1,501 research outputs found

    Robust Image Registration with Global Intensity Transformation

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    This paper presents a registration method for images with global illumination variations. The method is based on a joint iterative optimization (geometric and photometric) of the L1 norm of the intensity error. Two strategies are compared to directly find the appropriate intensity transformation within each iteration: histogram specification and the solution obtained by analyzing the necessary optimality conditions. Such strategies reduce the search space of the joint optimization to that of the geometric transformation between the images

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    Department of Biomedical EngineeringImage stitching is a well-known method to make panoramic image which has a wide field-of-view and high resolution. It has been used in various fields such as digital map, gigapixel imaging, and 360-degree camera. However, commercial stitching tools often fail, require a lot of processing time, and only work on certain images. The problems of existing tools are mainly caused by trying to stitch the wrong image pair. To overcome these problems, it is important to select suitable image pair for stitching in advance. Nevertheless, there are no universal standards to judge the good image pairs. Moreover, the derived stitching algorithms cannot be compatible with each other because they conform to their own available criteria. Here, we present universal stitching parameters and their conditions for selecting good image pairs. The proposed stitching parameters can be easily calculated through analysis of corresponding features and homography, which are basic elements in feature-based image stitching algorithm. In order to specify the conditions of the stitching parameters, we devised a new method to calculate stitching accuracy for qualifying stitching results into 3 classesgood, bad, and fail. With the classed stitching results, the values of the stitching parameters could be checked how they differ in each class. Through experiments with large datasets, the most valid parameter for each class is identified as filtering level which is calculated in corresponding feature analysis. In addition, supplemental experiments were conducted with various datasets to demonstrate the validity of the filtering level. As a result of our study, universal stitching parameters can judge the success of stitching, so that it is possible to prevent stitching errors through parameter verification test in advance. This paper can greatly contribute to guide for creating high performance and high efficiency stitching software by applying the proposed stitching conditions.ope

    Digital Image Processing

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    This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

    Color Correction and Depth Based Hierarchical Hole Filling in Free Viewpoint Generation

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    Pixel-level Image Fusion Algorithms for Multi-camera Imaging System

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    This thesis work is motivated by the potential and promise of image fusion technologies in the multi sensor image fusion system and applications. With specific focus on pixel level image fusion, the process after the image registration is processed, we develop graphic user interface for multi-sensor image fusion software using Microsoft visual studio and Microsoft Foundation Class library. In this thesis, we proposed and presented some image fusion algorithms with low computational cost, based upon spatial mixture analysis. The segment weighted average image fusion combines several low spatial resolution data source from different sensors to create high resolution and large size of fused image. This research includes developing a segment-based step, based upon stepwise divide and combine process. In the second stage of the process, the linear interpolation optimization is used to sharpen the image resolution. Implementation of these image fusion algorithms are completed based on the graphic user interface we developed. Multiple sensor image fusion is easily accommodated by the algorithm, and the results are demonstrated at multiple scales. By using quantitative estimation such as mutual information, we obtain the experiment quantifiable results. We also use the image morphing technique to generate fused image sequence, to simulate the results of image fusion. While deploying our pixel level image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also makes it hard to become deployed in system and applications that require real-time feedback, high flexibility and low computation abilit

    Computer-implemented system and method for automated and highly accurate plaque analysis, reporting, and visualization

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    A computer-implemented system and method of intra-oral analysis for measuring plaque removal is disclosed. The system includes hardware for real-time image acquisition and software to store the acquired images on a patient-by-patient basis. The system implements algorithms to segment teeth of interest from surrounding gum, and uses a real-time image-based morphing procedure to automatically overlay a grid onto each segmented tooth. Pattern recognition methods are used to classify plaque from surrounding gum and enamel, while ignoring glare effects due to the reflection of camera light and ambient light from enamel regions. The system integrates these components into a single software suite with an easy-to-use graphical user interface (GUI) that allows users to do an end-to-end run of a patient record, including tooth segmentation of all teeth, grid morphing of each segmented tooth, and plaque classification of each tooth image

    Novel Statistical Methodologies in Analysis of Position Emission Tomography Data: Applications in Segmentation, Normalization, and Trajectory Modeling

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    Position emission tomography (PET) is a powerful functional imaging modality with wide uses in fields such as oncology, cardiology, and neurology. Motivated by imaging datasets from a psoriasis clinical trial and a cohort of Alzheimer\u27s disease (AD) patients, several interesting methodological challenges were identified in various steps of quantitative analysis of PET data. In Chapter 1, we consider a classification scenario of bivariate thresholding of a predictor using an upper and lower cutpoints, as motivated by an image segmentation problem of the skin. We introduce a generalization of ROC analysis and the concept of the parameter path in ROC space of a classifier. Using this framework, we define the optimal ROC (OROC) to identify and assess performance of optimal classifiers, and describe a novel nonparametric estimation of OROC which simultaneous estimates the parameter path of the optimal classifier. In simulations, we compare its performance to alternative methods of OROC estimation. In Chapter 2, we develop a novel method to normalize PET images as an essential preprocessing step for quantitative analysis. We propose a method based on application of functional data analysis to image intensity distribution functions, assuming that that individual image density functions are variations from a template density. By modeling the warping functions using a modified function-on-scalar regression, the variations in density functions due to nuisance parameters are estimated and subsequently removed for normalization. Application to our motivating data indicate persistence of residual variations in standardized image densities. In Chapter 3, we propose a nonlinear mixed effects framework to model amyloid-beta (Aβ), an important biomarker in AD. We incorporate the hypothesized functional form of Aβ trajectory by assuming a common trajectory model for all subjects with variations in the location parameter, and a mixture distribution for the random effects of the location parameter address our empirical findings that some subjects may not accumulate Aβ. Using a Bayesian hierarchical model, group differences are specified into the trajectory parameters. We show in simulation studies that the model closely estimates the true parameters under various scenarios, and accurately estimates group differences in the age of onset
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