Direct Part Mark Bar Code Image Preprocessing

2015 ◽  
Vol 7 (3) ◽  
pp. 13-24
Author(s):  
Lingling Li ◽  
Tao Gao ◽  
Yaoquan Yang
Keyword(s):  
Recognition Algorithm ◽  
Code Region ◽  
Data Matrix ◽  
Background Region ◽  
Vertical Projection ◽  
Transform Method ◽  
Color Processing ◽  
Low Contrast ◽  
Bar Code ◽  
Intensity Changes

Due to factors such as ambient light and metal materials, the collected industrial DPM bar code images may exist uneven illumination, low contrast, color of background area is darker than bar code region and other harsh issues, while the existing 2D code recognition device can only recognize the type which bar code area color is darker than background region. Therefore, the quality of preprocessing effect is the key point to subsequent recognition algorithm. In this paper, the homomorphic filtering method is used to weaken the influence of uneven illumination firstly, which will enhance the image contrast degree. Then do horizontal and vertical projection, find the points with greater intensity changes in both directions, make the image into blocks, again use the classic Kittler binarization algorithm on each block, then use mathematical morphology method to standardize the dot data matrix images. Finally, an improved Hough transform method is used to detect the ‘L' type finder pattern accurately, then find its pixel value, if color of the background region is darker than the bar code area, do invert-color processing. The processing results of a set of industrial DPM bar code images confirm the effectiveness of the proposed method.

DPM 2D Bar Code Locating based on Corner Detection and Clustering Analysis

2015 ◽  
Vol 7 (3) ◽  
pp. 1-12
Author(s):  
Lingling Li ◽  
Tao Gao ◽  
Yaoquan Yang
Keyword(s):  
Scale Space ◽  
Corner Detection ◽  
Data Matrix ◽  
Low Contrast ◽  
Bar Code ◽  
Full Life Cycle ◽  
Density Based Clustering ◽  
Tracking Ability ◽  
Code Type ◽  
2D Bar Code

Industrial DPM bar code is achieved by means of laser etching on metal surface; it uses Data Matrix bar code image as the main carrier, which can ensure the full life cycle tracking ability. But due to the ambient light and metal materials, collected industrial DPM images may exist uneven illumination, low contrast and other issues. How to identify this code type quickly and accurately is a problem, and how to locate the bar code area accurately is even more critical and difficult. In this paper, according to the characteristics of frequently appeared cartesian points in DPM bar code area, an adaptive corner detection method based on curvature scale space is used to detect the corners effectively. Then for the corners gathered by clusters, an improved density-based clustering method is proposed to achieve precise positioning results. Experimental results show that the proposed algorithm has certain suppression ability for low contrast, illumination and blur deformation images and is superior to the traditional algorithms on positioning precision and accuracy.

Vehicle License Plate Recognition Based on Wavelet Transform and Vertical Edge Matching

2019 ◽  
Vol 34 (06) ◽  
pp. 2050016
Author(s):  
Zhongli Wang ◽  
Xiping Ma ◽  
Wenlin Huang
Keyword(s):  
Wavelet Transform ◽  
Road Traffic ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Vertical Edge ◽  
Vertical Projection ◽  
Edge Matching ◽  
Vehicle License Plate Recognition

With the improvement of our country’s economic level and quality of life, the numbers and scales of highway networks and motor vehicles are constantly expanding, which makes the current road traffic burden more and more serious. As an important means of traffic automation management, license plate recognition (LPR) technology plays an important role in traffic surveillance and control. However, the recognition rate and accuracy of the traditional license plate recognition methods still need to be improved. In the case of poor surrounding environment, it is prone to localization failure, vehicle license plate recognition errors or unrecognizable phenomena. Wavelet transform, as another landmark signal processing method after Fourier transform, has been widely used in the field of image processing. In China, the number of horizontal lines is usually larger than that of vertical lines. If the two vertical boundaries of the license plate can be detected successfully, the four angles of the license plate can be determined efficiently to complete the license plate positioning. In view of the advantages of wavelet transform technology and the characteristics of vehicle license plate, in this paper, a vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching is proposed. The edge of the license plate is detected by wavelet transform technology, and then the license plate is located by vertical edge matching technology. After the location is realized, the characters are segmented by vertical projection method and the characters are recognized by improved BP neural network algorithm. The experimental results show that the proposed vehicle license plate recognition algorithm based on wavelet transform and vertical edge matching performs well in algorithm performance, which provides a good reference for the development of vehicle license plate recognition system.

