Classification of Radar Targets Using Invariant Features

Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under real world conditions that include noise and obscuration

Correlation of partial frames in video matching

Correlating and fusing video frames from distributed and moving sensors is important area of video matching. It is especially difficult for frames with objects at long distances that are visible as single pixels where the algorithms cannot exploit the structure of each object. The proposed algorithm correlates partial frames with such small objects using the algebraic structural approach that exploits structural relations between objects including ratios of areas. The algorithm is fully affine invariant, which includes any rotation, shift, and scaling

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research

Novel Approaches to the Representation and Analysis of 3D Segmented Anatomical Districts

Nowadays, image processing and 3D shape analysis are an integral part of clinical practice and have the potentiality to support clinicians with advanced analysis and visualization techniques. Both approaches provide visual and quantitative information to medical practitioners, even if from different points of view. Indeed, shape analysis is aimed at studying the morphology of anatomical structures, while image processing is focused more on the tissue or functional information provided by the pixels/voxels intensities levels. Despite the progress obtained by research in both fields, a junction between these two complementary worlds is missing. When working with 3D models analyzing shape features, the information of the volume surrounding the structure is lost, since a segmentation process is needed to obtain the 3D shape model; however, the 3D nature of the anatomical structure is represented explicitly. With volume images, instead, the tissue information related to the imaged volume is the core of the analysis, while the shape and morphology of the structure are just implicitly represented, thus not clear enough. The aim of this Thesis work is the integration of these two approaches in order to increase the amount of information available for physicians, allowing a more accurate analysis of each patient. An augmented visualization tool able to provide information on both the anatomical structure shape and the surrounding volume through a hybrid representation, could reduce the gap between the two approaches and provide a more complete anatomical rendering of the subject. To this end, given a segmented anatomical district, we propose a novel mapping of volumetric data onto the segmented surface. The grey-levels of the image voxels are mapped through a volume-surface correspondence map, which defines a grey-level texture on the segmented surface. The resulting texture mapping is coherent to the local morphology of the segmented anatomical structure and provides an enhanced visual representation of the anatomical district. The integration of volume-based and surface-based information in a unique 3D representation also supports the identification and characterization of morphological landmarks and pathology evaluations. The main research contributions of the Ph.D. activities and Thesis are: \u2022 the development of a novel integration algorithm that combines surface-based (segmented 3D anatomical structure meshes) and volume-based (MRI volumes) information. The integration supports different criteria for the grey-levels mapping onto the segmented surface; \u2022 the development of methodological approaches for using the grey-levels mapping together with morphological analysis. The final goal is to solve problems in real clinical tasks, such as the identification of (patient-specific) ligament insertion sites on bones from segmented MR images, the characterization of the local morphology of bones/tissues, the early diagnosis, classification, and monitoring of muscle-skeletal pathologies; \u2022 the analysis of segmentation procedures, with a focus on the tissue classification process, in order to reduce operator dependency and to overcome the absence of a real gold standard for the evaluation of automatic segmentations; \u2022 the evaluation and comparison of (unsupervised) segmentation methods, finalized to define a novel segmentation method for low-field MR images, and for the local correction/improvement of a given segmentation. The proposed method is simple but effectively integrates information derived from medical image analysis and 3D shape analysis. Moreover, the algorithm is general enough to be applied to different anatomical districts independently of the segmentation method, imaging techniques (such as CT), or image resolution. The volume information can be integrated easily in different shape analysis applications, taking into consideration not only the morphology of the input shape but also the real context in which it is inserted, to solve clinical tasks. The results obtained by this combined analysis have been evaluated through statistical analysis

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Pathophysiology of normal pressure hydrocephalus

Normal pressure hydrocephalus (NPH), a CSF circulation disorder, is important as a reversible cause of gait and cognitive disturbance in an aging population. The inconsistent response to CSF shunting is usually attributed to difficulties in differential diagnosis or co-morbidity. Improving outcome depends on an increased understanding of the pathophysiology of NPH. Specifically, this thesis examines the contribution of, and inter-relationship between, the brain parenchyma and CSF circulation in the pathophysiology of NPH. Of the four core studies of the thesis, the first quantifies the characteristics of the CSF circulation and parenchyma in NPH using CSF infusion studies to measure the resistance to CSF absorption and brain compliance. The second study assesses cerebral blood flow (CBF) was using O15-labelled positron emission tomography (PET) with MR co-registration. By performing CSF infusion studies in the PET scanner, CBF at baseline CSF pressure and at a higher equilibrium pressure is measured. Regional changes and autoregulatory capacity are assessed. The final study examines the microstructural integrity of the parenchyma using MR diffusion tensor imaging. These studies confirm the importance of the inter-relationship of the brain parenchyma and CSF circulation. NPH symptomatology and its relationship to the observed regional CBF reductions in the basal ganglia and thalamus are discussed. Regional CBF reductions with increased CSF pressure and the implications for autoregulatory capacity in NPH are considered. The reduction in CBF when CSF was increased was most striking in the periventricular regions. In addition, periventricular structures demonstrated increased diffusivity and decreased anisotropy. The relationship between these changes and mechanisms such as transependymal CSF passage are reviewed. The findings of this thesis support a role of both the CSF circulation and the brain parenchyma in the pathophysiology of NPH. The results have implications for the approach to the management of patients with NPH

