Gray-level image recognition on a dynamically reconfigurable vision architecture

2012 ◽  
Author(s):  
Yuki Kamikubo ◽  
Minonu Watanabe ◽  
Shoji Kawahito
Keyword(s):  
Image Recognition ◽  
Gray Level ◽  
Dynamically Reconfigurable ◽  
Gray Level Image

Gray-level image recognition based on multiple cell-features

1991 ◽  
Vol 22 (5) ◽  
pp. 81-93 ◽  
Author(s):  
Mutsuo Sano ◽  
Shinichi Meguro ◽  
Akira Ishii
Keyword(s):  
Image Recognition ◽  
Gray Level ◽  
Multiple Cell ◽  
Gray Level Image

scholarly journals Double stages of feature extarction-based GFPMI for colored finger vein identification

2020 ◽  
Vol 18 (2) ◽  
pp. 927
Author(s):  
Dawlat Mustafa Sulaiman ◽  
Adnan Mohsin Abdulazeez ◽  
Habibollah Haron
Keyword(s):  
Feature Extraction ◽  
Texture Features ◽  
Biometric Identification ◽  
Gray Level ◽  
Image Detection ◽  
Structure Information ◽  
Finger Vein ◽  
Gray Level Image ◽  
Vein Recognition ◽  
Finger Vein Recognition

Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored localized finger vein images. Then, two kinds of features (gray scale and texture features) are extracted, which tell the structure information of the whole finger vein pattern in the whole dataset. The recurrent based residual neural network (RNN) is used to identify the finger vein images. The experimental show that the localized colored finger vein images based globalized feature extraction has achieved the higher accuracy (93.49%) while the original image dataset achieved less accuracy by (69.86%).

Gray-level image denoising with an improved weighted sparse coding

2020 ◽  
Vol 72 ◽  
pp. 102895
Author(s):  
Yang Ou ◽  
Jianqiao Luo ◽  
Bailin Li ◽  
M.N.S. Swamy
Keyword(s):  
Image Denoising ◽  
Sparse Coding ◽  
Gray Level ◽  
Gray Level Image

Multivalue processing of gray-level image

1993 ◽  
Author(s):  
De-Chen Zhan ◽  
Jing-Chun Chen
Keyword(s):  
Gray Level ◽  
Gray Level Image
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