A Survey of Multithreading Image Analysis
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paper_title: Digital image forgery detection through data embedding in spatial domain and cellular automata

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In this paper we present a new algorithm to detect digital image forgery based on cellular automata and data embedding in spatial domain. The original JPEG image will be partitioned into some regions. We use region-based segmentation to specifying the desired regions of interest from the input image. First we extract the visual attributes of the original image and achieve the statistical information for the selected region. Then we apply linear cellular automata rules to create a robust cipher key from these values. We embed the cipher key into the spatial domain to authenticate and validate the original image. The proposed algorithm is applied on 100 numbers of grayscale images (size 800 × 600). The results have demonstrated the robustness and stable time complexity of the proposed method.
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paper_title: Metacarpal Bones Localization in X-ray Imagery Using Particle Filter Segmentation

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Statistical methods such as sequential Monte Carlo Methods were proposed for detection, segmentation and tracking of objects in digital images. A similar approach, called Shape Particle Filters was introduced for the segmentation of vertebra, lungs and hearts [1]. In this contribution, a global shape and a local appearance model are derived from specific object annotated X-ray images of the metacarpal bones. In the test data a unique labeling of the bone boundary and the background points and a manual annotation is given. Using a set of local features (Haar-like) in the neighborhood of each pixel a probabilistic pixel classifier is built using the random forest algorithm. To fit the shape model to a new image, a label probability map is extracted and then the optimal shape is obtained by maximizing the probability of each landmark with the Differential Evolution algorithm.
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paper_title: Human Cell Detection in Microscopic Images through Discrete Cosine Transform and Gaussian Mixture Model

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Automatic detection of human cell is still one of the most common investigation methods that may be used as part of a computer aided medical decision making system(1). In this paper a statistical method based on Gaussian Mixture Model is applied to human cell detection in microscopic images(2). 120 normal microscopic images of human cell from our research laboratory were used for analysis. Texture and grayscale features extracted from blocks of these images are given to Gaussian Mixture Model as input. It is used to model this data into three classes which are cell, extra cellular space and cell membrane (3). Our proposed algorithm is applied on a sample dataset and experimental results show that this model is both accurate and fast with overall detection rate of around 91.23%. Error rate for cell detection was 1.82%.
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paper_title: RGB digital image forgery detection using Singular Value Decomposition and One Dimensional Cellular Automata

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A reliable approach for RGB digital image forgery detection is presented in this paper. Our method is based on the Singular Value Decomposition (SVD) of the original image in which the features of the image is extracted and then pushed into cellular automata to generate the Robust Secret Key for the image authentication. SVD is used as a strong mathematical tool to decompose the RGB Digital Image into three orthogonal matrices and create features that are rotation invariant. First we focus on a specific layer of RGB input image (i.e. red layer or red matrix) and apply SVD to calculate the Singular Values as well as Right and Left Singular Vectors. Then we generate an array to hold the values of the Singular Values and the Right and Left Singular Vectors and then push the values of the array into the One-Dimensional Cellular Automata to generate the Robust Secret key that can be used to protect the image against the forgery. Next, we embed the output of the cellular automata into the spatial domain of another layer, to authenticate the original image. We applied our algorithm on hundreds of 800 × 800, RGB digital images to test the behavior of our algorithm. The experimental results have illustrated the robustness, visual quality and reliability of our proposed algorithm.
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paper_title: 3D Microscopy Vision Using Multiple View Geometry and Differential Evolutionary Approaches

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The Scanning Electron Microscope (SEM) as 2D imaging equipment has been widely used in biology and material sciences to determine the surface attributes of a microscopic object. Having 3D surfaces from SEM images would provide true anatomic shapes of micro samples which allow for quantitative measurements and informative visualization of the systems being investigated. In this contribution, we present a Differential Evolutionary (DE) approach for both SEM extrinsic calibration and 3D surface reconstruction. We show that the SEM extrinsic calibration and its 3D shape model can be accurately estimated in a global optimization platform. Several experiments from various perspectives are performed on real and synthetic data to validate the speed, reliability and accuracy of the proposed system. The present work is expected to stimulate more interest and draw attentions from the computer vision community to the fast-growing SEM application area.
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paper_title: A Survey of Graph Theoretical Approaches to Image Segmentation

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Image segmentation is a fundamental problem in computer vision. Despite many years of research, general purpose image segmentation is still a very challenging task because segmentation is inherently ill-posed. Among different segmentation schemes, graph theoretical ones have several good features in practical applications. It explicitly organizes the image elements into mathematically sound structures, and makes the formulation of the problem more flexible and the computation more efficient. In this paper, we conduct a systematic survey of graph theoretical methods for image segmentation, where the problem is modeled in terms of partitioning a graph into several sub-graphs such that each of them represents a meaningful object of interest in the image. These methods are categorized into five classes under a uniform notation: the minimal spanning tree based methods, graph cut based methods with cost functions, graph cut based methods on Markov random field models, the shortest path based methods and the other methods that do not belong to any of these classes. We present motivations and detailed technical descriptions for each category of methods. The quantitative evaluation is carried by using five indices - Probabilistic Rand (PR) index, Normalized Probabilistic Rand (NPR) index, Variation of Information (VI), Global Consistency Error (GCE) and Boundary Displacement Error (BDE) - on some representative automatic and interactive segmentation methods.
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paper_title: Digital Images Encryption in Frequency Domain Based on DCT and One Dimensional Cellular Automata

