Distortion-invariant color image recognition by the joint transform correlator with a real-valued reference function

2002 ◽  
Author(s):  
Chulung Chen ◽  
Wei-Jun Wu
Keyword(s):  
Image Recognition ◽  
Color Image ◽  
Joint Transform Correlator ◽  
Reference Function

scholarly journals Creating Color Image Features Using Local Contrast Method

2018 ◽  
Vol 7 (3) ◽  
pp. 367-376
Author(s):  
Ayman Al-Rawashdeh ◽  
Ziad Al-Qadi
Keyword(s):  
Image Recognition ◽  
Color Image ◽  
Digital Processing ◽  
Image Features ◽  
Local Contrast ◽  
Image Size ◽  
Data Types ◽  
Memory Space ◽  
Contrast Method ◽  
Image Pixels

Digital color images are now one of the most popular data types used in the digital processing environment. Color image recognition plays an important role in many vital applications, which makes the enhancement of image recognition or retrieval system an important issue. Using color image pixels to recognize or retrieve the image, but the issue of the huge color image size that requires accordingly more time and memory space to perform color image recognition and/or retrieval. In the current study, image local contrast was used to create local contrast victor, which was then used as a key to recognize or retrieve the image. The proposed local contrast method was properly implemented and tested. The obtained results proved its efficiency as compared with other methods.

scholarly journals Plant Diseases Recognition Based on Image Processing Technology

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Guiling Sun ◽  
Xinglong Jia ◽  
Tianyu Geng
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Image Recognition ◽  
Color Image ◽  
Regional Growth ◽  
Recognition System ◽  
Plant Diseases ◽  
Image Feature

A new image recognition system based on multiple linear regression is proposed. Particularly, there are a number of innovations in image segmentation and recognition system. In image segmentation, an improved histogram segmentation method which can calculate threshold automatically and accurately is proposed. Meanwhile, the regional growth method and true color image processing are combined with this system to improve the accuracy and intelligence. While creating the recognition system, multiple linear regression and image feature extraction are utilized. After evaluating the results of different image training libraries, the system is proved to have effective image recognition ability, high precision, and reliability.

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