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作者： Maxwell Miao Simon Fraser University

学位级别：硕士

In recent years, Optical Coherence Tomography (OCT) has become one of the dominant imaging technologies for ophthalmic diagnostics and vision research. The fast and high- resolution cross-sectional data that OCT provides has brought a new possibility in the role of intra-operative imaging. However, existing commercial OCT systems lack the automated real-time functionality for providing immediate feedback of changes in anatomical configuration as the result of surgical actions. The predominant reason for lacking such functionality is because high complexity algorithms are hard to implement in real-time imaging due to their computationally expensive nature. In this thesis, we will present a Graphics processing Unit (GPU) accelerated retinal layer segmentation for real-time intra-operative imaging applications. Modern GPUs has emerged as a strong tool for mass computation in scientific researches. The computational power of the GPU outpaces Central processing Unit (CPU) significantly when the processing task is parallelizable. image segmentation is a computationally expensive algorithm and traditionally implemented in sequential instructions. An example of a parallelizable segmentation algorithm is Push-Relabel (PR) Graph-Cut(GC), which can be implemented using GPU. The GPU Retinal Segmentation (GRS) presented in this thesis is built upon such an algorithm. To ensure the run time of the GRS meets the real-time requirement for its application, multiple GPUs are used to accelerate the segmentation processing further in parallel. As a result of using GRS, we were able to achieve the visualization of the retinal thickness measurement and the enhancement of retinal vasculature networks in real-time.

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Optimizing Convergence Rates of Alternating Minimization Reconstruction algorithms for Real-Time Explosive Detection applications

Optimizing Convergence Rates of Alternating Minimization Rec...

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SPIE Anomaly Detection and imaging with x-Rays (ADIx) Conference

作者： Bosch, Carl Degirmenci, Soysal Barlow, Jason Mesika, Assaf Politte, David G. O'Sullivan, Joseph A. SureScan Corp 2507 Wayne St Endicott NY 13760 USA Washington Univ Dept Elect & Syst Engn 1 Brookings Dr St Louis MO 63130 USA Washington Univ Sch Med Mallinckrodt Inst Radiol 510 South Kingshighway Blvd St Louis MO 63110 USA

ISBN: (纸本)9781510600881

x-ray computed tomography reconstruction for medical, security and industrial applications has evolved through 40 years of experience with rotating gantry scanners using analytic reconstruction techniques such as filtered back projection (FBP). In parallel, research into statistical iterative reconstruction algorithms has evolved to apply to sparse view scanners in nuclear medicine, low data rate scanners in Positron Emission Tomography (PET) [5, 7, 10] and more recently to reduce exposure to ionizing radiation in conventional x-ray CT scanners. Multiple approaches to statistical iterative reconstruction have been developed based primarily on variations of expectation maximization (EM) algorithms. The primary benefit of EM algorithms is the guarantee of convergence that is maintained when iterative corrections are made within the limits of convergent algorithms. The primary disadvantage, however is that strict adherence to correction limits of convergent algorithms extends the number of iterations and ultimate timeline to complete a 3D volumetric reconstruction. Researchers have studied methods to accelerate convergence through more aggressive corrections [1], ordered subsets [1, 3, 4, 9] and spatially variant image updates. In this paper we describe the development of an AM reconstruction algorithm with accelerated convergence for use in a real-time explosive detection application for aviation security. By judiciously applying multiple acceleration techniques and advanced GPU processing architectures, we are able to perform 3D reconstruction of scanned passenger baggage at a rate of 75 slices per second. Analysis of the results on stream of commerce passenger bags demonstrates accelerated convergence by factors of 8 to 15, when comparing images from accelerated and strictly convergent algorithms

关键词： Reconstruction regularization computed tomography convergence explosive detection accelerated convergence

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Towards a unified multiresolution vision model for autonomous ground robots

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ROBOTICS AND AUTONOMOUS systems 2016年第PartB期75卷 221-232页

