2x1D Image Registration and Comparison

This paper presents a novel 2x1D phase correlation based image registration method for verification of printer emulator output. The method combines the basic phase correlation technique and a modified 2x1D version of it to achieve both high speed and high accuracy. The proposed method has been implemented and tested using images generated by printer emulators. Over 97% of the image pairs were registered correctly, accurately dealing with diverse images with large translations and image cropping

A Computational Framework for the Structural Change Analysis of 3D Volumes of Microscopic Specimens

Glaucoma, commonly observed with an elevation in the intraocular pressure level (IOP), is one of the leading causes of blindness. The lamina cribrosa is a mesh-like structure that provides axonal support for the optic nerves leaving the eye. The changes in the laminar structure under IOP elevations may result in the deaths of retinal ganglion cells, leading to vision degradation and loss. We have developed a comprehensive computational framework that can assist the study of structural changes in microscopic structures such as lamina cribrosa. The optical sectioning property of a confocal microscope facilitates imaging thick microscopic specimen at various depths without physical sectioning. The confocal microscope images are referred to as optical sections. The computational framework developed includes: 1) a multi-threaded system architecture for tracking a volume-of-interest within a microscopic specimen in a parallel computation environment using a reliable-multicast for collective-communication operations 2) a Karhunen-Loève (KL) expansion based adaptive noise prefilter for the restoration of the optical sections using an inverse restoration method 3) a morphological operator based ringing metric to quantify the ringing artifacts introduced during iterative restoration of optical sections 4) a l2 norm based error metric to evaluate the performance of optical flow algorithms without a priori knowledge of the true motion field and 5) a Compute-and-Propagate (CNP) framework for iterative optical flow algorithms. The realtime tracking architecture can convert a 2D-confocal microscope into a 4D-confocal microscope with tracking. The adaptive KL filter is suitable for realtime restoration of optical sections. The CNP framework significantly improves the speed and convergence of the iterative optical flow algorithms. Also, the CNP framework can reduce the errors in the motion field estimates due to the aperture problem. The performance of the proposed framework is demonstrated on real-life image sequences and on z-Stack datasets of random cotton fibers and lamina cribrosa of a cow retina with an experimentally induced glaucoma. The proposed framework can be used for routine laboratory and clinical investigation of microstructures such as cells and tissues, for the evaluation of complex structures such as cornea and has potential use as a surgical guidance tool

Anisotropic Neutron Imaging

Anisotropic neutron imaging presents a unique method to process images in order to produce a human-readable, singular, isotropic image from a set of anisotropic neutron images. These images were created using a Sӧller-slit collimator in a rotating device to change the azimuthal orientation of the slits with respect to the imaging plane. A multi-level, 2D-discreet wavelet transform (2D-DWT) was used to extract the information contained within each image and fuse the data into an isotropic resolution image. The footprint of the experimental system is small when compared to other neutron radiography facilities of comparable L/D and has the advantage of a relatively low acquisition time while maintaining high resolution image capture. The 2D-DWT algorithm developed within this dissertation is able to enhance the sharpness of the edges within the final image and remove the artifacts created by the slit-type collimator, which increased human readability in the test circumstanc

Statistical Methods for Image Registration and Denoising

This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the Barankin bound is a better predictor of shift estimator performance. The research also looks at the related problem of single-frame image denoising using block-based methods. The research introduces three algorithms that operate by identifying regions of interest within a noise-corrupted image and then generating noise free estimates of the regions as averages of similar regions in the image

Application of Wavelets-based SVM Classification for Automated Fault Diagnosis and Prognosis of Mechanical Systems

