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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Javier Silvestre-Blanes Instituto Tecnolgico de Informática (ITI) Universitat Politècnica de València (UPV) Alcoy Spain

ISBN: (纸本)9781509013159

The huge growth in the smartphones market has led to the improvement in ARM architectures, so that today there are many devices based on them, with sufficient capacity to deal with certain image processing applications. These are used in VSN (Visual Sensor Networks) and in other scenarios, where the energy used in the process is a parameter to be considered. In these scenarios, and with the HMP (Heterogeneous multi-processing) architectures on which the current platforms are based, it is necessary to determine both the core as well as the working frequency in order to achieve the application's image processing objectives with the least possible energy consumption. In this type of system, the workload is oriented towards interactive applications, so the priorities are different to those of VSN type systems. Conventional regulation algorithms do not make appropriate use of the processor's frequency range, in relation to the load. This paper analyses the influence of the maximum frequency value on system performance and power consumption.

关键词： Time-frequency analysis Vehicles Smart phones Energy consumption Computer architecture image processing Algorithm design and analysis

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Real-time combustion state identification via image processing: A dynamic data-driven approach

Real-time combustion state identification via image processi...

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American Control Conference

作者： Michael Hauser Yue Li Jihang Li Asok Ray Mechanical & Nuclear Engineering Department Pennsylvania State University University Park 16802 USA

ISBN: (纸本)9781467386838

This paper proposes a framework for analyzing video of physical processes as a paradigm of dynamic data-driven application systems (DDDAS). The algorithms were tested on a combustion system under fuel lean and ultra-lean conditions. The main challenge here is to develop feature extraction and information compression algorithms with low computational complexity such that they can be applied to real-time analysis of video captured by a high-speed camera. In the proposed method, image frames of the video is compressed into a sequence of image features. Then, these image features are mapped to a sequence of symbols by partitioning of the feature space. Finally, a special class of probabilistic finite state automata (PFSA), called D-Markov machines, are constructed from the symbol strings to extract pertinent features representing the embedded dynamic characteristics of the physical process. This paper compares the performance and efficiency of three image feature extraction algorithms: Histogram of Oriented Gradients, Gabor Wavelets, and Fractal Dimension. The k-means clustering algorithm has been used for feature space partitioning. The proposed algorithm has been validated on experimental data in a laboratory environment combustor with a single fuel-injector.

关键词： Feature extraction Combustion Heuristic algorithms Principal component analysis Probabilistic logic Automata Probabilistic logic Feature extraction Heuristic algorithms Automata Combustion principal components analysis Eigenvector Videotapes combustion system image feature Physical Processes image sequences SYMBOL STRINGS data driven images

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Real-time artefact filter for intraoperative thermographic imaging

Real-time artefact filter for intraoperative thermographic i...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Jens Müller Jan Müller Bill Thaute Ronald Tetzlaff Faculty of Electrical and Computer Engineering Technische Universität Dresden Dresden

ISBN: (纸本)9781509029600

En route to an intraoperative imaging system for brain surgery, a thermographic camera associated with time-resolved data analysis is used for the identification of functional areas and the detection of brain tumours. In this contribution we propose a Cellular Nonlinear Network based image processing system supporting massively-parallel and real-time processing of thermographic video data. In the current setup the system is implemented on a Xilinx Zynq FPGA and is used for the preprocessing of the raw data. The development and implementation of a digital low-pass filter for the compensation of local motion artefacts will be demonstrated as a typical application example of the freely programmable system.

关键词： Imaging Real-time systems Hardware Computer architecture Algorithm design and analysis Field programmable gate arrays Signal processing algorithms

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Gastric Cancer Regional Detection System

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JOURNAL OF MEDICAL systems 2016年第1期40卷 31-31页

作者： Ural, Berkan Hardalac, Firat Serhatlioglu, Selami Ilhan, Mustafa Necmi Gazi Univ Elect Elect Engn Ankara Turkey Firat Univ Dept Radiol Elazig Turkey Gazi Univ Dept Publ Hlth Ankara Turkey

