Automatic Object Classification and Image Retrieval by Sobel Edge Detection

2014 ◽  
pp. 196-201
Keyword(s):  
Edge Detection ◽  
Comparative Study ◽  
Image Retrieval ◽  
Numerical Experiments ◽  
Object Classification ◽  
Similarity Coefficient ◽  
Cosine Similarity ◽  
Retrieval Algorithms ◽  
Sobel Edge Detection ◽  
Classification Tool

A comparative study of ability of the proposed novel image retrieval algorithms is performed to provide automated object classification invariant of rotation, translation, and scaling. Simple cosine similarity coefficient methods are analyzed. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: the processing of the whole image and the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as an automated object classification tool without special image pre-processing.

Algorithm for Automated Iris Recognition Using Biorthogonal Wavelets

2014 ◽  
pp. 157-163
Keyword(s):  
Comparative Study ◽  
Image Retrieval ◽  
Numerical Experiments ◽  
Iris Recognition ◽  
Biorthogonal Wavelet ◽  
Biorthogonal Wavelets ◽  
Wavelet Method ◽  
Retrieval Algorithms ◽  
Identification Of Persons ◽  
Wavelet Methods

A comparative study of ability of two novel image retrieval algorithms to provide automated touch-free identification of persons by iris recognition is presented. Namely, applied biorthogonal wavelet methods and the SVD-Free Latent Semantic method are analyzed. Moreover, in case of the applied biorthogonal wavelet method, two approaches were tested and compared: the processing of the whole image and the processing of the image converted to a vector. The point is that different methods for getting rid of the noise were successfully applied in both cases. Numerical experiments on a real biometric database indicate feasibility of the presented approach as an automated iris recognition tool without special image pre-processing.

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.

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