Extraction of Tobacco Leaves Color Features Based on HSI Color Space

2014 ◽  
Vol 651-653 ◽  
pp. 2424-2429 ◽  
Author(s):  
Jiang Tao Ji ◽  
Ming Li Deng ◽  
Zhi Tao He ◽  
Shi Tong Jia ◽  
Xin Wu Du ◽  
...  
Keyword(s):  
Statistical Method ◽  
Color Space ◽  
Tobacco Leaves ◽  
Growth Status ◽  
Average Value ◽  
Color Features ◽  
Quality Grade ◽  
Hsi Color Space ◽  
Different Levels ◽  
Color Components

Tobacco is widely planted worldwide as an important economic crop. The differences of planting environment and growth status in different regions lead to different levels of tobacco quality, while color is one of the important indexes to evaluate the quality grade of tobacco leaves. Even for those that are planted in the same area, colors of different grades of tobacco leaves vary greatly, so the color of tobacco leaves is often used as the main evaluating index in tobacco leaves grading. In this paper color extraction of standard tobacco leaves based on HSI color space was thoroughly studied. H, S and I component would be quantified and extracted by using color histogram, then the average value corresponding to every color component was calculated. After extraction and calculation on the same level of standard tobacco leaves, the ranges of three color components could be obtained in HSI color space by using statistical method, and provide data information for tobacco leaves grading.

scholarly journals Insulator Contamination Grade Recognition Using the Deep Learning of Color Information of Images

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6662
Author(s):  
Da Zhang ◽  
Shuailin Chen
Keyword(s):  
Deep Learning ◽  
Color Space ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Contact Detection ◽  
Assessment Methodology ◽  
Color Spaces ◽  
Color Information ◽  
Color Features ◽  
Hsi Color Space

To implement the non-contact detection of contamination on insulators, a contamination severity assessment methodology using the deep learning of the colored image information of insulators can be used. For the insulator images taken at the substation site, a mathematical morphology-improved optimal entropic threshold (OET) method is utilized to extract the insulator from the background. By performing feature calculations of insulator images in RGB and HSI color spaces, sixty-six color features are obtained. By fusing the features of the two color spaces using kernel principal component analysis (KPCA), fused features are obtained. The recognition of contamination grades is then accomplished with a deep belief network (DBN) that consists of a three-layered restricted Boltzmann machine. The experimental results of the images taken on-site show that the fused features obtained by the KPCA can fully reflect the contamination state of the insulators. Compared with the identification obtained using RGB or HSI color-space features alone, accuracy is significantly improved, and insulator contamination grades can be effectively identified. The research provides a new method for the accurate, efficient, and non-contact detection of insulator contamination grades.

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 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.

scholarly journals Dehazing in HSI Color Space with Color Correction

2013 ◽  
Vol 18 (2) ◽  
pp. 140-148 ◽  
Author(s):  
Taeha Um ◽  
Wonha Kim
Keyword(s):  
Color Space ◽  
Color Correction ◽  
Hsi Color Space

scholarly journals Variational Histogram Equalization for Single Color Image Defogging

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Li Zhou ◽  
Du Yan Bi ◽  
Lin Yuan He
Keyword(s):  
Color Image ◽  
State Of The Art ◽  
Color Space ◽  
Histogram Equalization ◽  
Degradation Model ◽  
Image Defogging ◽  
Hsi Color Space ◽  
General Variation ◽  
Bad Weather ◽  
Physical Degradation

Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

2020 ◽  
Vol 36 (4) ◽  
pp. 557-564
Author(s):  
LingHan Cai ◽  
Yuan Zhao ◽  
Zhuojue Huang ◽  
Yang Gao ◽  
Han Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Color Space ◽  
List Type ◽  
Area Index ◽  
Average Value ◽  
Rapid Measurement ◽  
Canopy Coverage ◽  
Lab Color Space ◽  
Extraction Model

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

Rapid Measurement of Potato Canopy Coverage and Leaf Area Index Inversion

2020 ◽  
Vol 36 (4) ◽  
pp. 557-564
Author(s):  
LingHan Cai ◽  
Yuan Zhao ◽  
Zhuojue Huang ◽  
Yang Gao ◽  
Han Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Color Space ◽  
List Type ◽  
Area Index ◽  
Average Value ◽  
Rapid Measurement ◽  
Canopy Coverage ◽  
Lab Color Space ◽  
Extraction Model

Highlights This article calculates the canopy coverage (Cc) and inverts it to the leaf area index (LAI) of the collected images through a portable device such as a mobile phone, which is convenient for researchers. The Lab color model has been used for plant area extraction, which has achieved good results. Steps such as weed removal make the algorithm more universal. The inversion results of LAI based on canopy coverage has high accuracy, which indicates that it can be used for LAI calculation. Abstract . Canopy coverage (Cc) and leaf area index (LAI) are important parameters for qualitative and quantitative descriptions of plant growth trends. Meanwhile, LAI can be reflected by Cc. Therefore, it is of great significance to observe Cc and establish the relationship between Cc and LAI for monitoring the growth of plants. In July 2019, in Shang Zhuang experimental field of China Agricultural University, 30 potato canopy images were taken vertically by camera, and the actual LAI data of the corresponding images were measured and recorded by LAI-2200C. Image extraction algorithms of different models, such as ExG, ExGR, NDIGR, and Lab color space extraction model are evaluated and compared. After that, estimating the parameters of the logarithmic model of LAI-Cc by minimizing errors, evaluating the inversion model by Hold-Out. Besides, the result shows Cc can be calculated efficiently by using Lab color space extraction model. In the training set, the average value of R2 between the predicted LAI and the actual LAI reaches 0.940, and the RMSE reaches 0.144. In the test set, the average value of R2 reaches 0.937, the RMSE reaches 0.197. And the average time consumption of the entire process is 2.989 s on an image. It suggests that the study can provide a basis for dynamic monitoring of potato and other crops. Keywords: Canopy coverage (Cc), Leaf area index (LAI), Image processing, Potato, Rapid measurement.

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