A Method for Extracting QR Code from Complex Background Based on Morphology

2012 ◽  
Vol 239-240 ◽  
pp. 1466-1471
Author(s):  
Xu Yang ◽  
Xiang Gao ◽  
Si Qiang Jia ◽  
Qi Yong Lu
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Perspective Transformation ◽  
Pattern Detection ◽  
Corner Detection ◽  
Qr Code ◽  
Gravity Center ◽  
Complex Background ◽  
Region Growth ◽  
Seed Pixel

In this paper we propose a new QR code extracting method based on morphology. Most of the time, locating Finder patterns is a significant part of QR code extraction. On the basis of traditional Finder Pattern detection method which checks whether certain areas meet 1:1:3:1:1 in both vertical and horizontal directions, we further refine the true Finder Patterns from several candidate areas through acreage proportion and gravity center detection, so as to eliminate interference from complex background. After image segmentation and getting the true finder patterns, other than the traditional method such as edge detection, we introduce the algorithm of region growth, along with choosing one seed pixel from obtained finder patterns to roughly figure out the QR code area. Eventually, by combining corner detection and inverse perspective transformation, we accomplish the extraction of QR code. Experiment results show that this method has robust correction capability from complex background and QR code deformation.

scholarly journals Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2325
Author(s):  
Xinyu Hu ◽  
Qi Chen ◽  
Xuhui Ye ◽  
Daode Zhang ◽  
Yuxuan Tang ◽  
...  
Keyword(s):  
Detection Method ◽  
Region Growing ◽  
Distribution Area ◽  
Color Information ◽  
Microscopic Images ◽  
Micro Particles ◽  
True Value ◽  
Complex Background ◽  
The World ◽  
Region Growth

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.

scholarly journals Measurement of Apparent Building Damage by Using a Single Image

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Junda Lyu ◽  
Jiacheng Gao ◽  
Dejun Gao
Keyword(s):  
Measurement Errors ◽  
Quantitative Measurement ◽  
Detection Method ◽  
Perspective Transformation ◽  
Surface Damage ◽  
Corner Detection ◽  
Area Measurement ◽  
Single Image ◽  
Damage Area ◽  
Selection Of

In the proposed detection method, a single image was used for the rapid quantitative measurement of the damage area of a building. By using cross-ratio invariability between real and image coordinates, the damage area and the side lengths of the selected region were measured using the vanishing point, the vanishing line, and a line segment with a known length. Perspective transformation and image binarisation were performed on the selected frame to convert the length into area information. The damage area was obtained rapidly from the proportion of pixels in the damage region to pixels in the selected region. Corner detection and subpixel methods were combined to determine measurement errors because of the selection of measurement points. The experimental results from the example test revealed that quantitative area measurement of surface damage on buildings could be realised using the proposed method.

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

Vehicles Categorization in Complex Background

2014 ◽  
Vol 998-999 ◽  
pp. 708-711
Author(s):  
Ying Zhuo Xiang ◽  
Dong Mei Yang ◽  
Ji Kun Yan
Keyword(s):  
Detection Method ◽  
Line Length ◽  
Line Detection ◽  
View Point ◽  
Training Set ◽  
Large Variability ◽  
Linear Classifier ◽  
Complex Background ◽  
Novel Approach ◽  
Dimension Characteristic

This paper presents a novel approach to categorize multi-view vehicles in complex background using only two dimension characteristic vectors instead of high dimension vectors. Vehicles have large variability of models and the view-point makes the appearance change dramatically. Significant characteristics should be chosen as the evidence to categorize. In this paper, we categorize the vehicles into two categories – cars and lorries. Line detection method is used and calculating the average line length and the number of parallel lines as the two characteristics. A linear classifier is trained using 30 different view cars and lorries as the training set and an 10 additional different cars and lorries as the testing set.

scholarly journals Efficient 3D stereo vision stabilization for multi-camera viewpoints

2019 ◽  
Vol 8 (3) ◽  
pp. 882-889
Author(s):  
Sharif Shah Newaj Bhuiyan ◽  
Othman O. Khalifa
Keyword(s):  
Stereo Vision ◽  
Linear Order ◽  
Spanning Tree ◽  
Detection Method ◽  
Corner Detection ◽  
Geometric Transformation ◽  
Image Stabilization ◽  
Tree Algorithm ◽  
Stabilization Process ◽  
Key Points

In this paper, an algorithm is developed in 3D Stereo vision to improve image stabilization process for multi-camera viewpoints. Finding accurate unique matching key-points using Harris Laplace corner detection method for different photometric changes and geometric transformation in images. Then improved the connectivity of correct matching pairs by minimizing the global error using spanning tree algorithm. Tree algorithm helps to stabilize randomly positioned camera viewpoints in linear order. The unique matching key-points will be calculated only once with our method. Then calculated planar transformation will be applied for real time video rendering. The proposed algorithm can process more than 200 camera viewpoints within two seconds.

Multi-feature SUSAN corner detection method

2005 ◽  
Author(s):  
Xun Wang ◽  
Jianqiu Jin ◽  
Yun Ling ◽  
Zhaoyi Jiang
Keyword(s):  
Detection Method ◽  
Corner Detection

Video Vehicle Detection Method Based on Multiple Color Space Information Fusion

2012 ◽  
Vol 546-547 ◽  
pp. 721-726
Author(s):  
Hong Xiang Shao ◽  
Xiao Ming Duan
Keyword(s):  
Information Fusion ◽  
Traditional Method ◽  
Detection Method ◽  
Color Space ◽  
Vehicle Detection ◽  
Data Accuracy ◽  
Traffic Information ◽  
Detection Accuracy ◽  
Relative Independence ◽  
Hsi Color Space

A detection method which selective fuses the nine detection results of RGB, YCbCr and HSI color space according to the image color space relative independence of each component and complementarities is approached in order to improve vehicle video detection accuracy. The method fuses three different detection results in nine components by the value of H when the value of both S and I are higher and does another three detection results when the value of both S and I are smaller. Experiments show that the method compared to the traditional method using only the detection results of the brightness component improved substantial, reduced empty of the detected vehicle a large extent and increased traffic information data accuracy depending on the detection result.

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