Quality Analysis of Color Images Compressed with Enhanced Vector Quantizer Designed Using HSI Color Space

2007 ◽  
Author(s):  
Y.H. Dandawate ◽  
M.A. Joshi ◽  
A.V. Chitre
Keyword(s):  
Color Space ◽  
Color Images ◽  
Quality Analysis ◽  
Vector Quantizer ◽  
Hsi Color Space

Analysis of Characters of HSI Color Space and Its Application in Moving Robot

2010 ◽  
Vol 26-28 ◽  
pp. 48-54
Author(s):  
Jin Ling Wei ◽  
Jun Meng ◽  
Wei Song
Keyword(s):  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Value Range ◽  
Hsi Color Space ◽  
Rgb Color Space

According to the analysis of every feature element’s grey images in RGB color space and HSI color space, each of the elements represents different information of the color image. From the analysis of the Histogram of color images, the value range of hue H basically keeps stable, which is proved by experiments to be the most stable and representative one. Finally we illustrated by application instances that the method of recognition and tracking of the objective moving robot based on hue character H is applicable.

Research and Design on Vision System of the Mobile Robot

2013 ◽  
Vol 341-342 ◽  
pp. 797-800
Author(s):  
Xin Ma ◽  
Rong Guang Sun ◽  
Yong Feng Dong
Keyword(s):  
Object Recognition ◽  
Mobile Robot ◽  
Vision System ◽  
Color Space ◽  
Color Images ◽  
Light Conditions ◽  
Hsi Color Space ◽  
Research And Design

The paper presents the design of vision system of the mobile robot, and shows methods of object recognition based on color images in the system of mobile robot. To adapt to the different light conditions, HSI color space is used. The system is able to meet the demand on rapidity and veracity of system of mobile robot. Experiment results show that the proposed technique is liable to accomplish object recognition in presence of changing illumination environment conditions.

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.

scholarly journals Influence of Luminance L* in the L*a*b* Color Space during Color Segmentation in Highly Saturated Color Images

2015 ◽  
Vol 102 (1) ◽  
pp. 21-31
Author(s):  
Rodolfo Alvarado-Cervantes ◽  
Edgardo M. Felipe-Riveron ◽  
Vladislav Khartchenko ◽  
Oleksiy Pogrebnyak
Keyword(s):  
Color Space ◽  
Color Images ◽  
Color Segmentation ◽  
Saturated Color

scholarly journals Multichannel Correlation Clustering Target Detection

2020 ◽  
Vol 49 (3) ◽  
pp. 335-345
Author(s):  
Yan Xu ◽  
Jiangtao Dong ◽  
Zishuo Han ◽  
Peiguang Wang
Keyword(s):  
Color Space ◽  
Training Model ◽  
Gaussian Mixture ◽  
Time Correlation ◽  
Model Parameters ◽  
Time Dimension ◽  
Dynamic Background ◽  
Correlation Clustering ◽  
Hsi Color Space ◽  
Real Time Computing

During target tracking, certain multi-modal background scenes are unsuitable for off-line training model. To solve this problem, based on the Gaussian mixture model and considering the pixels’ time correlation, a method that combines the random sampling operator and neighborhood space propagation theory is proposed to simplify the model update process. To accelerate the model convergence, the observation vector is constructed in the time dimension by optimizing the model parameters. Finally, a three channel-multimodal background model fusing the HSI color space and gradient information is established in this study. Hence the detection of moving targets in a complicated environment is achieved. Experiments indicate that the algorithm has good detection performance when inhibiting ghosts, dynamic background, and shade; thus, the execution efficiency can meet the needs of real-time computing.

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