作者： Sustersic, John Wyble, Brad Advani, Siddharth Narayanan, Vijaykrishnan Penn State Univ Appl Res Lab University Pk PA 16804 USA Penn State Univ Dept Psychol University Pk PA 16804 USA Penn State Univ Dept Comp Sci & Engn University Pk PA 16804 USA

While remotely operated unmanned vehicles are increasingly a part of everyday life, truly autonomous robots capable of independent operation in dynamic environments have yet to be realized - particularly in the case of ground robots required to interact with humans and their environment. We present a unified multiresolution vision model for this application designed to provide the wide field of view required to maintain situational awareness and sufficient visual acuity to recognize elements of the environment while permitting feasible implementations in real-time vision applications. The model features a kind of color-constant processing through single-opponent color channels and contrast invariant oriented edge detection using a novel implementation of the Combination of Receptive Fields model. The model provides color and edge-based salience assessment, as well as a compressed color image representation suitable for subsequent object identification. We show that bottom-up visual saliency computed using this model is competitive with the current state-of-the-art while allowing computation in a compressed domain and mimicking the human visual system with nearly half (45%) of computational effort focused within the fovea. This method reduces storage requirement of the image pyramid to less than 5% of the full image, and computation in this domain reduces model complexity in terms of both computational costs and memory requirements accordingly. We also quantitatively evaluate the model for its application domain by using it with a camera/lens system with a 185 field of view capturing 3.5M pixel color images by using a tuned salience model to predict human fixations. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Computer vision Gaussian pyramid Visual salience parallel algorithms

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Fast and Scalable Architectures and algorithms for the Computation of the Forward and Inverse Discrete Periodic Radon Transform with applications to 2D Convolutions and Cross-Correlations

Fast and Scalable Architectures and Algorithms for the Compu...

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作者： Carranza, Cesar The University of New Mexico

学位级别：Ph.D.

The Discrete Radon Transform (DRT) is an essential component of a wide range of applications in image processing, e.g. image denoising, image restoration, texture analysis, line detection, encryption, compressive sensing and reconstructing objects from projections in computed tomography and magnetic resonance imaging. A popular method to obtain the DRT, or its inverse, involves the use of the Fast Fourier Transform, with the inherent approximation/rounding errors and increased hardware complexity due the need for floating point arithmetic implementations. An alternative implementation of the DRT is through the use of the Discrete Periodic Radon Transform (DPRT). The DPRT also exhibits discrete properties of the continuous-space Radon Transform, including the Fourier Slice Theorem and the convolution property. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions O(N3) and the need for a large number of memory accesses. This PhD dissertation introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: (i) a parallel array of fixed-point adder trees, (ii) circular shift registers to remove the need for accessing external memory components when selecting the input data for the adder trees, and (iii) an image block-based approach to DPRT computation that can fit the proposed architecture to available resources, and as a result, for an NxN image (N prime), the proposed approach can compute up to N 2 additions per clock cycle. Compared to previous approaches, the scalable approach provides the fastest known implementations for different amounts of computational resources. For the fastest case, I introduce optimized architectures that can compute the DPRT and its inverse in just 2N +(log2 N)+1 and 2N +3(log2 N)+B+2 clock cycles respectively, where B is the number of bits used to represent each input pixel. In comparison, the prior state of the art method required N2 +N +1 cl

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GPGPU based Implementation of BLiiNDS-ii NR-IQA. 1

GPGPU based Implementation of BLIINDS-II NR-IQA. 1

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作者： Yadav, Aman.0 Arizona State University.bComputer Science.0

学位级别：M.S.

