Digital watermarking based on pixels modification using adaptive Gaussian filter in lab color space

2011 ◽  
Author(s):  
Alisa Songputtipong ◽  
Thumrongrat Amornraksa
Keyword(s):  
Digital Watermarking ◽  
Color Space ◽  
Gaussian Filter ◽  
Lab Color Space

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.

Digital watermarking for color images in hue-saturation-value color space

2014 ◽  
Vol 23 (3) ◽  
pp. 033009 ◽  
Author(s):  
Suwat Tachaphetpiboon ◽  
Kharittha Thongkor ◽  
Thumrongrat Amornraksa ◽  
Edward J. Delp
Keyword(s):  
Digital Watermarking ◽  
Color Space ◽  
Color Images

Illumination variation-resistant video-based heart rate monitoring using LAB color space

2021 ◽  
Vol 136 ◽  
pp. 106328
Author(s):  
Yuzhong Zhang ◽  
Zhe Dong ◽  
Kezun Zhang ◽  
Shuangbao Shu ◽  
Fucheng Lu ◽  
...  
Keyword(s):  
Heart Rate ◽  
Color Space ◽  
Heart Rate Monitoring ◽  
Illumination Variation ◽  
Lab Color Space

Extracting Golden Area from Image Based on Canny Operator

2015 ◽  
Vol 731 ◽  
pp. 201-204
Author(s):  
Ying Wu ◽  
Xiu Ping Zhao ◽  
Yang Jin ◽  
Xi Zhang
Keyword(s):  
Image Segmentation ◽  
Color Space ◽  
Threshold Value ◽  
Segmentation Algorithm ◽  
Canny Operator ◽  
Small Areas ◽  
Color Separation ◽  
Lab Color Space ◽  
Canny Algorithm ◽  
Image Segmentation Algorithm

This paper researched application of Canny algorithm on the color separation of golden image , to generate a separated golden image plate base on the extraction of golden area, so as to get the effect more closer to the real metallic. Canny algorithm is based on the gray-scale image segmentation algorithm. The image is mapped from RGB to Lab color space. According to the color attributes of b, the golden target regions are extracted using Canny algorithm. But it’s difficult to get the closed target boundary outlet by Canny algorithm, so this paper modified image segmentation algorithm. Firstly, the image is filtered by Canny operator; secondly, small areas on the Canny processed image are removed by using some pre-determined threshold value.; then processed the image through using smoothing and sharping method so to make inner area of image more smooth meanwhile improving boundary sharpness. The experimental results showed that the method based on Canny operator is very suitable for golden area extraction from a image. The golden target-regions can be closed boundary outlet, which makes the golden areas are more accurate and continuous.

Digital Camera Color Resolution Test Based on Knife-Edge Method

2013 ◽  
Vol 469 ◽  
pp. 246-250 ◽  
Author(s):  
Yue Hong Song ◽  
Jun Qing Xu
Keyword(s):  
Transfer Function ◽  
Modulation Transfer Function ◽  
Color Space ◽  
Digital Camera ◽  
Image Data ◽  
Knife Edge ◽  
Modulation Transfer ◽  
Lab Color Space ◽  
Edge Method

A resolution test-chart for digital camera has been developed based on the knife-edge method. RAW images have been acquired with NikonD7000 DSLR and Canon G12. Set the device color-space to Adobe RGB, and convert the image data to LAB color-space using Adobe RGB profile. The Fourier transfer of the LAB tonal value were computed under MATLAB environment. Then by analyzing the L/C/H (Derived from LAB space ) and the Modulation Transfer Function (MTF), we can get the resolution characteristics of the Nikon D7000 and Canon G12 camera.

In Vitro Evaluation of New Dental Software for Shade Matching

2013 ◽  
Vol 587 ◽  
pp. 360-365
Author(s):  
Bogdan Culic ◽  
Vasile Prejmerean ◽  
Cristina Gasparik ◽  
Carina Culic ◽  
Cristian Dragos ◽  
...  
Keyword(s):  
Color Space ◽  
Computer Software ◽  
Tooth Color ◽  
B Values ◽  
Color Selection ◽  
Statistical Indicator ◽  
Lab Color Space ◽  
Shade Matching ◽  
Cie Lab

In this study we evaluate a new computer software developed for determining tooth color parameters obtained from digital images taken in a general practice working conditions. In order to evaluate the accuracy of the program, we used as samples dental shade tabs. The tabs were measured using a dental spectrophotometer, and then photographed and measured using our program (Toodent). L, a, b values of the CIE Lab color space were obtained. The results were also automatically expressed in dental shade tab code. The results analysis was made by comparing the L, a, b values obtained by the program, with spectrophotometer ones taken from the same shade tab. A statistical indicator was created in order to evaluate the accuracy of the program. Further evaluation of the program shall be made, in order to be used in routine clinical color selection.

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