作者：
Anna V. DoronichevaSergey Z. SavinCC FEB RAS
Federal State Budgetary Institution of Science computing Center of the Far Eastern Branch of the Russian Academy of Sciences Khabarovsk Russia
In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the develo...
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ISBN:
(纸本)9781538675328
In work is described practical using of WEB-technology for segmentation and analysis tasks of medical image. Progress in the development of bioinformatics and mathematical methods in biomedicine, as well as the development of computer and telecommunications systems and networks determines the look of the present and future of medical technology and of medicine in general [8], [10]. At last years of one of the directions of development of cloud, computing technologies in high-tech-medicine is a processing the digital image: improvement of quality of image, recovering image, its recognition of separate elements. Recognition of pathological processes is one of the most important problems of processing the medical image. By now, a number of standards for medical imaging have been developed. By analogy with CAD/CAM systems (computer aided design and computer aided manufacturing) for technical applications, CAD (computer-aided diagnosis) systems are being developed for medical purposes. Some of them are already successfully operating, but to date these systems are only "assistants" of a diagnostician who takes decisions. CAD algorithms for medical imaging systems typically include image segmentation, the selection of some objects of interest ("masscs"), their analysis, parametric description of the selected objects and their classification.
Fuzzy C-Means and Possibilistic C-Means are two most used algorithms in the computer aided diagnosis systems-(CAD) for image segmentation. Due to simple implementation, fast convergence and unsupervised procedure, the...
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ISBN:
(纸本)9781538644058
Fuzzy C-Means and Possibilistic C-Means are two most used algorithms in the computer aided diagnosis systems-(CAD) for image segmentation. Due to simple implementation, fast convergence and unsupervised procedure, these algorithms have been become widespread and the most desirable methods to employ in disparate image segmentation problems. However, emerging new imaging devices and different quality of images, unveil disability of these methods. Falling in local optima and high sensitivity to noise and outliers, have confronted the scholars with the new challenges in recent decades. This paper by employing a meta-heuristic algorithm, Differential Evolution, and kernel-based inner product norm metric has taken a new direction yet simple to overcome to initial configuration sensitivity and premature convergence in the conventional FCM and PCM. Proposed method succeeded to get to an accuracy about 94% true segmentation of brain tissue in presence of noise and intensity inhomogeneity.
We develop two techniques based on alternating minimization and alternating directions method of multipliers for phase retrieval (PR) by employing a variable-splitting approach in a maximum likelihood estimation frame...
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ISBN:
(纸本)9781538646595
We develop two techniques based on alternating minimization and alternating directions method of multipliers for phase retrieval (PR) by employing a variable-splitting approach in a maximum likelihood estimation framework. This leads to an additional equality constraint, which is incorporated in the optimization framework using a quadratic penalty. Both algorithms are iterative, wherein the updates are computed in closed-form. Experimental results show that: (i) the proposed techniques converge faster than the state-of-the-art PR algorithms;(ii) the complexity is comparable to the state of the art;and (iii) the performance does not depend critically on the choice of the penalty parameter. We also show how sparsity can be incorporated within the variable splitting framework and demonstrate concrete applications to image reconstruction in frequency-domain optical-coherence tomography.
Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or ...
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ISBN:
(纸本)9783319483085;9783319483078
Hadoop has become a widely used open source framework for large scale data processing. MapReduce is the core component of Hadoop. It is this programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster. It allows processing of extremely large video files or image files on data nodes. This can be used for implementing Content Based image Retrieval (CBIR) algorithms on Hadoop to compare and match query images to the previously stored terabytes of an image descriptors databases. This work presents the implementation for one of the well-known CBIR algorithms called Scale Invariant Feature Transformation (SIFT) for image features extraction and matching using Hadoop platform. It gives focus on utilizing the parallelization capabilities of Hadoop MapReduce to enhance the CBIR performance and decrease data input\output operations through leveraging Partitioners and Combiners. Additionally, imageprocessing and computer vision tools such as Hadoop imageprocessing (HIPI) and Open Computer Vision (OpenCV) are integration is shown.
Drones are used in a number of industrial applications such as asset tracking and inspection. Indoor industrial applications based on visual data pose various challenges such as low lighting conditions and presence of...
