Color perception and recognition method for Guangdong embroidery image based on discrete mathematical model

2021 ◽  
pp. 1063293X2199436
Author(s):  
Ya Zhang ◽  
Qiang Xiong
Keyword(s):  
Mathematical Model ◽  
Spatial Model ◽  
Traditional Method ◽  
Dynamic Range ◽  
Color Space ◽  
Color Perception ◽  
Histogram Equalization ◽  
Input Image ◽  
Recognition Method ◽  
Discrete Mathematical Model

Aiming at the problem that the traditional color perception and recognition method for Guangdong embroidery image has poor color stereo restoring ability, a color perception, and recognition method for Guangdong embroidery image based on discrete mathematical model is proposed. Through histogram equalization, the input image with centralized gray distribution is transformed into the output image with approximate uniform distribution to enhance the dynamic range of the gray value of the pixels; the median filtering method is used to smooth the Guangdong embroidery image and remove the noise in the Guangdong embroidery image. The RGB spatial model and HSI spatial model of image color are constructed by normalizing the coordinates and color attributes of pixels. Using these two models to transform RGB color space and HSI color space, image color perception, and recognition model is established to realize color perception and recognition of Guangdong embroidery image. In order to verify the color stereo restoring ability of the method, the method is compared with the traditional method for color perception and recognition of Guangdong embroidery image, which proves that the color stereo restoring ability of the method is better than that of the traditional method.

Color perception and recognition method for Guangdong embroidery image based on discrete mathematical model

2020 ◽  
pp. 1-11
Author(s):  
Ya Zhang ◽  
Qiang Xiong
Keyword(s):  
Mathematical Model ◽  
Spatial Model ◽  
Dynamic Range ◽  
Color Space ◽  
Color Perception ◽  
Histogram Equalization ◽  
Input Image ◽  
Pixel Image ◽  
Color Pixel ◽  
Discrete Mathematical Model

The traditional method of Guangdong embroidery image color perception recognition has poor stereoscopic color reduction. Therefore, this paper introduces discrete mathematical model to design a new method of Guangdong embroidery image color perception recognition. Through histogram equalization, the input image with relatively concentrated gray distribution is transformed into the histogram output image with approximately uniform distribution to enhance the dynamic range of pixel gray value. The image of Yuexiu is smoothed and filtered by median filtering method to remove the noise in the image of Yuexiu. The RGB spatial model and HSI spatial model of image color are constructed by normalizing the coordinates and color attributes of pixels. The RGB color space and HSI color space are transformed, and the image color perception recognition model is established to realize the color perception recognition of Guangdong embroidery image. The experimental results show that the pixels of each color in the color pixel image curve of the proposed method are as high as 800, the color pixel image curve distribution is the most intensive, and the color restoration is high.

scholarly journals Photographic Reproduction and Enhancement Using HVS-Based Modified Histogram Equalization

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4136
Author(s):  
Yung-Yao Chen ◽  
Kai-Lung Hua ◽  
Yun-Chen Tsai ◽  
Jun-Hua Wu
Keyword(s):  
Visual Information ◽  
Dynamic Range ◽  
State Of The Art ◽  
Histogram Equalization ◽  
Input Image ◽  
Processing Stage ◽  
Global Contrast ◽  
Photographic Reproduction ◽  
Reproduction Method ◽  
Reproduction Stage

Photographic reproduction and enhancement is challenging because it requires the preservation of all the visual information during the compression of the dynamic range of the input image. This paper presents a cascaded-architecture-type reproduction method that can simultaneously enhance local details and retain the naturalness of original global contrast. In the pre-processing stage, in addition to using a multiscale detail injection scheme to enhance the local details, the Stevens effect is considered for adapting different luminance levels and normally compressing the global feature. We propose a modified histogram equalization method in the reproduction stage, where individual histogram bin widths are first adjusted according to the property of overall image content. In addition, the human visual system (HVS) is considered so that a luminance-aware threshold can be used to control the maximum permissible width of each bin. Then, the global tone is modified by performing histogram equalization on the output modified histogram. Experimental results indicate that the proposed method can outperform the five state-of-the-art methods in terms of visual comparisons and several objective image quality evaluations.

scholarly journals Quadrant Dynamic with Automatic Plateau Limit Histogram Equalization for Image Enhancement

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
P. Jagatheeswari ◽  
S. Suresh Kumar ◽  
M. Mary Linda
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Dynamic Range ◽  
Median Filter ◽  
Histogram Equalization ◽  
Input Image ◽  
Enhancement Technique ◽  
Image Contrast Enhancement ◽  
The Mean ◽  
Plateau Limit

The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.

A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image

2011 ◽  
Vol 341-342 ◽  
pp. 893-897
Author(s):  
Gui Zhou Wang ◽  
Guo Jin He
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Constancy ◽  
Human Perception ◽  
Histogram Equalization ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Dynamic Range Compression ◽  
Restoration Algorithm ◽  
Color Restoration

The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

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.

Two-dimensional discrete mathematical model of tumor-induced angiogenesis

2009 ◽  
Vol 30 (4) ◽  
pp. 455-462
Author(s):  
Gai-ping Zhao ◽  
Er-yun Chen ◽  
Jie Wu ◽  
Shi-xiong Xu ◽  
M. W. Collins ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Two Dimensional ◽  
Discrete Mathematical Model

scholarly journals Adaptive Image Enhancement Using Entropy-Based Subhistogram Equalization

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Liyun Zhuang ◽  
Yepeng Guan
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Image Database ◽  
Input Image ◽  
Public Image ◽  
Contrast Enhanced ◽  
Quantitative Results ◽  
The Mean ◽  
Visual Assessments

A novel image enhancement approach called entropy-based adaptive subhistogram equalization (EASHE) is put forward in this paper. The proposed algorithm divides the histogram of input image into four segments based on the entropy value of the histogram, and the dynamic range of each subhistogram is adjusted. A novel algorithm to adjust the probability density function of the gray level is proposed, which can adaptively control the degree of image enhancement. Furthermore, the final contrast-enhanced image is obtained by equalizing each subhistogram independently. The proposed algorithm is compared with some state-of-the-art HE-based algorithms. The quantitative results for a public image database named CVG-UGR-Database are statistically analyzed. The quantitative and visual assessments show that the proposed algorithm outperforms most of the existing contrast-enhancement algorithms. The proposed method can make the contrast of image more effectively enhanced as well as the mean brightness and details well preserved.

Effect of dynamic range input image on performance of binary subtracted joint transform correlator

1996 ◽  
Vol 3 (6) ◽  
pp. 505-511 ◽  
Author(s):  
Takumi Minemoto ◽  
Yukihisa Osugi ◽  
Hiromitsu Mizukawa ◽  
Junko Ishikawa
Keyword(s):  
Dynamic Range ◽  
Input Image ◽  
Joint Transform Correlator
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