14‐2: Data‐Line Driver with Gray‐Level‐Dependent Far‐End Auxiliary Driving for Large AMOLED Display Panel

2021 ◽  
Vol 52 (1) ◽  
pp. 169-172
Author(s):  
Hing-Mo Lam ◽  
Hailong Jiao ◽  
Min Zhang ◽  
Shengdong Zhang
Keyword(s):  
Gray Level ◽  
Data Line ◽  
Line Driver

Data line driver design for A 10" 480×640×3 color FED

2002 ◽  
Author(s):  
Chi-Chang Wang ◽  
Jiin-Chuan Wu ◽  
Chin-Ming Huang
Keyword(s):  
Data Line ◽  
Line Driver

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Characterization of Pancreas at Diabetic Patients in CT images using Texture Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 8
Author(s):  
Mona E. Elbashier ◽  
Suhaib Alameen ◽  
Caroline Edward Ayad ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Size Distribution ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Ct Images ◽  
Diabetic Patients ◽  
Gray Level ◽  
Grey Level ◽  
Run Length ◽  
Interactive Data

This study concern to characterize the pancreas areato head, body and tail using Gray Level Run Length Matrix (GLRLM) and extract classification features from CT images. The GLRLM techniques included eleven’s features. To find the gray level distribution in CT images it complements the GLRLM features extracted from CT images with runs of gray level in pixels and estimate the size distribution of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level distribution of images. The results show that the Gray Level Run Length Matrix and  features give classification accuracy of pancreashead 89.2%, body 93.6 and the tail classification accuracy 93.5%. The overall classification accuracy of pancreas area 92.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate pancreas area names.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Quantitative Evaluation of Gray Level Difference and Successive Abandonment Methods Using Artificial Visualized Images.

1999 ◽  
Vol 19 (75) ◽  
pp. 321-327
Author(s):  
In-seop LEE ◽  
Akikazu KAGA ◽  
Katsuhito YAMAGUCHI
Keyword(s):  
Quantitative Evaluation ◽  
Gray Level ◽  
Level Difference

Research and improvement of data line and common line invisible short in copper process array substrate

2020 ◽  
Vol 35 (10) ◽  
pp. 1036-1043
Author(s):  
Chen LIN ◽  
◽  
Yu-chun FENG ◽  
Xi CHEN ◽  
Gui-quan TANG ◽  
...  
Keyword(s):  
Common Line ◽  
Data Line

Combining Global Probability Density Difference and Local Gray Level Fitting for Ultrasound Image Segmentation

2010 ◽  
Vol 36 (7) ◽  
pp. 951-959 ◽  
Author(s):  
Bo LIU ◽  
Jian-Hua HUANG ◽  
Xiang-Long TANG ◽  
Jia-Feng LIU ◽  
Ying-Tao ZHANG
Keyword(s):  
Image Segmentation ◽  
Probability Density ◽  
Ultrasound Image ◽  
Density Difference ◽  
Gray Level ◽  
Ultrasound Image Segmentation

Detection of image region-duplication forgery based on gray level co-occurrence matrix

2012 ◽  
Vol 31 (6) ◽  
pp. 1628-1630
Author(s):  
Jia-jia OU ◽  
Bi-ye CAI ◽  
Bing XIONG ◽  
Feng LI
Keyword(s):  
Gray Level ◽  
Image Region ◽  
Region Duplication ◽  
Occurrence Matrix

Implementation of gray level error conpensation for optical 4f system

2013 ◽  
Vol 33 (7) ◽  
pp. 1973-1975 ◽  
Author(s):  
Liang HAN ◽  
Ziqi JIANG ◽  
Xiujuan PU
Keyword(s):  
Gray Level

Extracting Gray Level Profiles of Human Chromosomes by Curve Fitting

2012 ◽  
Vol 1 (2) ◽  
Author(s):  
Sadina Gagula-Palalic ◽  
Mehmet Can
Keyword(s):  
Curve Fitting ◽  
Gray Level ◽  
Human Chromosomes
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