowable rejection level, and the acceptable error rates