Anwendung der Wavelet-basierte SVM Klassifizierung für die automatisierte Fehlerdiagnose und -prognose mechanischer Systeme In dieser Arbeit werden Techniken der Mustererkennung auf verschiedene Problemstellungen der Fehlerdiagnose und -prognose angewendet. Die untersuchten Anwendungen stellen reale industrielle Anwendungen dar, bei denen verschiedene Messeigenschaften (wie zyklische, impulsive, und periodische Signale), verschiedene Charakteristik der Erkennungsobjektiven (wie kumulativ und einmalige Ereignisse), verschiedene Betriebsbedingungen und -parameter der Maschine, und verschiedene Fehler und Erkennungssystemanforderungen (wie Verschleiß, Riss, und Objekterkennung; Systemzustand und Restlebensdauer) die modulare Mustererkennungsverfahren und -techniken erfordern. Verschiedene Ansätze werden untersucht und angewendet, wie Support Vector Machine (SVM), Continuous Wavelet-Transform (CWT),Wavelet Packet Transform (WPT) und Diskrete Wavelet-Transform (DWT), und viele Konzepte und Lösungen werden vorgeschlagen und überprüft, um ein zuverlässiges Zustandsüberwachungssystem zu erreichen, dass die Instandhaltungsplanung der Maschine unterstützt und die Produktionsqualität und Produktionskosten verbessert. In der ersten untersuchten Anwendung in dieser Arbeit wird ein Ansatz für die Entwicklung eines Fehlerdiagnose- und -prognosesystems vorgestellt. Das System wird als Vorwarnmodul verwendet, um die Notwendigkeit für das Ersetzen von Verschleißteilen von Produktionsmaschinen zu erkennen und die Restlebensdauer des überwachten Teils zu bewerten. In der zweiten untersuchten Anwendung wird ein Produktionsverfahren überwacht. Ziel ist die Erkennung eines Objektes mit einer möglichst geringen Fehlalarmrate. Die Signale beinhalten nichtstationäre, impulsartige bzw. einmalige Ereignisse. Ein weiteres Merkmal der Sensorcluster-Signale ist die nicht gleichzeitige Erzeugung von Ereignissen, die die Verwendung von geeigneten Entscheidungsfusionstechniken erfordert. In der letzten untersuchten Anwendung, werden modell- und signalbasierte Verfahren für die Risserkennung und Prognose in rotierenden Maschinen untersucht, um eine Vorwarnung für Rotor-Risse zu erreichen für Online- Überwachung in Turbomaschinen. Die angetroffenen Signale sind periodische Schwingungssignale mit kumulativen Auswirkungen der Fehlerereignisse. Offene Fragen stellen sich bei den Themen Zustandsbewertung, Fehlerschweregrad und Restlebensdauer, basierend auf spezifischen Sensordaten mit besonderen anwendungsorientierten Eigenschaften. Diese Arbeit befasst sich mit diesen offenen Fragen, um ein zuverlässiges Zustandsüberwachungssystem zu erreichen. Es kann festgestellt werden, dass Wavelets und SVM sehr nützliche Werkzeuge für die Merkmalsextraktion und Klassifikation im Bereich der Zustandsüberwachung sind. Der Merkmalsraum von SVM ist nützlich für die Bewertung der verbleibenden Lebensdauer. Allerdings zeigt sich ebenfalls, dass angesichts der Herausforderungen anwendungsorientierte Lösungen gefunden werden müssen.In this thesis, the application of pattern recognition techniques is considered for different kinds of fault diagnosis and prognosis problems and applications. The investigated applications represent real industrial applications, in which different measurement characteristics (such as cyclic, impulsive, and periodic signals), different recognition objective characteristics (such as accumulative and one-time events), different operational conditions and parameters of the machine, and different faults and detection system requirements (such as wear, crack, and object detection; System state and remaining life time) are challenging the existence of modular pattern recognition procedures and techniques. Different approaches are investigated and applied such as Support Vector Machine (SVM), Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), and Continuous Wavelet Transform (CWT), and many concepts and solutions are proposed and verified, in order to achieve a reliable condition monitoring system, which supports the maintenance planning of the machine and adds value to the production quality and cost. In the first investigated application in this thesis, an approach for developing a fault diagnosis and prognosis system is presented. The system is used as a prewarning module to detect the necessity for replacing wear parts of production machines and to evaluate the remaining life time of the supervised part. The sensor signals encountered for processing are nondeterministic with cyclic nature related to the operation cycle of the machine. In the second investigated application, the goal is to monitor a production process for online detection of a target object with the lowest possible false alarm rate. The signals encountered in the system of this work are characterized with nonstationary impulsive one-time events representing the goal object. Another characteristic of the sensor cluster signals is the partly simultaneous stimulation of events which requires the use of suitable decision fusion techniques. In the last investigated application, two main approaches used for crack detection and prediction in rotating machinery; model based and signal based, are investigated, in order to achieve a prewarning technique for rotor cracks to be applied for online monitoring in turbo-machinery. The signals encountered are periodic vibration signals with accumulative impact of the fault incident. Open questions arise in the issues of state evaluation, severity estimation, and remaining life time prediction, based on specific sensor data with particular applicationoriented characteristics. This work deals with these open questions, in order to achieve a reliable condition monitoring system. As a general conclusion of the work, it can be stated that Wavelets and SVM are reliable tools for feature extraction and classification in the field of condition monitoring, and the feature space of SVM is useful for remaining life prediction. However; specific application oriented Solutions and tricks are necessary, considering the diversity of fault diagnosis and prognosis problems and difficulties

Quantitative digital image processing in fringe analysis and particle image velocimetry (PIV)