Quick identification of Data Matrix two-dimensional bar code based on chain code tracking

2013 ◽  
Vol 32 (11) ◽  
pp. 3206-3209
Author(s):  
Yi-zhao XU ◽  
Rui-lin BAI ◽  
Zhen-hong YU ◽  
Feng JI
Keyword(s):  
Data Matrix ◽  
Two Dimensional ◽  
Bar Code ◽  
Chain Code ◽  
Code Tracking

Data Hiding Scheme for Video Watermarking Using Horizontal and Vertical Coefficients of Single Level Discrete Wavelet Transform Method

2021 ◽  
pp. 2250044
Author(s):  
J. Jerisha Liby ◽  
T. Jaya
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Similarity Index ◽  
Video Watermarking ◽  
Data Matrix ◽  
Experimental Result ◽  
Video Frame ◽  
Discrete Wavelet ◽  
Transform Method ◽  
Single Level

This paper proposes a new watermarking algorithm based on a single-level discrete wavelet transform (DWT). This method initially chooses ‘[Formula: see text]’ number of carrier frames to hide the data. After estimating the carrier frames, each frame is separated into RGB frames. Each R, G, and B frames are decomposed using a single-level DWT. The horizontal and vertical coefficients are selected to embed the watermark information since small changes in the horizontal and vertical coefficients do not highly affect the quality of the video frame. The watermark image pixels are shuffled using a predetermined key before embedding. The shuffled pixels are converted to binary, and they are grouped into three data matrices. Each data matrix is embedded in horizontal and vertical coefficients of the R, G and B frames of the video frame. After embedding the data, the watermarked video is reconstructed using the original approximation coefficients, the embed coefficients, and the original diagonal coefficients. During the extraction process, the watermark is extracted from the horizontal and vertical coefficients of the watermarked video. Experimental result reveals that the proposed method outperforms other related methods in terms of video quality and structural similarity index measurement.

1D BAR CODE READING ON CAMERA PHONES

2007 ◽  
Vol 07 (03) ◽  
pp. 529-550 ◽  
Author(s):  
KONGQIAO WANG ◽  
YANMING ZOU ◽  
HAO WANG
Keyword(s):  
Research Effort ◽  
Recognition Rate ◽  
Poor Quality ◽  
Code Region ◽  
Camera Phone ◽  
Bar Code ◽  
Knowledge Based ◽  
Camera Phones ◽  
Class Reference ◽  
Correct Recognition Rate

The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.

Synchrosqueezing with short-time fourier transform method for trinary frequency shift keying encoded SSVEP

2020 ◽  
pp. 1-15
Author(s):  
Dechun Zhao ◽  
Xiaoxiang Li ◽  
Xiaorong Hou ◽  
Mingyang Feng ◽  
Renping Jiang
Keyword(s):  
Fourier Transform ◽  
Frequency Shift ◽  
Recognition Algorithm ◽  
Short Time Fourier Transform ◽  
Frequency Shift Keying ◽  
Transform Method ◽  
Coherent Demodulation ◽  
Reconstructed Signal ◽  
Shift Keying ◽  
Short Time

BACKGROUND: The frequencies that can evoke strong steady state visual evoked potentials (SSVEP) are limited, which leads to brain-computer interface (BCI) instruction limitation in the current SSVEP-BCI. To solve this problem, the visual stimulus signal modulated by trinary frequency shift keying was introduced. OBJECTIVE: The main purpose of this paper is to find a more reliable recognition algorithm for SSVEP-BCI based on trinary frequency shift keying modulated stimuli. METHODS: First, the signal modulated by trinary frequency shift keying is simulated by MATLAB. At different noise levels, the empirical mode decomposition, singular value decomposition, and synchrosqueezing with the short-time Fourier transform are used to extract the characteristic frequency and reconstruct the signal. Then, the coherent method is used to demodulate the reconstructed signal. Second, in the paradigm of BCI using trinary frequency shift keying modulated stimuli, the three methods mentioned above are used to reconstruct EEG signals, and canonical correlation analysis and coherent demodulation are used to recognize the BCI instructions. RESULTS: For simulated signals, it is found that synchrosqueezing with short-time Fourier transform has a better effect on extracting the characteristic frequencies. For the EEG signal, it is found that the method combining synchrosqueezing with short-time Fourier transform and coherent demodulation has a higher accuracy and information translate rate than other methods. CONCLUSION: The method combining synchrosqueezing with short-time Fourier transform and coherent demodulation proposed in this paper can be applied in the SSVEP system based on trinary frequency shift keying modulated stimuli.

scholarly journals Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3494
Author(s):  
Yongchae Kim ◽  
Hiroyuki Kudo
Keyword(s):  
Image Quality ◽  
Total Variation ◽  
Second Order ◽  
Ct Image ◽  
Low Contrast ◽  
New Class ◽  
Nonlocal Total Variation ◽  
Intensity Changes ◽  
Adequate Image Quality ◽  
The Cost

We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity changes for textures and low-contrast objects, which is a major limitation in improving image quality. The proposed nonlocal TV combines the first and second order derivatives to preserve smooth intensity changes well. Furthermore, to accelerate the iterative algorithm to minimize the cost function using the proposed nonlocal TV, we propose a proximal splitting based on Passty’s framework. We demonstrate that the proposed nonlocal TV method achieves adequate image quality both in sparse-view CT and low-dose CT, through simulation studies using a brain CT image with a very narrow contrast range for which it is rather difficult to preserve smooth intensity changes.

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