Perceptual Image Similarity Metrics and Applications.

This dissertation presents research in perceptual image similarity metrics and applications, e.g., content-based image retrieval, perceptual image compression, image similarity assessment and texture analysis. The first part aims to design texture similarity metrics consistent with human perception. A new family of statistical texture similarity features, called Local Radius Index (LRI), and corresponding similarity metrics are proposed. Compared to state-of-the-art metrics in the STSIM family, LRI-based metrics achieve better texture retrieval performance with much less computation. When applied to the recently developed perceptual image coder, Matched Texture Coding (MTC), they enable similar performance while significantly accelerating encoding. Additionally, in photographic paper classification, LRI-based metrics also outperform pre-existing metrics. To fulfill the needs of texture classification and other applications, a rotation-invariant version of LRI, called Rotation-Invariant Local Radius Index (RI-LRI), is proposed. RI-LRI is also grayscale and illuminance insensitive. The corresponding similarity metric achieves texture classification accuracy comparable to state-of-the-art metrics. Moreover, its much lower dimensional feature vector requires substantially less computation and storage than other state-of-the-art texture features. The second part of the dissertation focuses on bilevel images, which are images whose pixels are either black or white. The contributions include new objective similarity metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of objective metrics. Several similarity metrics are proposed that outperform existing ones in the sense of attaining significantly higher Pearson and Spearman-rank correlations with the ground truth. The new metrics include Adjusted Percentage Error, Bilevel Gradient Histogram, Connected Components Comparison and combinations of such. Another portion of the dissertation focuses on the aforementioned MTC, which is a block-based image coder that uses texture similarity metrics to decide if blocks of the image can be encoded by pointing to perceptually similar ones in the already coded region. The key to its success is an effective texture similarity metric, such as an LRI-based metric, and an effective search strategy. Compared to traditional image compression algorithms, e.g., JPEG, MTC achieves similar coding rate with higher reconstruction quality. And the advantage of MTC becomes larger as coding rate decreases.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113586/1/yhzhai_1.pd

Emerging Techniques in Breast MRI

As indicated throughout this chapter, there is a constant effort to move to more sensitive, specific, and quantitative methods for characterizing breast tissue via magnetic resonance imaging (MRI). In the present chapter, we focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast. At the physiological scale, we present an overview of ultrafast dynamic contrast-enhanced MRI and magnetic resonance elastography which provide remarkable insights into the vascular and mechanical properties of tissue, respectively. Moving to the biochemical scale, magnetization transfer, chemical exchange saturation transfer, and spectroscopy (both “conventional” and hyperpolarized) methods all provide unique, noninvasive, insights into tumor metabolism. Given the breadth and depth of information that can be obtained in a single MRI session, methods of data synthesis and interpretation must also be developed. Thus, we conclude the chapter with an introduction to two very different, though complementary, methods of data analysis: (1) radiomics and habitat imaging, and (2) mechanism-based mathematical modeling

