scholarly journals Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Omar Lopez-Rincon ◽  
Oleg Starostenko ◽  
Vicente Alarcon-Aquino ◽  
Juan C. Galan-Hernandez
Keyword(s):  
Recognition Rate ◽  
Qr Code ◽  
Large Object ◽  
Position Detection ◽  
Object Based ◽  
Geometrical Features ◽  
Particular Solution ◽  
Variable Illumination ◽  
Low Performance ◽  
Iterative Filtering

Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB-) based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

scholarly journals Frame-Based Facial Expression Recognition Using Geometrical Features

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Anwar Saeed ◽  
Ayoub Al-Hamadi ◽  
Robert Niese ◽  
Moftah Elzobi
Keyword(s):  
Facial Expression ◽  
Prior Knowledge ◽  
State Of The Art ◽  
Recognition Rate ◽  
Human Interaction ◽  
Expression Recognition ◽  
Acoustic Data ◽  
Geometrical Features ◽  
Benchmark Datasets ◽  
Neutral Expression

To improve the human-computer interaction (HCI) to be as good as human-human interaction, building an efficient approach for human emotion recognition is required. These emotions could be fused from several modalities such as facial expression, hand gesture, acoustic data, and biophysiological data. In this paper, we address the frame-based perception of the universal human facial expressions (happiness, surprise, anger, disgust, fear, and sadness), with the help of several geometrical features. Unlike many other geometry-based approaches, the frame-based method does not rely on prior knowledge of a person-specific neutral expression; this knowledge is gained through human intervention and not available in real scenarios. Additionally, we provide a method to investigate the performance of the geometry-based approaches under various facial point localization errors. From an evaluation on two public benchmark datasets, we have found that using eight facial points, we can achieve the state-of-the-art recognition rate. However, this state-of-the-art geometry-based approach exploits features derived from 68 facial points and requires prior knowledge of the person-specific neutral expression. The expression recognition rate using geometrical features is adversely affected by the errors in the facial point localization, especially for the expressions with subtle facial deformations.

A study for indoor position detection using LED QR code and smart-terminal

ICTC 2011 ◽  
2011 ◽  
Author(s):  
Yongsik Kang ◽  
Junghoon Lee ◽  
Juyoung Lee ◽  
Jaesang Cha
Keyword(s):  
Qr Code ◽  
Position Detection

scholarly journals Fast Blind Deblurring of QR Code Images Based on Adaptive Scale Control

2021 ◽  
Author(s):  
Rongjun Chen ◽  
Zhijun Zheng ◽  
Junfeng Pan ◽  
Yongxing Yu ◽  
Huimin Zhao ◽  
...  
Keyword(s):  
Recognition Rate ◽  
Industrial Applications ◽  
Qr Code ◽  
Optical Noise ◽  
Qr Codes ◽  
Short Delay ◽  
Scale Division ◽  
Blind Deblurring ◽  
Evaluation Mechanism ◽  
Scale Control

AbstractWith the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications.

scholarly journals Research on QR image code recognition system based on artificial intelligence algorithm

2021 ◽  
Vol 30 (1) ◽  
pp. 855-867
Author(s):  
Lina Huo ◽  
Jianxing Zhu ◽  
Pradeep Kumar Singh ◽  
Pljonkin Anton Pavlovich
Keyword(s):  
Artificial Intelligence ◽  
Median Filter ◽  
Recognition Rate ◽  
Distortion Correction ◽  
Deformation Pattern ◽  
Qr Code ◽  
Two Dimensional ◽  
Image Code ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

Abstract The QR code recognition often faces the challenges of uneven background fluctuations, inadequate illuminations, and distortions due to the improper image acquisition method. This makes the identification of QR codes difficult, and therefore, to deal with this problem, artificial intelligence-based systems came into existence. To improve the recognition rate of QR image codes, this article adopts an improved adaptive median filter algorithm and a QR code distortion correction method based on backpropagation (BP) neural networks. This combination of artificial intelligence algorithms is capable of fitting the distorted QR image into the geometric deformation pattern, and QR code recognition is accomplished. The two-dimensional code distortion is addressed in this study, which was a serious research issue in the existing software systems. The research outcomes obtained after emphasizing on the preprocessing stage of the image revealed that a significant improvement of 14% is observed for the reading rate of QR image code, after processing by the system algorithm in this article. The artificial intelligence algorithm adopted has a certain effect in improving the recognition rate of the two-dimensional code image.

scholarly journals Smart image recognition system based on machine vision

2021 ◽  
Vol 2083 (4) ◽  
pp. 042049
Author(s):  
Lei Li ◽  
Fenggang Liu
Keyword(s):  
Machine Vision ◽  
High Speed ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Accurate Method ◽  
Recognition System ◽  
Experimental Results ◽  
Movement Speed ◽  
Qr Code ◽  
Two Dimensional

Abstract This paper proposes an efficient and accurate method of two-dimensional code recognition for industrial actual projects, and develops a high-speed and batch two-dimensional code recognition system based on machine vision. Firstly, according to the position of the QR code in the target subspace, a method to locate the region of interest of each QR code by using geometric relationship and batch processing QR code is proposed. On this basis, Gaussian noise is added to simulate the possible noise in production practice, and the anti-noise ability of the system is evaluated. Finally, the relationship between system recognition rate and QR code movement speed is analyzed and the experimental results are compared. The experimental results show that the system can meet the requirement of real-time online detection.

Error handling in large, object-based Ada systems

1990 ◽  
Vol X (7) ◽  
pp. 91-103 ◽  
Author(s):  
Philippe Kruchten
Keyword(s):  
Large Object ◽  
Error Handling ◽  
Object Based

Video Based Visual Speech Feature Model Construction

2012 ◽  
Vol 182-183 ◽  
pp. 1367-1371
Author(s):  
Xi Bin Jia ◽  
Mei Xia Zheng
Keyword(s):  
Recognition Rate ◽  
Texture Features ◽  
Visual Speech ◽  
Feature Model ◽  
Single Feature ◽  
Geometrical Features ◽  
Motion Features ◽  
Geometrical Information ◽  
Speech Feature ◽  
Global And Local

This paper aims to give a solutions for the construction of chinese visual speech feature model based on HMM. We propose and discuss three kind representation model of the visual speech which are lip geometrical features, lip motion features and lip texture features. The model combines the advantages of the local LBP and global DCT texture information together, which shows better performance than the single feature. Equally the model combines the advantages of the local LBP and geometrical information together is better than single feature. By computing the recognition rate of the visemes from the model, the paper shows the HMM which describing the dynamic of speech, coupled with the combined feature for describing the global and local texture is the best model.

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