In this study, a novel system was created to localize cancerous regions for stomach images which were taken with computed tomography(CT). The aim was to determine the coordinates of cancerous regions which spread in the stomach area in the color space with using this system. Also, to limit these areas with a high accuracy ratio and to feedback to the user of this system were the other objectives. This integration was performed with using energy mapping, analysis methods and multiple image processing methods and the system which was consisted from these advanced algorithms was appeared. For this work, in the range of 25-40 years and when gender discrimination was insignificant, 30 volunteer patients were chosen. During the formation of the system, to exalt the accuracy to the maximum level, 2 main stages were followed up. First, in the system, advanced image processing methods were processed between each other and obtained data were studied. Second, in the system, FFT and Log transformations were used respectively for the first two cases, then these transformations were used together for the third case. For totally three cases, energy distribution and DC energy intensity analysis were done and the performance of this system was investigated. Finally, with using the system's unique algorithms, a non-invasive method was achieved to detect the gastric cancer and when FFT and Log transformation were used together, the maximum success rate was obtained and this rate was calculated as 83,3119 %.

关键词： Cancer Gastric cancer image processing image acquisition image filtering image thresholding Power Spectrum Density (PSD) analysis

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A SYSTEM FOR HIGH PRECISION GLASS-TO-GLASS DELAY MEASUREMENTS IN VIDEO COMMUNICATION

A SYSTEM FOR HIGH PRECISION GLASS-TO-GLASS DELAY MEASUREMENT...

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IEEE International Conference on image processing

作者： Christoph Bachhuber Eckehard Steinbach Technical University of Munich Chair of Media Technology

ISBN: (纸本)9781467399623

Ultra low delay video transmission is becoming increasingly important. Video-based applications with ultra low delay requirements range from teleoperation scenarios such as controlling drones or telesurgery to autonomous control of dynamic processes using computer vision algorithms applied on real-time video. To evaluate the performance of the video transmission chain in such systems, it is important to be able to precisely measure the glass-to-glass (G2G) delay of the transmitted video. In this paper, we present a low-complexity system that takes a series of pairwise independent measurements of G2G delay and derives performance metrics such as mean delay or minimum delay etc. from the data. The precision is in the sub-millisecond range, mainly limited by the sampling rate of the measurement system. In our implementation, we achieve a G2G measurement precision of 0.5 milliseconds with a sampling rate of 2kHz.

关键词： Video signal processing Glass-to-glass delay measurement Video delay distribution Video signal processing sampling rate G2G measurement system telesurgery Precision autonomous control delay mean delay Video transmission transient processes Performance metrics images computer vision Complex systems

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Big biomedical image processing hardware acceleration: A case study for K-means and image filtering

Big biomedical image processing hardware acceleration: A cas...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Katayoun Neshatpour Arezou Koohi Farnoud Farahmand Rajiv Joshi Setareh Rafatirad Avesta Sasan Houman Homayoun George Mason University IBM T. J. Watson

ISBN: (纸本)9781479953424

Most hospitals today are dealing with the big data problem, as they generate and store petabytes of patient records most of which in form of medical imaging, such as pathological images, CT scans and X-rays in their datacenters. Analyzing such large amounts of biomedical imaging data to enable discovery and guide physicians in personalized care is becoming an important focus of data mining and machine learning algorithms developed for biomedical Informatics (BMI). algorithms that are developed for BMI heavily rely on complex and computationally intensive machine learning and data mining methods to learn from large data. The high processing demand of big biomedical imaging data has given rise to their implementation in high-end server platforms running software ecosystems that are optimized for dealing with large amount of data including Apache Hadoop and Apache Spark. However, efficient processing of such large amount of imaging data running computational intensive learning methods is becoming a challenging problem using state-of-the-art high performance computing server architectures. To address this challenge, in this paper, we introduce a scalable and efficient hardware acceleration method using low cost commodity FPGAs that is interfaced with a server architecture through a high speed interface. In this work we present a full end-to-end implementation of big data image processing and machine learning applications in a heterogeneous CPU+FPGA architecture. We develop the MapReduce implementation of K-means and Laplacian Filtering in Hadoop Streaming environment that allows developing mapper functions in non-Java based languages suited for interfacing with FPGA-based hardware accelerating environment. We accelerate the mapper functions through hardware+software (HW+SW) co-design. We do a full implementation of the HW+SW mappers on the Zynq FPGA platform. The results show promising kernel speedup of up to 27× for large image data sets. This translate to 7.8× and 1.8

关键词： Acceleration Biomedical imaging Hardware Servers Field programmable gate arrays Computer architecture

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An Efficient Hardware Implementation of Canny Edge Detection Algorithm

An Efficient Hardware Implementation of Canny Edge Detection...