Breast Cancer Analysis in DCE-MRI

Breast cancer is the most common women tumour worldwide, about 2 million new cases diagnosed each year (second most common cancer overall). This disease represents about 12% of all new cancer cases and 25% of all cancers in women. Early detection of breast cancer is one of the key factors in determining the prognosis for women with malignant tumours. The standard diagnostic tool for the detection of breast cancer is x-ray mammography. The disadvantage of this method is its low specificity, especially in the case of radiographically dense breast tissue (young or under-forty women), or in the presence of scars and implants within the breast. Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has demonstrated a great potential in the screening of high-risk women for breast cancer, in staging newly diagnosed patients and in assessing therapy effects. However, due to the large amount of information, DCE-MRI manual examination is error prone and can hardly be inspected without the use of a Computer-Aided Detection and Diagnosis (CAD) system. Breast imaging analysis is made harder by the dynamical characteristics of soft tissues since any patient movements (such as involuntary due to breathing) may affect the voxel-by-voxel dynamical analysis. Breast DCE-MRI computer-aided analysis needs a pre-processing stage to identify breast parenchyma and reduce motion artefacts. Among the major issues in developing CAD for breast DCE-MRI, there is the detection and classification of lesions according to their aggressiveness. Moreover, it would be convenient to determine those subjects who are likely to not respond to the treatment so that a modification may be applied as soon as possible, relieving them from potentially unnecessary or toxic treatments. In this thesis, an automated CAD system is presented. The proposed CAD aims to support radiologist in lesion detection, diagnosis and therapy assessment after a suitable preprocessing stage. Segmentation of breast parenchyma has been addressed relying on fuzzy binary clustering, breast anatomical priors and morphological refinements. The breast mask extraction module combines three 2D Fuzzy C-Means clustering (executed from the three projection, axial, coronal and transversal) and geometrical breast anatomy characterization. In particular, seven well-defined key-points have been considered in order to accurately segment breast parenchyma from air and chest-wall. To diminish the effects of involuntary movement artefacts, it is usual to apply a motion correction of the DCE-MRI volumes before of any data analysis. However, there is no evidence that a single Motion Correction Technique (MCT) can handle different deformations - small or large, rigid or non-rigid - and different patients or tissues. Therefore, it would be useful to develop a quality index (QI) to evaluate the performance of different MCTs. The existent QI might not be adequate to deal with DCE-MRI data because of the intensity variation due to contrast media. Therefore, in developing a novel QI, the underlying idea is that once DCE-MRI data have been realigned using a specific MCT, the dynamic course of the signal intensity should be as close as possible to physiological models, such as the currently accepted ones (e.g. Tofts-Kermode, Extended Tofts-Kermode, Hayton-Brady, Gamma Capillary Transit Time, etc.). The motion correction module ranks all the MCTs, using the QI, selects the best MCT and applies a correction before of further data analysis. The proposed lesion detection module performs the segmentation of lesions in Regions of Interest (ROIs) by means of classification at a pixel level. It is based on a Support Vector Machine (SVM) trained with dynamic features, extracted from a suitably pre-selected area by using a pixel-based approach. The pre-selection mask strongly improves the final result. The lesion classification module evaluates the malignity of each ROI by means of 3D textural features. The Local Binary Patterns descriptor has been used in the Three Orthogonal Planes (LBP-TOP) configuration. A Random Forest has been used to achieve the final classification into a benignant or malignant lesion. The therapy assessment stage aims to predict the patient primary tumour recurrence to support the physician in the evaluation of the therapy effects and benefits. For each patient which has at least a malignant lesion, the recurrence of the disease has been evaluated by means of a multiple classifiers system. A set of dynamic, textural, clinicopathologic and pharmacokinetic features have been used to assess the probability of recurrence for the lesions. Finally, to improve the usability of the proposed work, we developed a framework for tele-medicine that allows advanced medical image remote analysis in a secure and versatile client-server environment, at a low cost. The benefits of using the proposed framework will be presented in a real-case scenario where OsiriX, a wide-spread medical image analysis software, is allowed to perform advanced remote image processing in a simple manner over a secure channel. The proposed CAD system have been tested on real breast DCE-MRI data for the available protocols. The breast mask extraction stage shows a median segmentation accuracy and Dice similarity index of 98% (+/-0,49) and 93% %(+/-1,48) respectively and 100% of neoplastic lesion coverage. The motion correction module is able to rank the MCTs with an accordance of 74% with a 'reference ranking'. Moreover, by only using 40% of the available volume, the computational load is reduced selecting always the best MCT. The automatic detection maximises the area of correctly detected lesions while minimising the number of false alarms with an accuracy of 99% and the lesions are, then, diagnosed according to their stage with an accuracy of 85%. The therapy assessment module provides a forecasting of the tumour recurrence with an accuracy of 78% and an AUC of 79%. Each module has been evaluated by a leave-one-patient-out approach, and results show a confidence level of 95% (p<0.05). Finally, the proposed remote architecture showed a very low transmission overhead which settles on about 2.5% for the widespread 10\100 Mbps. Security has been achieved using client-server certificates and up-to-date standards