The technological advances in the past few decades have made possible creation and consumption of digital visual content at an explosive rate. Consequently, there is a need for efficient quality monitoring systems to ensure minimal degradation of images and videos during various processing operations like compression, transmission, storage etc. Objective image Quality Assessment (IQA) algorithms have been developed that predict quality scores which match well with human subjective quality assessment. However, a lot of research still remains to be done before IQA algorithms can be deployed in real world systems. Long runtimes for one frame of image is a major hurdle. Graphics processing Units (GPUs), equipped with massive number of computational cores, provide an opportunity to accelerate IQA algorithms by performing computations in parallel. Indeed, General Purpose Graphics processing Units (GPGPU) techniques have been applied to a few Full Reference IQA algorithms which fall under the. We present a GPGPU implementation of Blind image Integrity Notator using DCT Statistics (BLiiNDS-ii), which falls under the No Reference IQA algorithm paradigm. We have been able to achieve a speedup of over 30x over the previous CPU version of this algorithm. We test our implementation using various distorted images from the CSIQ database and present the performance trends observed. We achieve a very consistent performance of around 9 milliseconds per distorted image, which made possible the execution of over 100 images per second (100 fps).

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Fast and Scalable Architectures and algorithms for the Computation of the Forward and Inverse Discrete Periodic Radon Transform with applications to 2D Convolutions and Cross-Correlations

Fast and Scalable Architectures and Algorithms for the Compu...

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作者： Cesar Carranza University of New Mexico

学位级别：博士

The Discrete Radon Transform (DRT) is an essential component of a wide range of applications in image processing, e.g. image denoising, image restoration, texture analysis, line detection, encryption, compressive sensing and reconstructing objects from projections in computed tomography and magnetic resonance imaging. A popular method to obtain the DRT, or its inverse, involves the use of the Fast Fourier Transform, with the inherent approximation/rounding errors and increased hardware complexity due the need for floating point arithmetic implementations. An alternative implementation of the DRT is through the use of the Discrete Periodic Radon Transform (DPRT). The DPRT also exhibits discrete properties of the continuous-space Radon Transform, including the Fourier Slice Theorem and the convolution property. Unfortunately, the use of the DPRT has been limited by the need to compute a large number of additions O(N^3) and the need for a large number of memory accesses. This PhD dissertation introduces a fast and scalable approach for computing the forward and inverse DPRT that is based on the use of: (i) a parallel array of fixed-point adder trees, (ii) circular shift registers to remove the need for accessing external memory components when selecting the input data for the adder trees, and (iii) an image block-based approach to DPRT computation that can fit the proposed architecture to available resources, and as a result, for an NxN image (N prime), the proposed approach can compute up to N^2 additions per clock cycle. Compared to previous approaches, the scalable approach provides the fastest known implementations for different amounts of computational resources. For the fastest case, I introduce optimized architectures that can compute the DPRT and its inverse in just 2N +ceil(log2 N)+1 and 2N +3(log2 N)+B+2 clock cycles respectively, where B is the number of bits used to represent each input pixel. In comparison, the prior state of the art method required N^2 +N

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Proceedings of SPIE-IS and T Electronic imaging - image processing: algorithms and systems x;and parallel processing for imaging applications ii

Proceedings of SPIE-IS and T Electronic Imaging - Image Proc...

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image processing: algorithms and systems x;and parallel processing for imaging applications ii

ISBN: (纸本)9780819489425

The proceedings contain 52 papers. The topics discussed include: analysis of different image-based biofeedback models for improving cycling performances;textured areas detection and segmentation in circular harmonic functions domain;feature extraction from ladar data using modified GPCA;image inpainting using cubic spline-based edge reconstruction;super-resolution image reconstruction with edge adaptive weight in video sequence;multi-scale image enhancement using a second derivative-like measure of contrast;GPGPU-based surface inspection from structured white light;plane-dependent error diffusion on a GPU;intensity constrained flat kernel filter for local dark feature suppression: application to removal of hair artifacts in dermatoscopic images;new decision support tool for acute lymphoblastic leukemia classification;and multi-resolution analysis for region of interest extraction in thermographic nondestructive evaluation.

