scholarly journals Optical and mathematical method for studying electrode discharge spots with a liquid electrode

2021 ◽  
Vol 2094 (2) ◽  
pp. 022001
Author(s):  
B Kh Tazmeev ◽  
V V Tsybulevsky
Keyword(s):  
High Speed ◽  
Color Image ◽  
Empirical Distribution ◽  
Statistical Characteristics ◽  
Color Image Processing ◽  
Color Intensity ◽  
Color Channel ◽  
Channel Changes ◽  
Green Color ◽  
Electrode Discharge

Abstract High-speed visualization of the discharge with a liquid cathode, color image processing were performed. The area of cathode spots concentration was identified. Statistical characteristics of the distribution of cathode spots were obtained in order to determine the range in which the intensity code of the green color channel changes, the polygon function of the empirical distribution of the intensity code for the green color channel. The graphical dependence on the frequency of the cathode spot indication hit into the specified interval of the color intensity code was created.

Pc-Based Imaging System for Color Cell Identification and Scoring

1999 ◽  
Vol 5 (S2) ◽  
pp. 942-943
Author(s):  
Jasjit S. Suri ◽  
Kumar Satyender
Keyword(s):  
High Speed ◽  
Imaging System ◽  
Color Image ◽  
Diagnostic System ◽  
Cellular Level ◽  
Connected Components ◽  
Color Image Processing ◽  
Data Sets ◽  
Multiple Document ◽  
Detection And Quantification

Pathologists, microbiologists and cytologists are very interested to automatically identify and quantify the sperms, cells or nucleus in the cellular level images. Complexities like voluminous data sets, variability in data sets, millions of colors, and different kinds of artifacts make the detection and quantification process very difficult. This paper is an attempt to design a sophisticated cellular diagnostic system based on color image processing, mathematical morphology and connected components based on run length encoding. This system runs on Windows ‘98/NT PC platform, in Visual C++, 6.0 environment using three different architectures: Single document interface, multiple document interface and dialog based applications. The system takes around 1.5 seconds per image of 512 x 484 square pixels using high speed threading architecture written on the on 400 MHz Pentiumll processor. The system has an accuracy of 95%. The software has been validated tested on NASA and machine vision real world images.

A Sensation Model for Color Images’ Cognition

2013 ◽  
Vol 303-306 ◽  
pp. 1489-1493
Author(s):  
Zhong Sheng Li ◽  
Tong Cheng Huang ◽  
Niu Li ◽  
Ze Su Cai
Keyword(s):  
Image Processing ◽  
Computational Intelligence ◽  
High Speed ◽  
Granular Computing ◽  
Color Image ◽  
Color Images ◽  
Color Image Processing ◽  
New Concepts ◽  
Single Attribute ◽  
Human Thinking

It’s a new idea to make computers be able to obtain “sensations” from a color image through some unsupervised ways. To let the idea come into true, a granule-based model, based on granular computing(GrC) which is a new way to simulate human thinking to help solve complicated problems in the field of computational intelligence, is proposed for color image processing. First, this paper deems data a hypercube, defines two new concepts, attribute granules(AtG) and connected granules(CoG), and presents the definitions of the granule-based model. Then, in order to fulfill the granule-based model, this paper designs a single attribute analyser(SAA), defines some theorems and lemmas related to decomposition, and describes the processing of extracting all attibute granules. Experimental results on over 300 color images show that the proposed analyser is accurate, robust, high-speed, and able to provide computers with “sensations”.

scholarly journals Scalable Methodology to Hide Audio Data in Cover Image using RGB and Gray Color based Key Positioning Image Steganography

2019 ◽  
Vol 8 (3) ◽  
pp. 7674-7679
Keyword(s):  
Color Image ◽  
Least Significant Bit ◽  
Major Area ◽  
Color Channel ◽  
Green Color ◽  
Replacement Algorithm ◽  
Audio Data ◽  
Positioning Algorithm ◽  
The Given

This objective of this paper is primarily focused on RGB color and Gray scale color based key positioning steganography which has been used to overcome the disadvantages of the Least Significant Bit replacement algorithm and helps to embed the audio data in the color images. The given audio data of various sizes is used to embed in the green color channel of the 24 bit color image sequentially by the key based LSB positioning algorithm. Here the audio threshold is another major area where the focus has been laid as increasing the size of the audio data[26] which can be sent through an image without losing the quality of the audio. This method of hiding the audio data through an image helps to authenticate the sender[25] and verifies whether the data has been really sent to the valid user or is used to prevent morphed secret details by the attacker in the middle. The proposed algorithm has been tested against various existing algorithm to study how effectively the algorithm is working, and how effectively it overcomes the drawbacks of the present algorithms. The algorithm is scalable to serve the purpose of authenticating the different demographical region users living all over world and also to identify that the message is reaching only to the valid user[31].

New method of morphological color image processing

2010 ◽  
Vol 30 (8) ◽  
pp. 2101-2104
Author(s):  
Hong-zhong TANG ◽  
Hui-xian HUANG ◽  
Xue-feng GUO ◽  
Ye-wei XIAO
Keyword(s):  
Image Processing ◽  
Color Image ◽  
New Method ◽  
Color Image Processing

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

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