scholarly journals RESEARCH OF IMAGES FILTRATION METHODS IN COMPUTER SYSTEMS

2021 ◽  
Vol 5 (1) ◽  
pp. 93-99
Author(s):  
Oleksandr Tymochko ◽  
Volodymyr Larin ◽  
Maksym Kolmykov ◽  
Oleksander Timochko ◽  
Vladislava Pavlenko
Keyword(s):  
Color Image ◽  
Image Representation ◽  
Color Space ◽  
Visual Image ◽  
Color Model ◽  
Color Information ◽  
Color Formation ◽  
Rgb Color Space ◽  
Human Eyes ◽  
Three Components

It is known that human eyes are less sensitive to color, than to their brightness. In the RGB color space, all three components are considered equally important, and they are usually stored with the same resolution. However, you can display a color image more efficiently, separating the brightness from color information and presenting it with a higher resolution than color. RGB space is well suited for computer graphics, because it uses these three components for color formation. However, RGB space is not very effective when it comes to real images. The fact is that to save the color of an image, you need to know and store all three components of the RGB, and if one of them is missing, it will greatly distort the visual image representation. Also, when processing images in RGB space, it is not always convenient to perform any pixel conversion, because, in this case, it will be necessary to list all three values of the RGB component and write back. This greatly reduces the performance of various image processing algorithms. For these and other reasons, many video standards use brightness and two signals that carry information about the red and blue components of the signal, as a color model other than RGB. The most famous among such spaces is YCbCr.

An Effective Flame Segmentation Method Based on Ohta Color Space

2012 ◽  
Vol 485 ◽  
pp. 7-11
Author(s):  
Jian Sheng Wu ◽  
Bin Zhang ◽  
Yun Ling Gao
Keyword(s):  
Detection System ◽  
Color Space ◽  
Weather Conditions ◽  
Color Model ◽  
Segmentation Method ◽  
Video Streams ◽  
Color Information ◽  
Otsu Method ◽  
Flame Detection ◽  
Rgb Color Space

A new fire segmentation method is proposed, which based on OHTA color model and Otsu method. Through this method we can accurately split flame in different weather conditions and different environmental conditions outdoor. The flame can be extracted completely. The method takes advantage of the flame image color space, color information and spatial characteristics of the different complementary color and provides a new idea for the extraction of flame image. This is an efficient flame segmentation algorithm, and time complexity is low. And the conversion from the RGB color space to OHTA color space is linear. It can achieve flame object segmentation from video streams in Video-based flame detection system

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 Hue-Preserving Saturation Improvement in RGB Color Cube

2021 ◽  
Vol 7 (8) ◽  
pp. 150
Author(s):  
Kohei Inoue ◽  
Minyao Jiang ◽  
Kenji Hara
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Color Image Enhancement ◽  
Histogram Specification ◽  
Target Intensity ◽  
Rgb Color Space ◽  
Intensity Image

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.

A novel enhanced quantum image representation based on bit-planes for log-polar coordinates

2022 ◽  
Vol 22 (1&2) ◽  
pp. 17-37
Author(s):  
Xiao Chen ◽  
Zhihao Liu ◽  
Hanwu Chen ◽  
Liang Wang
Keyword(s):  
Color Image ◽  
Image Representation ◽  
Polar Coordinates ◽  
Quantum Cost ◽  
Color Information ◽  
Simulation Experiments ◽  
Quantum Image ◽  
Comparison Results ◽  
Bit Planes ◽  
Quantum Image Representation

Quantum image representation has a significant impact in quantum image processing. In this paper, a bit-plane representation for log-polar quantum images (BRLQI) is proposed, which utilizes $(n+4)$ or $(n+6)$ qubits to store and process a grayscale or RGB color image of $2^n$ pixels. Compared to a quantum log-polar image (QUALPI), the storage capacity of BRLQI improves 16 times. Moreover, several quantum operations based on BRLQI are proposed, including color information complement operation, bit-planes reversing operation, bit-planes translation operation and conditional exchange operations between bit-planes. Combining the above operations, we designed an image scrambling circuit suitable for the BRLQI model. Furthermore, comparison results of the scrambling circuits indicate that those operations based on BRLQI have a lower quantum cost than QUALPI. In addition, simulation experiments illustrate that the proposed scrambling algorithm is effective and efficient.

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.

scholarly journals Color Systems and Color Image Enhancement Methods

1970 ◽  
Vol 10 (2) ◽  
pp. 97-110
Author(s):  
Akira Taguchi
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Color Image Processing ◽  
Saturation Intensity ◽  
Color Image Enhancement ◽  
Color System ◽  
Hsi Color Space ◽  
Rgb Color Space

There are many color systems. Some systems are correspond to the human visual system, such as the Munsell color system. Other systems are formulated to ease data processing in machines, such as RGB color space. At first, Munsell color system is introduced in this paper. Next, RGB color system and hue-saturation-intensity (HSI) color system which is derived from RGB color systems are reviewed. HSI color system is important, because HSI color system is closely related to Munsell color system. We introduce the advantage and drawbacks of the conventional HSI color space. Furthermore, the improved HSI color system is introduced. The second half of this paper, we introduce a lot of color image enhancement methods based on the histogram equalization or the differential histogram equalization. Since hue preserving is necessary for color image processing, intensity processing methods by using both intensity and saturation in HSI color space are reviewed. Finally, hue preserving color image enhancement methods in RGB color system are explained.

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