Efficacy of morphological approach in the classification of urban land covers.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Understanding the often-heterogeneous land use land cover (LULC) in urban areas is critical for among others environmental monitoring, spatial planning and enforcement. Recently, several earth observation satellites have been developed with enhanced spatial resolution that provide for precise and detailed representation of image objects. This has generated new demand for enhanced processing capabilities. Thus, the need for techniques that incorporate spatial and spectral information in the analysis of urban LULC has drawn increasing attention. Enhanced spatial resolution comes with challenges for most pixel based classifiers. This include salt and pepper effects that arise from incapability of pixel based techniques in considering spatial or contextual information related to the pixel of interest during image analysis. These challenges have often contributed to the inaccuracy of heterogeneous LULC classification. Object based techniques on the other hand have been proposed to provide effective framework for incorporating spatial information in their analysis. However, challenges such as over/under segmentation and difficulty or non-robust statistical estimation hamper most object techniques in achieving optimum performance. Thus, to achieve optimum LULC classification, the full exploitation of both spectral-spatial information is essential. Hence, this study investigated the efficacy of Mathematical Morphological (MM) techniques referred to as morphological profiles (MP) in LULC classification of a heterogeneous urban landscape. The first objective of the study evaluated two MP techniques i.e. concatenation of morphological profiles (CMP) and multi-morphological profiles (MMP) in the classification of a heterogeneous urban LULC. Findings from this study indicated that both CMP and MMP provided higher accuracies in classifying a heterogeneous urban landscape. However, in evaluating their capability in preserving geometrical characteristics such as shape, theme, edge and positional similarity of image structures, CMP provided higher accuracies than MMP. This was attributed to the use of Principal Component Analysis (PCA) in the construction of MMP that resulted in the distorted edges of some of the image objects. However, in comparing the techniques in terms of the capability to discriminate image objects, MMP provided higher classification accuracies compared to CMP. This can be attributed to the former’s capability to exploit both spectral and spatial information from very high spatial resolution imagery. Hence in the second objective, MMP was adopted due to its ability to deal with dimensionality problem associated with CMP and its superior object discrimination capability. The findings indicated that MMP significantly enhanced ML and SVM classification accuracies. Specifically, the use of MMP as a feature vector for SVM and ML classification increased LULC distinction of objects with similar spectral signatures in a heterogeneous urban landscape. This is due to its capability to provide an effective framework for synthesis of spectral and spatial information. Overall the study demonstrated that morphological techniques provides robust novel image analysis techniques which can effectively be used for operational classification of a heterogeneous urban LULC

ATTRIBUTE ASSISTED SEISMIC FACIES, FAULTS, KARST, AND ANISOTROPY ANALYSIS

Seismic attributes provide quantitative measures of key statistical, geometric, or kinematic components of the 3D seismic volume. These measures can thus be subsequently used in 3D visualization, interactive crossplotting, or computer-assisted facies analysis. In this dissertation, I evaluate the attribute expression of seismic facies including karst collapse features, mass transport complexes, turbidites, and salt using 3D visualization and 3D pattern recognition. One of the more common and more important seismic facies is salt. Salt segmentation is critical for accelerating velocity modeling, which in turn is necessary for seismic depth migration. In general, geophysicists need to pick the high velocity salt interface manually. In the first chapter of the dissertation, I present a semi-supervised multiattribute clustering method, and apply it not only to salt segmentation, but also to mass transport complex, shale, and sand segmentation in the Gulf of Mexico. I develop a 3D Kuwahara filtering algorithm, and smooth the interior attribute response and sharpen the attribute contrast between one face with neighboring facies. Then, I manually paint target facies to evaluate the ability of candidate attributes to discriminate each seismic facies from the other. Crosscorrelating their histogram, candidate attributes with low correlation coefficients provide good facies discrimination. Kuwahara filtering significantly increases this discrimination. Kuwahara filtered attributes corresponding to interpreter-defined facies are then projected against a Generative Topological Mapping (GTM) manifold, resulting in a suite of n probability density functions (PDFs). The Bhattacharyya distance between the PDF of each unlabeled voxel to each facies PDF results in a probability volume of each interpreter-defined facies. In the second chapter, I introduce a 3D fault enhancement and skeletonization workflow. For large datasets, interpreter hand-picking of faults can be very time-consuming. This process can be accelerated by generating high resolution edge detecting attributes. Coherence is an algorithm that measures both stratigraphic and structural discontinuities. Application of a directional Laplacian of a Gaussian (LoG) filter to coherence volumes provides more continuous and sharper faults. To further increase fault resolution and preserve stratigraphic discontinuities, I skeletonize the filtered coherence volumes perpendicular to the discontinuities with the goal of providing subvoxel resolution. “Fault” points doesn’t fall on the geometric grid suggesting the distribution of the value onto eight neighboring grid points. I demonstrate this fault enhancement and skeletonization workflow through application to two datasets from New Zealand and the Gulf of Mexico. With the advent of shale resource plays, wide azimuth acquisition has become quite common. Migrating seismic gathers into different azimuthal bins provides a means to estimate horizontal stress and natural fractures. Different azimuths preferentially illustrate faults perpendicular to them. However, coherence applied to the lower fold azimuthally limited seismic volumes is contaminated by noise. In the third chapter, I improve the energy ratio coherence algorithm and extend it to map more subtle discontinuities, which can only be seen in different azimuthally limited seismic volumes. The main modification compared to the original energy ratio coherence algorithm is that I add the weighted covariance matrices of each azimuthal sectors together to form a single covariance matrix, thereby improving the signal-to-noise ratio. I apply this multi-azimuth coherence algorithm to two datasets from the Fort Worth Basin. In the fourth chapter, I summarize attribute-assisted interpretation in the Barnett Shale and the Ellenburger Group. Karst, faults, and joints are known to form geologic hazards for most Barnett Shale wells in the Fort Worth Basin. In the best cases, these drilling-related geohazards form conductive features that draw off expensive hydraulic fracturing fluid from the targeted shale formation. In the worst cases, the completed wells are hydraulically connected to the underlying Ellenburger aquifer and produce large amounts of water that must be disposed. Karst collapse generates a distinct morphologic pattern on 3D seismic data. I show that multiple attributes delineate different components of the same geologic features, thereby confirming my interpretation

