A Rock Cracks Dynamic Recognition Algorithm Based on Fingerprint

2012 ◽  
Vol 466-467 ◽  
pp. 176-180
Author(s):  
Ling Ling Xiao ◽  
Yan Ting Liu
Keyword(s):  
Labor Force ◽  
Rock Fracture ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Texture Recognition ◽  
Detection Technology ◽  
Dynamic Recognition ◽  
Rock Texture

Traditional detection technology of rock cracks in mine is asking somebody to go deep into the mine and detect them. This method doesn’t only waste so much labor force, but also lose timeliness. It will bring amount of risk to the workers and the results will contain subjectivity. As complex texture of rock is similar to the fingerprint, we give a rock fracture recognition algorithm based on fingerprint. In this paper, the fingerprint recognition algorithm is introduced into the rock texture recognition, and to be improved by us. We demonstrate the algorithm is feasibility with experiments.

A New Method of Flame Image Segmentation

2012 ◽  
Vol 562-564 ◽  
pp. 1365-1368
Author(s):  
Shu Xian Zhu ◽  
Xue Li Zhu ◽  
Yong Jun Zhu
Keyword(s):  
Image Segmentation ◽  
Threshold Value ◽  
Recognition Algorithm ◽  
New Method ◽  
Current Application ◽  
Real Time Processing ◽  
Integral Projection ◽  
Otsu Method ◽  
Dynamic Recognition ◽  
Image Pattern

Separating flame image from background is a precondition and necessary link for identification for recognition algorithm. In current application, the Otsu method is a simple, effective and threshold value selection method suitable for real time processing, but the limitation of this method is it depends too much on the image pattern of “double peak histogram”. To overcome the weakness and shortcoming of Otsu method, this paper puts forward a new method based on grayscale integral projection, for flame image segmentation and positioning. Experiments have proved that this method not only can separate a flame image from background correctly, but also can position the center of a flame image quickly so as to provide the important criterion for dynamic recognition of flame image.

Multi-Template Processing of Fingerprint Recognition Algorithm

2014 ◽  
Vol 610 ◽  
pp. 332-338
Author(s):  
Lian Ying Zou ◽  
Ying Zhou ◽  
Xiang Dong ◽  
Yu Chen
Keyword(s):  
Image Processing ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Ridge Line ◽  
Black And White ◽  
White Point ◽  
Direction Filter ◽  
Fingerprint Feature ◽  
Line Texture

Using multi-template processing algorithm, the fingerprint features are accurately collected. Through normalization, make the black and white point contrast of the fingerprint image more obviously, strengthen the ridge line texture. Direction calculating algorithm is based on the grey value of the neighborhood pixels. It can be implemented simply and speedily. Through direction filter, noises can be removed, and the contrast of the fingerprint’s ridge lines and valley lines can be enhanced. After binary converting, all information of the fingerprint is stored with 0 and 1. The effect of thinning is to make the fingerprint image more distinct to extract the fingerprint feature point easily. These steps had been implemented on Altera DE2 board with HDL codes. The experimental results indicate that the multi-template algorithm of fingerprint image processing is correct and practicable.

Automatic Real-Time Identification of Fingerprint Images Using Wavelets and Gradient of Gaussian

1997 ◽  
Vol 07 (05) ◽  
pp. 433-440 ◽  
Author(s):  
Woo Kyu Lee ◽  
Jae Ho Chung
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Type I Error ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Type I ◽  
Type Ii ◽  
Type Ii Error ◽  
Local Orientation ◽  
Real Time Identification

In this paper, a fingerprint recognition algorithm is suggested. The algorithm is developed based on the wavelet transform, and the dominant local orientation which is derived from the coherence and the gradient of Gaussian. By using the wavelet transform, the algorithm does not require conventional preprocessing procedures such as smoothing, binarization, thining and restoration. Computer simulation results show that when the rate of Type II error — Incorrect recognition of two different fingerprints as identical fingerprints — is held at 0.0%, the rate of Type I error — Incorrect recognition of two identical fingerprints as different ones — turns out as 2.5% in real time.

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