Fuzzy Image Preprocessing

2017 ◽  
pp. 61-82
Keyword(s):  
Image Preprocessing ◽  
Fuzzy Image

Improved image preprocessing algorithm based on FPGA

2012 ◽  
Vol 31 (6) ◽  
pp. 1706-1708
Author(s):  
Peng ZHANG ◽  
Jun ZHONG ◽  
An-ming GUO ◽  
Qiang PENG
Keyword(s):  
Image Preprocessing

Research on Improved Gamma Transform Face Image Preprocessing Fusion Algorithm under Complex Lighting Conditions

2020 ◽  
Vol 13 ◽  
Author(s):  
Xiaolin Tang ◽  
Xiaogang Wang ◽  
Jin Hou ◽  
Huafeng Wu ◽  
Ping He
Keyword(s):  
Edge Detection ◽  
Face Image ◽  
Light Sources ◽  
Fusion Algorithm ◽  
Edge Information ◽  
Image Preprocessing ◽  
Lighting Conditions ◽  
Weighted Fusion ◽  
The Face ◽  
Sobel Edge Detection

Introduction: Under complex illumination conditions such as poor light sources and light changes rapidly, there are two disadvantages of current gamma transform in preprocessing face image: one is that the parameters of transformation need to be set based on experience; the other is the details of the transformed image are not obvious enough. Objective: Improve the current gamma transform. Methods: This paper proposes a weighted fusion algorithm of adaptive gamma transform and edge feature extraction. First, this paper proposes an adaptive gamma transform algorithm for face image preprocessing, that is, the parameter of transformation generated by calculation according to the specific gray value of the input face image. Secondly, this paper uses Sobel edge detection operator to extract the edge information of the transformed image to get the edge detection image. Finally, this paper uses the adaptively transformed image and the edge detection image to obtain the final processing result through a weighted fusion algorithm. Results: The contrast of the face image after preprocessing is appropriate, and the details of the image are obvious. Conclusion: The method proposed in this paper can enhance the face image while retaining more face details, without human-computer interaction, and has lower computational complexity degree.

Randomly Attracted Rough Firefly Algorithm for histogram based fuzzy image clustering

2021 ◽  
Vol 216 ◽  
pp. 106814
Author(s):  
Krishna Gopal Dhal ◽  
Arunita Das ◽  
Swarnajit Ray ◽  
Jorge Gálvez
Keyword(s):  
Firefly Algorithm ◽  
Image Clustering ◽  
Fuzzy Image

scholarly journals Cashmere and wool identification based on convolutional neural network

2021 ◽  
Vol 16 ◽  
pp. 155892502110050
Author(s):  
Junli Luo ◽  
Kai Lu ◽  
Yueqi Zhong ◽  
Boping Zhang ◽  
Huizhu Lv
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Morphological Characteristics ◽  
Identification Accuracy ◽  
Test Set ◽  
Image Preprocessing ◽  
Softmax Regression ◽  
Wool Fibers ◽  
Chinese White ◽  
Multi Classification

Wool fiber and cashmere fiber are similar in physical and morphological characteristics. Thus, the identification of these two fibers has always been a challenging proposition. This study identifies five kinds of cashmere and wool fibers using a convolutional neural network model. To this end, image preprocessing was first performed. Then, following the VGGNet model, a convolutional neural network with 13 weight layers was established. A dataset with 50,000 fiber images was prepared for training and testing this newly established model. In the classification layer of the model, softmax regression was used to calculate the probability value of the input fiber image for each category, and the category with the highest probability value was selected as the prediction category of the fiber. In this experiment, the total identification accuracy of samples in the test set is close to 93%. Among these five fibers, Mongolian brown cashmere has the highest identification accuracy, reaching 99.7%. The identification accuracy of Chinese white cashmere is the lowest at 86.4%. Experimental results show that our model is an effective approach to the identification of multi-classification fiber.

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