Resource-constrained re-identification in camera networks

PhDIn multi-camera surveillance, association of people detected in different camera views over time, known as person re-identification, is a fundamental task. Re-identification is a challenging problem because of changes in the appearance of people under varying camera conditions. Existing approaches focus on improving the re-identification accuracy, while no specific effort has yet been put into efficiently utilising the available resources that are normally limited in a camera network, such as storage, computation and communication capabilities. In this thesis, we aim to perform and improve the task of re-identification under constrained resources. More specifically, we reduce the data needed to represent the appearance of an object through a proposed feature selection method and a difference-vector representation method. The proposed feature-selection method considers the computational cost of feature extraction and the cost of storing the feature descriptor jointly with the feature’s re-identification performance to select the most cost-effective and well-performing features. This selection allows us to improve inter-camera re-identification while reducing storage and computation requirements within each camera. The selected features are ranked in the order of effectiveness, which enable a further reduction by dropping the least effective features when application constraints require this conformity. We also reduce the communication overhead in the camera network by transferring only a difference vector, obtained from the extracted features of an object and the reference features within a camera, as an object representation for the association. In order to reduce the number of possible matches per association, we group the objects appearing within a defined time-interval in un-calibrated camera pairs. Such a grouping improves the re-identification, since only those objects that appear within the same time-interval in a camera pair are needed to be associated. For temporal alignment of cameras, we exploit differences between the frame numbers of the detected objects in a camera pair. Finally, in contrast to pairwise camera associations used in literature, we propose a many-to-one camera association method for re-identification, where multiple cameras can be candidates for having generated the previous detections of an object. We obtain camera-invariant matching scores from the scores obtained using the pairwise re-identification approaches. These scores measure the chances of a correct match between the objects detected in a group of cameras. Experimental results on publicly available and in-lab multi-camera image and video datasets show that the proposed methods successfully reduce storage, computation and communication requirements while improving the re-identification rate compared to existing re-identification approaches

Development of a low-cost video imaging system for pavement evaluation

The evaluation of pavement condition is an important part of pavement management. To evaluate a pavement, a distress survey has been performed mainly by manual field inspections. Several automatic pavement evaluation systems have been developed to overcome the drawbacks of field inspections. Automated evaluation systems, however, imply their own limitations in terms of cost, technical problems, and adaptability for pavement management. The main purpose of this research is to develop a low-cost automatic pavement video imaging system. The secondary purpose is the development of techniques to process the collected video images. A low-cost video image-collection system and an in-office system were developed. A video test was implemented on a selected route including various pavement types and several variables. As a result of the test, seven loop tests provided acceptable results to allow image analysis. By using the video camera with fast shutter-speed, it was decided that the survey vehicle could drive at high speed (65mph) while maintaining good picture quality. To evaluate the performance of the system, video and field inspections were performed using two approaches: the Oregon Department of Transportation (ODOT) and Metropolitan Transportation Commissions (MTC) approaches. The inspections were conducted on 107 sample sections. Also, sample still images were digitized for analysis. To conduct a video inspection, the Global Positioning System (GPS) technique was applied for conversion of video mileage to real field mileage. The results of video and field inspections were compared using statistical analyses. The ODOT approach shows a good correlation between video and field inspection for AC sections. In particular, patching and non-load crack indices provide good correlation. The MTC-PMS analysis showed strong linear relationships between video and field inspections. The analysis of crack indices from digitized images shows poor repeatability for each test loop. Using general linear model analysis, variable effects on crack indices were tested. The cost for development and operation of the system was estimated as well as cost for an enhanced prototype system. Discussions on various aspects of the developed system are provided. Finally, summary and conclusion are included as well as recommendations for future system development