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Real time ultrasound image denoising using NVIDIA CUDA

Real time ultrasound image denoising using NVIDIA CUDA

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International Conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： Amira Hadj Fredj Jihene Malek Faculty of Sciences Monastir University Monastir Tunisia

ISBN: (纸本)9781467385275

image filtering is a process of reducing noise which degrades the performance of image processing. In some applications such as segmentation or classification, denoising has been designed to smooth the homogeneous areas while keeping and enhancing the edges. In several applications such as video analysis, image-guided surgical interventions or visual servoing, real-time denoising is needed. The devoted filter was suggested to adjust the filter of anisotropic diffusion to the features of the multiplicative noise existing within the ultrasound video and also to make easier the filtering process. The purpose of this study is to decrease the processing time implementation of the diffusion function using parallel processors through the optimization of the Graphics processor unit (GPU). The results show that the suggested method is very effictive in terms of real-time compared to a standard central processing unit (CPU) implementation. Our proposed model magnifies the acceleration of the image filtering to 7x compared to the sequential calculation.

关键词： Graphics processing units Ultrasonic imaging Streaming media Real-time systems Speckle image edge detection Kernel

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parallel Implementation of FCM-Based Volume Segmentation of 3D images

Parallel Implementation of FCM-Based Volume Segmentation of ...

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IEEE/ACS International Conference of Computer systems and applications

作者： Shadi AlZu'bi Moahmmed A. Shehab Mahmoud Al-Ayyoub Elhadj Benkhelifa Yaser Jararweh Zaytoonah University of Jordan Jordan University of Science and Technology Staffordshire University

ISBN: (纸本)9781509043217

parallel programming has many benefits that can help developers and researchers to improve the performance of some algorithms to become more efficient in real life. This is especially true for systems involving medical images. image segmentation for volume extraction is a famous segmentation process that takes long time to finish execution. In this paper, we consider a new version of the Fuzzy C-Means (FCM) segmentation algorithm (known as IT2FPCM) and provide a parallel implementation of it that is 12x time faster than the sequential implementation. The considered algorithm is based on Interval Type-2 FCM and combines fuzzy and possibilistic ideas in order to obtain higher accuracy. We conduct our experiments using two different machines and the results show that the improvement gains for both machines 11x and 12x, respectively.

关键词： Clustering algorithms Mathematical model image segmentation Three-dimensional displays Graphics processing units Phase change materials Linear programming

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A Novel Framework for Interactive Visualization and Analysis of Hyperspectral image Data

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JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2016年第1期2016卷 1-17页

作者： Jordan, Johannes Angelopoulou, Elli Maier, Andreas Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany

Multispectral and hyperspectral images are well established in various fields of application like remote sensing, astronomy, and microscopic spectroscopy. In recent years, the availability of new sensor designs, more powerful processors, and high-capacity storage further opened this imaging modality to a wider array of applications like medical diagnosis, agriculture, and cultural heritage. This necessitates new tools that allow general analysis of the image data and are intuitive to users who are new to hyperspectral imaging. We introduce a novel framework that bundles new interactive visualization techniques with powerful algorithms and is accessible through an efficient and intuitive graphical user interface. We visualize the spectral distribution of an image via parallel coordinates with a strong link to traditional visualization techniques, enabling new paradigms in hyperspectral image analysis that focus on interactive raw data exploration. We combine novel methods for supervised segmentation, global clustering, and nonlinear false-color coding to assist in the visual inspection. Our framework coined Gerbil is open source and highly modular, building on established methods and being easily extensible for application-specific needs. It satisfies the need for a general, consistent software framework that tightly integrates analysis algorithms with an intuitive, modern interface to the raw image data and algorithmic results. Gerbil finds its worldwide use in academia and industry alike with several thousand downloads originating from 45 countries.

关键词： HYPERSPECTRAL imaging systems SOFTWARE frameworks COMPUTER visualization SEGMENTATION (image processing) image analysis

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