This thesis concerns the application of Quantitative Digital Image Pro­cessing to some problems in the domain of Optical Engineering. The applications addressed are those of automatic two dimensional phase unwrapping and the analysis of images from high speed particle image displacement velocimetry. The first application involves subdivision of the two dimensional image of a wrapped phase map into small two dimensional areas or tiles, which are unwrapped individually, in order that discontinuities may be localised to small areas. In this case the discontinuities have a contained effect on the unwrapped phase solution. The concept of minimum spanning trees, from Graph Theory, is employed to minimise the effect of such local discontinuities by computation of an un­wrapping path which avoids areas likely to be discontinuous in a probabilistic manner. This approach is implemented over two hierarchical levels, the first level identifying pixel level discontinuities such as spike noise, the second ad­dressing larger scale discontinuities which may not be detected by pixel level comparisons, but which can be detected by comparison of the local solutions of image areas larger than the pixel. The second application is in the area of Particle Image Displacement Velocimetry (PIDV). A digital processing method is developed for high speed PIDV. In high speed PIDV the seeding is sparsely distributed. This method attempts to pair individual particle images, rather than statistically average the positions of a large number of particle images as is the case with other analysis methods. The digital processing method is suitable for use with Video PIDV whose feasibility has recently been demonstrated

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Medical Image Modality Synthesis and Resolution Enhancement Based on Machine Learning Techniques

To achieve satisfactory performance from automatic medical image analysis algorithms such as registration or segmentation, medical imaging data with the desired modality/contrast and high isotropic resolution are preferred, yet they are not always available. We addressed this problem in this thesis using 1) image modality synthesis and 2) resolution enhancement. The first contribution of this thesis is computed tomography (CT)-tomagnetic resonance imaging (MRI) image synthesis method, which was developed to provide MRI when CT is the only modality that is acquired. The main challenges are that CT has poor contrast as well as high noise in soft tissues and that the CT-to-MR mapping is highly nonlinear. To overcome these challenges, we developed a convolutional neural network (CNN) which is a modified U-net. With this deep network for synthesis, we developed the first segmentation method that provides detailed grey matter anatomical labels on CT neuroimages using synthetic MRI. The second contribution is a method for resolution enhancement for a common type of acquisition in clinical and research practice, one in which there is high resolution (HR) in the in-plane directions and low resolution (LR) in the through-plane direction. The challenge of improving the through-plane resolution for such acquisitions is that the state-of-art convolutional neural network (CNN)-based super-resolution methods are sometimes not applicable due to lack of external LR/HR paired training data. To address this challenge, we developed a self super-resolution algorithm called SMORE and its iterative version called iSMORE, which are CNN-based yet do not require LR/HR paired training data other than the subject image itself. SMORE/iSMORE create training data from the HR in-plane slices of the subject image itself, then train and apply CNNs to through-plane slices to improve spatial resolution and remove aliasing. In this thesis, we perform SMORE/iSMORE on multiple simulated and real datasets to demonstrate their accuracy and generalizability. Also, SMORE as a preprocessing step is shown to improve segmentation accuracy. In summary, CT-to-MR synthesis, SMORE, and iSMORE were demonstrated in this thesis to be effective preprocessing algorithms for visual quality and other automatic medical image analysis such as registration or segmentation

Panoramic Video Stitching

Digital camera and smartphone technologies have made high quality images and video pervasive and abundant. Combining or stitching collections of images from a variety of viewpoints into an extended panoramic image is a common and popular function for such devices. Extending this functionality to video however, poses many new challenges due to the demand for both spatial and temporal continuity. Multi-view video stitching (also called panoramic video stitching) is an emerging, common research area in computer vision, image/video processing and computer graphics and has wide applications in virtual reality, virtual tourism, surveillance, and human computer interaction. In this thesis, I will explore the technical and practical problems in the complete process of stitching a high-resolution multiview video into a high-resolution panoramic video. The challenges addressed include video stabilization, efficient multi-view video alignment and panoramic video stitching, color correction, and blurred frame detection and repair. Specifically, I propose a continuity aware Kalman filtering scheme for rotation angles for video stabilization and jitter removal. For efficient stitching of long, high-resolution panoramic videos, I propose constrained and multigrid SIFT matching schemes, concatenated image projection and warping and min-space feathering. These three approaches together can greatly reduce the computational time and memory requirement in panoramic video stitching, which makes it feasible to stitch high-resolution (e.g., 1920x1080 pixels) and long panoramic video sequences using standard workstations. Color correction is the emphasis of my research. On this topic I first performed a systematic survey and performance evaluation of nine state of the art color correction approaches in the context of two-view image stitching. My evaluation work not only gives useful insights and conclusions about the relative performance of these approaches, but also points out the remaining challenges and possible directions for future color correction research. Based on the conclusions from this evaluation work, I proposed a hybrid and scalable color correction approach for general n-view image stitching, and designed a two-view video color correction approach for panoramic video stitching. For blurred frame detection and repair, I have completed preliminary work on image partial blur detection and classification, in which I proposed a SVM-based blur block classifier using improved and new local blur features. Then, based on partial blur classification results, I designed a statistical thresholding scheme for blurred frame identification. For the detected blurred frames, I repaired them using polynomial data fitting from neighboring unblurred frames. Many of the techniques and ideas in this thesis are novel and general solutions to the technical or practical problems in panoramic video stitching. At the end of this thesis, I conclude the contributions made by this thesis to the research and popularization of panoramic video stitching, and describe those open research issues