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International Conference on VLSI Design

作者： D. Sangeetha P. Deepa Department of ECE Government College of Technology Coimbatore India

The edge detection is one of the key techniques in most image processing applications. The canny edge detection is proven to be able to significantly outperform existing edge detection techniques due to its superior performance. Unfortunately, the implementation of the systems in real-time is computationally complex, high hardware cost with increased latency. The proposed canny edge detection algorithm uses approximation methods to replace the complex operations, the pipelining is employed to reduce the latency. Finally, this algorithm is implemented on Xilinx Virtex-5 FPGA. When compared with the previous hardware architecture for canny edge detection, the proposed architecture requires fewer hardware costs and takes 1ms to detect the edges of 512×512 image.

关键词： image edge detection Hardware Computer architecture Detectors Approximation algorithms Algorithm design and analysis Real-time systems

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A Randomised Primal-Dual Algorithm for Distributed Radio-Interferometric Imaging

A Randomised Primal-Dual Algorithm for Distributed Radio-Int...

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European Signal processing Conference

作者： Alexandru Onose Rafael E. Carrillo Jason D. McEwen Yves Wiaux Institute of Sensors Signals and Systems Heriot-Watt University Signal Processing Laboratory (LTS5) Ecole Polytechnique Federale de Lausanne Mullard Space Science Laboratory University College London

ISBN: (纸本)9781509018918

Next generation radio telescopes, like the Square Kilometre Array, will acquire an unprecedented amount of data for radio astronomy. The development of fast, parallelisable or distributed algorithms for handling such large-scale data sets is of prime importance. Motivated by this, we investigate herein a convex optimisation algorithmic structure, based on primal-dual forward-backward iterations, for solving the radio interferometric imaging problem. It can encompass any convex prior of interest. It allows for the distributed processing of the measured data and introduces further flexibility by employing a probabilistic approach for the selection of the data blocks used at a given iteration. We study the reconstruction performance with respect to the data distribution and we propose the use of nonuniform probabilities for the randomised updates. Our simulations show the feasibility of the randomisation given a limited computing infrastructure as well as important computational advantages when compared to state-of-the-art algorithmic structures.

关键词： Primal-dual algorithm image processing Radio interferometry radiowave interferometry image processing data distribution RADIO TELESCOPES RANDOMISING Distributed processing distributed algorithms Randomisation Square kilometre Dataset iterative methods

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A Fast Filter Tracker against Serious Occlusion

A Fast Filter Tracker against Serious Occlusion

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IEEE International Conference on Bioinformatics and Biomedicine

作者： Libo Zhang Tiejian Luo Yihan Sun Lin Yang University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509016129

Many tracking algorithms applied in medical image processing, such as observing the movement of cells, have a great improvement in accuracy and robustness. However, it is difficult to deal with the large area occlusion and complete occlusion. In this paper, we propose a fast scale adaptive tracking algorithm based on correlation filtering. Except tracking the change of the target scale quickly, our method can also deal with the problem of large area occlusion and the complete disappearance of the target. Compared with the outstanding scale adaptive tracking method, the proposed method demonstrates higher performances in terms of the accuracy of tracking the target and the real-time performance.

关键词： Biomedical imaging Real-time systems Robustness Visualization Biomedical imaging real-time systems Visualization Occlusion Robustness Adaptive tracking Medical image processing Targets

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A Novel image Registration Algorithm Using SIFT Feature Descriptors

A Novel Image Registration Algorithm Using SIFT Feature Desc...

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International Conference on Smart City and systems Engineering (ICSCSE)

作者： Zhou Jie Yangzhou Polytechnic institute Yangzhou Jiangsu China

ISBN: (纸本)9781509055319

This paper focuses on the problem of image registration, which is a fundamental and important problem in computer vision. As local features are effective to tackle the task of image registration, hence, we exploit SIFT feature descriptors to describe the interesting key points in images. The reasons is that SIFT can extract features which are invariant to scaling, orientation, affine transforms and illumination changes. Afterwards, to lower the effects of outliers, the nearest distance ratio method is used to get initial matched feature descriptor pairs, and then scale-orientation joint restriction is used to detect false matching SIFT descriptor pairs. After using Random Sample Consensus to prune outliers, image registration results can be obtained. In the end, we develop an experiment based on QuickBird and IKONOS datasets, and very positive results are achieved.

关键词： image registration Feature extraction Histograms Signal processing algorithms Computer vision Mathematical model Transforms

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