2D barcode adaptive threshold algorithm based on wavelet multi-resolution

2012 ◽  
Author(s):  
Yongying Dong ◽  
Lin Liu ◽  
Sen Dong
Keyword(s):  
Adaptive Threshold ◽  
2D Barcode ◽  
Threshold Algorithm

Image Denoising with Contour-Based Directionlet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 3607-3611
Author(s):  
Huan An Xu ◽  
Guo Hua Peng ◽  
Zhe Liu
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Numerical Experiments ◽  
Adaptive Threshold ◽  
Perceptual Quality ◽  
Laplacian Pyramid ◽  
Low Frequencies ◽  
Threshold Algorithm ◽  
Image Transform

A novel mutiscale and directionally adaptive image transform called contour based directionlet tansform is presented. Directionlet transform (DT) has shown its charming performance in image processing, but it has scrambled frequencies. Laplacian Pyramid is employed here to separate the low frequencies before applying DT for avoiding the drawback. And an adaptive threshold algorithm is proposed for denoising. Numerical experiments are performed to assess the applicability of the proposed method. The obtained results show that the proposed scheme outperforms Wavelet and Directionlet transforms in terms of numerical and perceptual quality.

Locating Method of DPM 2D Barcode Image

2014 ◽  
Vol 989-994 ◽  
pp. 3865-3867 ◽  
Author(s):  
He Rong Zheng ◽  
Bing Shan ◽  
Zhi Liu
Keyword(s):  
Adaptive Threshold ◽  
High Light ◽  
Threshold Method ◽  
Filtering Method ◽  
2D Barcode ◽  
Otsu Method ◽  
Homomorphic Filtering ◽  
Positioning Method ◽  
Convex Hull Algorithm ◽  
Segmentation Problem

This paper studies the positioning method of DPM 2D barcode image. Because of the reflective problems of metal surface, the barcode image gotten will have uneven illumination, high light issues. In this paper, the homomorphic filtering method is used to enhance the image. Then the gradient projection is used to positioning the barcode region preliminary. We use an adaptive threshold method composed of Otsu method and neighborhood threshold method to solve the error segmentation problem. Finally the convex hull algorithm is applied to locate barcode region. Experiment results show that this method can effectively locate the barcode region quickly.

A New Method of Line Feature Generalization Based on Shape Characteristic Analysis

2011 ◽  
Vol 18 (4) ◽  
pp. 597-606 ◽  
Author(s):  
Hongshan Nie ◽  
Zhijian Huang
Keyword(s):  
Adaptive Threshold ◽  
Experimental Results ◽  
New Method ◽  
Characteristic Analysis ◽  
Line Feature ◽  
Shape Characteristic ◽  
Position Accuracy ◽  
Threshold Algorithm ◽  
Key Points ◽  
Line Features

A New Method of Line Feature Generalization Based on Shape Characteristic Analysis This paper presents a piecewise line generalization algorithm (PG) based on shape characteristic analysis. An adaptive threshold algorithm is used to detect all corners, from which key points are selected. The line is divided into some segments by the key points and generalized piecewise with the Li-Openshaw algorithm. To analyze the performance, line features with different complexity are used. The experimental results compared with the DP algorithm and the Li-Openshaw algorithm show that the PG has better performance in keeping the shape characteristic with higher position accuracy.

scholarly journals QRS Detection Based on Improved Adaptive Threshold

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Xuanyu Lu ◽  
Maolin Pan ◽  
Yang Yu
Keyword(s):  
High Speed ◽  
Detection Rate ◽  
Adaptive Threshold ◽  
Memory Storage ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Peak Finding ◽  
Threshold Algorithm ◽  
Complex Detection ◽  
Electrocardiogram Ecg

Cardiovascular disease is the first cause of death around the world. In accomplishing quick and accurate diagnosis, automatic electrocardiogram (ECG) analysis algorithm plays an important role, whose first step is QRS detection. The threshold algorithm of QRS complex detection is known for its high-speed computation and minimized memory storage. In this mobile era, threshold algorithm can be easily transported into portable, wearable, and wireless ECG systems. However, the detection rate of the threshold algorithm still calls for improvement. An improved adaptive threshold algorithm for QRS detection is reported in this paper. The main steps of this algorithm are preprocessing, peak finding, and adaptive threshold QRS detecting. The detection rate is 99.41%, the sensitivity (Se) is 99.72%, and the specificity (Sp) is 99.69% on the MIT-BIH Arrhythmia database. A comparison is also made with two other algorithms, to prove our superiority. The suspicious abnormal area is shown at the end of the algorithm and RR-Lorenz plot drawn for doctors and cardiologists to use as aid for diagnosis.

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