Monitoring deforestation and forest degradation linking high-resolution satellite data and field data in the context of REDD+. A case of Tanzania

El principal objetivo de este doctorado es apoyar el desarrollo de un sistema nacional de monitoreo forestal en Tanzania para informar sobre las emisiones actuales e históricas derivadas de la deforestación y la degradación forestal. El marco de la tesis se centra específicamente en el emergente contexto internacional de la iniciativa REDD + (Reducción de Emisiones por Deforestación y Degradación) de las Naciones Unidas, bajo la cual los países pueden obtener subsidios financieros para demostrar que están reduciendo sus emisiones de carbono de tierras forestales con respecto a su práctica histórica reciente. La investigación se centró en cinco áreas de investigación: La parte (1) revisa los antecedentes políticos de REDD +. En él se describen las normas y las opciones a ser abordadas por los países participantes y se demuestran algunos de los problemas técnicos y las opciones que pueden enfrentar y adoptar en la tecnología de teledetección. La parte (2) presenta los resultados del trabajo de campo en Tanzania. Esto incluye la creación de una recopilación rápida de datos sobre el terreno y directrices sobre protocolos para vincular los datos de campo con los datos de teledetección, con el fin de producir mapas de cobertura vegetal y biomasa aérea utilizando imágenes de muy alta resolución. La parte (3) demuestra la mejora en el mapeo de los bosques con una fina resolución espacial y alta frecuencia de adquisiciones con la llegada de los nuevos satélites Sentinel-2. Este potencial se ha probado en un área de bosque seco en el centro de Tanzania. En la parte (4) se evalúa una estimación a gran escala de la biomasa terrestre para toda Tanzania, utilizando una combinación de datos de teledetección y de campo. La capacidad predictiva se investigó comparando los resultados con las mediciones en tierra realizadas por el inventario nacional. La parte (5) investiga la dinámica de la deforestación alrededor de Dar es Salaam, junto con un modelo para inferir la probabilidad futura de deforestación a nivel nacional. La capacidad del modelo de replicar los patrones espaciales de deforestación se evaluó a través de datos del terreno. Entre los principales resultados de este doctorado están que las estimaciones de cambios de cobertura forestal de diferentes fuentes tienen una amplia varianza a nivel nacional y que las estimaciones de emisiones para el proceso REDD + siguen siendo poco fiables. Hay un gran número de opciones a las que se enfrenta un sistema de monitoreo forestal, en términos de definiciones y métodos, que tienen un impacto en la factibilidad de implementación y en los resultados. Se ha demostrado la dificultad de vincular los datos de teledetección con los parámetros forestales de los estudios nacionales, con recomendaciones para mejorar la futura recopilación de datos sobre el terreno. Sin embargo, el uso sinérgico de la teledetección y los datos del estudio sobre el terreno pueden reducir efectivamente los costes de cartografía y monitoreo de los cambios y la degradación de los bosques. Para ello, se encontró que el uso de índices de textura y segmentación de imágenes de satélite de alta resolución espacial (5m) era útil en la producción de mapas de biomasa forestal. Además, la llegada de Sentinel-2 ofrece la oportunidad de analizar datos de media resolución espacial (<20m) en series temporales, especialmente útiles para áreas secas.The main objective of this PhD is to support the development of a national forest monitoring system in Tanzania so as to report on current and historical emissions which derive from deforestation and forest degradation. The framework of the thesis is specifically focused on the emerging international context of the REDD+ (Reducing Emissions from Deforestation and Degradation) initiative from the United Nations, under which countries may obtain financial grants for demonstrating that they are reducing their carbon emissions from forest lands with respect to their recent historical practice. The research focused on five focal areas of research: Part (1) reviews the policy background to REDD+. It outlines the rules and choices to be addressed by participatory countries and demonstrates some of the technical problems and options that they can face and adopt in the remote sensing technology. Part (2) presents the results from the PhD field work in Tanzania. This included the set-up of rapid field data collection and guidelines on protocols to link the field data to the remote sensing data, so as to produce maps of vegetation cover and above ground biomass using very high resolution images. Part (3) demonstrates the improvement to map forests at a fine spatial resolution and high frequency of acquisitions with the arrival of the new Sentinel-2 satellites. This potential has been tested on an area of dry forest in Central Tanzania. Part (4) tests a full scale estimate of above ground biomass for the whole of Tanzania, using a combination of remote sensing and field data. The predictive capability was investigated by comparing the results against ground measurements undertaken by the national inventory. Part (5) investigates the dynamics of deforestation around Dar es Salaam, along with a model to infer future probability of deforestation at the national level. The ability of the model to replicate spatial patterns of deforestation was assessed through ground truth data. Among the main outcome of this PhD is that estimates of forest change from different sources have wide variance at national level and emissions estimates for the REDD+ process remain unreliable. There are a large number of choices facing a forest monitoring system, in terms of forest definitions and methods, which have an impact on the feasibility of implementation and results. The difficulty of linking remote sensing data to the forest parameter from national surveys has been shown, with recommendations to improve future field data collection. However the synergistic use of remote sensing and field survey data can effectively reduce the costs for mapping and monitoring forest changes and forest degradation. For this, the use of high spatial resolution (5m) satellite image segmentation and texture indices was found to be useful in the production of forest biomass maps. Additionally, the arrival of Sentinel-2 data provides the opportunity to analyse medium high spatial resolution data (<20m) in time series, especially useful for dry areas