Pathophysiology of normal pressure hydrocephalus

Normal pressure hydrocephalus (NPH), a CSF circulation disorder, is important as a reversible cause of gait and cognitive disturbance in an aging population. The inconsistent response to CSF shunting is usually attributed to difficulties in differential diagnosis or co-morbidity. Improving outcome depends on an increased understanding of the pathophysiology of NPH. Specifically, this thesis examines the contribution of, and inter-relationship between, the brain parenchyma and CSF circulation in the pathophysiology of NPH. Of the four core studies of the thesis, the first quantifies the characteristics of the CSF circulation and parenchyma in NPH using CSF infusion studies to measure the resistance to CSF absorption and brain compliance. The second study assesses cerebral blood flow (CBF) was using O15-labelled positron emission tomography (PET) with MR co-registration. By performing CSF infusion studies in the PET scanner, CBF at baseline CSF pressure and at a higher equilibrium pressure is measured. Regional changes and autoregulatory capacity are assessed. The final study examines the microstructural integrity of the parenchyma using MR diffusion tensor imaging. These studies confirm the importance of the inter-relationship of the brain parenchyma and CSF circulation. NPH symptomatology and its relationship to the observed regional CBF reductions in the basal ganglia and thalamus are discussed. Regional CBF reductions with increased CSF pressure and the implications for autoregulatory capacity in NPH are considered. The reduction in CBF when CSF was increased was most striking in the periventricular regions. In addition, periventricular structures demonstrated increased diffusivity and decreased anisotropy. The relationship between these changes and mechanisms such as transependymal CSF passage are reviewed. The findings of this thesis support a role of both the CSF circulation and the brain parenchyma in the pathophysiology of NPH. The results have implications for the approach to the management of patients with NPH

Study on Co-occurrence-based Image Feature Analysis and Texture Recognition Employing Diagonal-Crisscross Local Binary Pattern

In this thesis, we focus on several important fields on real-world image texture analysis and recognition. We survey various important features that are suitable for texture analysis. Apart from the issue of variety of features, different types of texture datasets are also discussed in-depth. There is no thorough work covering the important databases and analyzing them in various viewpoints. We persuasively categorize texture databases ? based on many references. In this survey, we put a categorization to split these texture datasets into few basic groups and later put related datasets. Next, we exhaustively analyze eleven second-order statistical features or cues based on co-occurrence matrices to understand image texture surface. These features are exploited to analyze properties of image texture. The features are also categorized based on their angular orientations and their applicability. Finally, we propose a method called diagonal-crisscross local binary pattern (DCLBP) for texture recognition. We also propose two other extensions of the local binary pattern. Compare to the local binary pattern and few other extensions, we achieve that our proposed method performs satisfactorily well in two very challenging benchmark datasets, called the KTH-TIPS (Textures under varying Illumination, Pose and Scale) database, and the USC-SIPI (University of Southern California ? Signal and Image Processing Institute) Rotations Texture dataset.九州工業大学博士学位論文 学位記番号:工博甲第354号　学位授与年月日:平成25年9月27日CHAPTER 1 INTRODUCTION|CHAPTER 2 FEATURES FOR TEXTURE ANALYSIS|CHAPTER 3 IN-DEPTH ANALYSIS OF TEXTURE DATABASES|CHAPTER 4 ANALYSIS OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 5 CATEGORIZATION OF FEATURES BASED ON CO-OCCURRENCE IMAGE MATRIX|CHAPTER 6 TEXTURE RECOGNITION BASED ON DIAGONAL-CRISSCROSS LOCAL BINARY PATTERN|CHAPTER 7 CONCLUSIONS AND FUTURE WORK九州工業大学平成25年