scholarly journals Efficiency of spatially recursive algorithms for transmitting images of marine vessels

2021 ◽  
pp. 134-141
Author(s):  
Ш.С. Фахми ◽  
Н.В. Шаталова ◽  
Е.В. Костикова ◽  
Н.Ю. Пышкина ◽  
Ю.И. Васильев
Keyword(s):  
Image Processing ◽  
Computational Complexity ◽  
Information Quality ◽  
Processing System ◽  
Spatial Processing ◽  
Bit Rate ◽  
Complexity Of Algorithms ◽  
Video Information ◽  
Video Systems ◽  
Marine Vessels

На современном этапе развития интеллектуальных морских технологий необходимо включить в состав видеосистемы обработки изображений две подсистемы передачи видеоинформации морских сюжетов. Во первых на основе спектрального преобразования сигналов из пространственной области в частотную для оперативной доставки видеоинформации, полученной с различных камер подводного и надводного наблюдения. Во вторых, на основе пространственных методов обработки, без перехода в спектральную область сигнала для передачи выделенных ключевых точек объектов на изображениях. При этом важнейшая особенность этих подсистем заключается в улучшении информационных показателей качества морских видеосистем автоматизированной обработкой видеоинформации: точность визуальных данных, битовая скорость передачи по каналам связи и вычислительная сложность алгоритмов анализа и передачи видеоинформации. В предлагаемом исследовании приводятся алгоритмы спектральной и пространственной обработки видеоинформации, проведена оценка эффективности алгоритмов обработки изображений. А также отражены результаты моделирования алгоритмов и сравнительная оценка информационных показателей интеллектуальных морских видеосистем: точность, битовая скорость и вычислительная сложность видеосистем обработки морских изображений. At the present stage of the development of intelligent marine technologies, it is necessary to include two subsystems for the transmission of video information of marine scenes in the video image processing system: 1) based on the spectral conversion of signals from the spatial domain to the frequency domain for the rapid delivery of video information obtained from various underwater and surface surveillance cameras; 2) based on spatial processing methods without switching to the spectral domain of the signal to transmit selected key points of objects in the images. At the same time, the most important feature of these subsystems is to improve the information quality indicators of marine video systems by automated processing of video information: the accuracy of visual data, the bit rate of transmission over communication channels and the computational complexity of algorithms for analyzing and transmitting video information. The proposed study provides algorithms for spectral and spatial processing of video information. The results of algorithm modeling and comparative evaluation of information indicators of intelligent marine video systems are also presented: accuracy, bit rate and computational complexity of marine image processing video systems.

Digital image processing methods applied to the study of annealing time on semiconductor-metal eutectic composites

1988 ◽  
Vol 46 ◽  
pp. 848-849
Author(s):  
J. Hefter
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Electronic Device ◽  
Processing System ◽  
Average Diameter ◽  
Eutectic Solidification ◽  
Metal Silicide ◽  
The Matrix ◽  
Microscopic Studies

Semiconductor-metal composites, formed by the eutectic solidification of silicon and a metal silicide have been under investigation for some time for a number of electronic device applications. This composite system is comprised of a silicon matrix containing extended metal-silicide rod-shaped structures aligned in parallel throughout the material. The average diameter of such a rod in a typical system is about 1 μm. Thus, characterization of the rod morphology by electron microscope methods is necessitated.The types of morphometric information that may be obtained from such microscopic studies coupled with image processing are (i) the area fraction of rods in the matrix, (ii) the average rod diameter, (iii) an average circularity (roundness), and (iv) the number density (Nd;rods/cm2). To acquire electron images of these materials, a digital image processing system (Tracor Northern 5500/5600) attached to a JEOL JXA-840 analytical SEM has been used.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

EMCAT: An automatic image-processing system for electron-microscopic tomography

1994 ◽  
Vol 52 ◽  
pp. 418-419
Author(s):  
Weiping Liu ◽  
John W. Sedat ◽  
David A. Agard
Keyword(s):  
Image Processing ◽  
Structural Information ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Processing System ◽  
Electron Microscopic ◽  
Data Set ◽  
Ordered Structures ◽  
Automatic Image Processing ◽  
Em Tomography

Any real world object is three-dimensional. The principle of tomography, which reconstructs the 3-D structure of an object from its 2-D projections of different view angles has found application in many disciplines. Electron Microscopic (EM) tomography on non-ordered structures (e.g., subcellular structures in biology and non-crystalline structures in material science) has been exercised sporadically in the last twenty years or so. As vital as is the 3-D structural information and with no existing alternative 3-D imaging technique to compete in its high resolution range, the technique to date remains the kingdom of a brave few. Its tedious tasks have been preventing it from being a routine tool. One keyword in promoting its popularity is automation: The data collection has been automated in our lab, which can routinely yield a data set of over 100 projections in the matter of a few hours. Now the image processing part is also automated. Such automations finish the job easier, faster and better.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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