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Drones are used in a number of industrial applications such as asset tracking and inspection. Indoor industrial applications based on visual data pose various challenges such as low lighting conditions and presence of non-planar scenes. Due to the nature of the indoor applications, image data is acquired at close range and this leads to the loss of context. In order to get global context, image stitching is a key step for data interpretation. We propose an approach to stitch drone-captured indoor video frames, where feature based stitching fails. In order to achieve this, the image feature data extracted is fused with drone inertial measurement unit (IMU) data. The approach is tested in a warehouse and the performance is compared with other state-of-the-art image stitching algorithms. The proposed approach shows robust performance in cases of highly non-planar scenes.
In imageprocessing, the inpainting (image restoration) problem is often considered with respect to the interpolation. We involve into the solution of this problem the F-transform-based nonlocal operators that define ...
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In imageprocessing, the inpainting (image restoration) problem is often considered with respect to the interpolation. We involve into the solution of this problem the F-transform-based nonlocal operators that define a new type of functionals, extending the ability of classical PDE-based algorithms in handling textures and repetitive structures. We showed that in the particular space with a fuzzy partition, the nonlocal Laplacian and partial derivatives can be represented by the F~0- and F~1-transforms. For the inpainting problem specified by relatively large damaged areas, we propose a new total variation model with the F-transform-based nonlocal operators. We show that the proposed model together with the corresponding algorithm increase the quality of a (usually considered) patch-based searching algorithm.
This paper aims to propose a novel Organic Computing concept to dealing with the overall issue of automated design of processing pipelines. It is outlined how several methods standing under the Artificial Intelligence...
This paper aims to propose a novel Organic Computing concept to dealing with the overall issue of automated design of processing pipelines. It is outlined how several methods standing under the Artificial Intelligence umbrella are combined to form a technique that can be realized by Organic Computing systems to strengthen their self-configuration property by implementing self-optimization and self-learning techniques. Three envisioned application scenarios are discussed which will serve as first testbeds for the proposed architecture in a future research project: The automated design of 1) a data pre-processing observer component for the refurbishment and the analysis of insufficient quality data to improve the learning ability of employed machine learning algorithms, 2) an imageprocessing pipeline for industrial imaging systems, and, 3) production lines in manufacturing scenarios.
Target recognition is a challenge and one of the most active research areas in computer vision. Variations in image cause many challenges to target recognition system due to change in illumination, noise and detail bl...
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ISBN:
(纸本)9781538657393;9781538657386
Target recognition is a challenge and one of the most active research areas in computer vision. Variations in image cause many challenges to target recognition system due to change in illumination, noise and detail blurring. In this paper, a new approach based on image preprocessing is proposed to speeded up robust features in target recognition. The proposed image preprocessing method improves the global air-light first by using Retinex algorithm of the original image, then a homomorphic filtering scheme is proposed after db2-type wavelet transform. In target recognition, based on speeded up robust feature extraction approach, the feature points are extracted by Hessian operator in the algorithm, then the algorithm gets all the matching pairs by matching threshold. The experiments show that the algorithm can solve the registration accuracy problem under complex conditions, and improve the correction results effectively.
Aiming at the high requirement of real-time and accuracy in moving object detection, this paper designs a moving object detection system based on FPGA. First, the D5M camera is used to capture video images, and then t...
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ISBN:
(纸本)9781538693902
Aiming at the high requirement of real-time and accuracy in moving object detection, this paper designs a moving object detection system based on FPGA. First, the D5M camera is used to capture video images, and then the image is cached into SDRAM memory. The buffered image is detected by background subtraction method based on GMM, and then the processed image is sent to the image display module for display. Matlab, Xlinx ISE and Modelsim are used to complete the hardware design and Simulation of the algorithm. Experimental results show that the designed system can detect moving targets in real time with high accuracy.
Feature extraction and segmentation on multidimensional images is still a tedious task in the field of imageprocessing. images provide depth of reality and featuring the image interactively for which imageprocessing...
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ISBN:
(纸本)9781538623428;9781538623411
Feature extraction and segmentation on multidimensional images is still a tedious task in the field of imageprocessing. images provide depth of reality and featuring the image interactively for which imageprocessing is beneficial for extracting different features from any image by applying various algorithms and obtaining discrete results. These algorithm help to extract features like edges, texture and surface of an image. This paper attempts to evaluate efficiency of different edge detection algorithm and comparison of their result to find out the best operator for Edge Detection Technique.
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