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We are living in an age where security of digital information like digital images is becoming more important in many aspects and the tranmission of digital images is increasing over the internal and international computer networks. The protection of digital images from unauthorized access is the main purpose of this paper.
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paper_title: Fast Range Image Segmentation and Smoothing using Approximate Surface Reconstruction and Region Growing

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Decomposing sensory measurements into relevant parts is a fundamental prerequisite for solving complex tasks, e.g., in the field of mobile manipulation in domestic environments. In this paper, we present a fast approach to surface reconstruction in range images by means of approximate polygonal meshing. The obtained local surface information and neighborhoods are then used to 1) smooth the underlying measurements, and 2) segment the image into planar regions and other geometric primitives. An evaluation using publicly available data sets shows that our approach does not rank behind state-of-the-art algorithms while allowing to process range images at high frame rates.
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paper_title: Building Rome in a day

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We present a system that can reconstruct 3D geometry from large, unorganized collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo-sharing sites. Our system is built on a set of new, distributed computer vision algorithms for image matching and 3D reconstruction, designed to maximize parallelism at each stage of the pipeline and to scale gracefully with both the size of the problem and the amount of available computation. Our experimental results demonstrate that it is now possible to reconstruct city-scale image collections with more than a hundred thousand images in less than a day.
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paper_title: Algorithms for Image Processing and Computer Vision

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A cookbook of algorithms for common image processing applicationsThanks to advances in computer hardware and software, algorithms have been developed that support sophisticated image processing without requiring an extensive background in mathematics. This bestselling book has been fully updated with the newest of these, including 2D vision methods in content-based searches and the use of graphics cards as image processing computational aids. Its an ideal reference for software engineers and developers, advanced programmers, graphics programmers, scientists, and other specialists who require highly specialized image processing.Algorithms now exist for a wide variety of sophisticated image processing applications required by software engineers and developers, advanced programmers, graphics programmers, scientists, and related specialistsThis bestselling book has been completely updated to include the latest algorithms, including 2D vision methods in content-based searches, details on modern classifier methods, and graphics cards used as image processing computational aidsSaves hours of mathematical calculating by using distributed processing and GPU programming, and gives non-mathematicians the shortcuts needed to program relatively sophisticated applications.Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
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paper_title: Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

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Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.
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paper_title: FAST: framework for heterogeneous medical image computing and visualization

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Purpose ::: Computer systems are becoming increasingly heterogeneous in the sense that they consist of different processors, such as multi-core CPUs and graphic processing units. As the amount of medical image data increases, it is crucial to exploit the computational power of these processors. However, this is currently difficult due to several factors, such as driver errors, processor differences, and the need for low-level memory handling. This paper presents a novel FrAmework for heterogeneouS medical image compuTing and visualization (FAST). The framework aims to make it easier to simultaneously process and visualize medical images efficiently on heterogeneous systems.
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paper_title: Multithreading Image Processing in Single-core and Multi-core CPU using Java

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Multithreading has been shown to be a powerful approach for boosting a system performance. One of the good examples of applications that benefits from multithreading is image processing. Image processing requires many resources and processing run time because the calculations are often done on a matrix of pixels. The programming language Java supports the multithreading programming as part of the language itself instead of treating threads through the operating system. In this paper we explore the performance of Java image processing applications designed with multithreading approach. In order to test how the multithreading influences on the performance of the program, we tested several image processing algorithms implemented with Java language using the sequential one thread and multithreading approach on single and multi-core CPU. The experiments were based not only on different platforms and algorithms that differ from each other from the level of complexity, but also on changing the sizes of the images and the number of threads when multithreading approach is applied. Performance is increased on single core and multiple core CPU in different ways in relation with image size, complexity of the algorithm and the platform.
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paper_title: Multithreading Method to Perform the Parallel Image Registration

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With the multi-core technology be widely used in PCs, multithreading method becomes the most efficient programming tool for improving the computation capacity on a single processor. Especially in image processing, most numerical approaches are based on the computing over the pixel matrices. As the image size increases, more time consuming is needed. In this paper, a parallel method to perform the PDE-based image registration computing is discussed, the multithreading process with OpenMP on a dual core computer is detailed. Some experimental results show that this method can produce the large size parallel image registration and save nearly a half computing time.
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Title: A Survey of Multithreading Image Analysis

Section 1: Introduction
Description 1: Provide an overview of the intersection of image analysis and multithreading, highlighting the significance and objectives of using multithreading techniques in digital image analysis.

Section 2: Digital Image Analysis
Description 2: Discuss the various tasks and levels of digital image analysis, ranging from low-level to high-level processing, and the importance of these tasks in different domains.

Section 3: Multithreading Approach
Description 3: Explain what multithreading is, its advantages, and how it improves the computational efficiency of image analysis applications.

Section 4: Literature Review
Description 4: Review the current knowledge and progress in the field by detailing significant research works that have incorporated multithreading strategies in digital image analysis.

Section 5: Discussion, Conclusion, and Possible Extension
Description 5: Summarize the findings of the survey, discuss the benefits and challenges of using multithreading in image analysis, and suggest potential extensions for future research.