Geomatics in support of the Common Agriculture Policy

The 2009 Annual Conference was the 15th organised by GeoCAP action of the Joint Research Centre in ISPRA. It was jointly organised with the Italian Agenzia per le erogazioni in agricoltura (AGEA, coordinating organism of the Italian agricultural paying agencies). The Conference covered the 2009 Control with Remote sensing campaign activities and ortho-imagery use in all the CAP management and control procedures. There has been a specific focus on the Land Parcel Identification Systems quality assessment process. The conference was structured over three days ¿ 18th to 20th November. The first day was mainly dedicated to future Common Agriculture Policy perspectives and futures challenges in Agriculture. The second was shared in technical parallel sessions addressing topics like: LPIS Quality Assurance and geodatabases features; new sensors, new software, and their use within the CAP; and Good Agriculture and Environmental Conditions (GAEC) control methods and implementing measures. The last day was dedicated to the review of the 2009 CwRS campaign and the preparation of the 2010 one. The presentations were made available on line, and this publication represents the best presentations judged worthy of inclusion in a conference proceedings aimed at recording the state of the art of technology and practice of that time.JRC.DG.G.3-Monitoring agricultural resource

Feature-based object tracking in maritime scenes.

A monitoring of presence, location and activity of various objects on the sea is essential for maritime navigation and collision avoidance. Mariners normally rely on two complementary methods of the monitoring: radar and satellite-based aids and human observation. Though radar aids are relatively accurate at long distances, their capability of detecting small, unmanned or non-metallic craft that generally do not reflect radar waves sufficiently enough, is limited. The mariners, therefore, rely in such cases on visual observations. The visual observation is often facilitated by using cameras overlooking the sea that can also provide intensified infra-red images. These systems or nevertheless merely enhance the image and the burden of the tedious and error-prone monitoring task still rests with the operator. This thesis addresses the drawbacks of both methods by presenting a framework consisting of a set of machine vision algorithms that facilitate the monitoring tasks in maritime environment. The framework detects and tracks objects in a sequence of images captured by a camera mounted either on a board of a vessel or on a static platform over-looking the sea. The detection of objects is independent of their appearance and conditions such as weather and time of the day. The output of the framework consists of locations and motions of all detected objects with respect to a fixed point in the scene. All values are estimated in real-world units, i. e. location is expressed in metres and velocity in knots. The consistency of the estimates is maintained by compensating for spurious effects such as vibration of the camera. In addition, the framework continuously checks for predefined events such as collision threats or area intrusions, raising an alarm when any such event occurs. The development and evaluation of the framework is based on sequences captured under conditions corresponding to a designated application. The independence of the detection and tracking on the appearance of the sceneand objects is confirmed by a final cross-validation of the framework on previously unused sequences. Potential applications of the framework in various areas of maritime environment including navigation, security, surveillance and others are outlined. Limitations to the presented framework are identified and possible solutions suggested. The thesis concludes with suggestions to further directions of the research presented