Remote QR code recognition based on HOG and SVM classifiers

Tribak, Hicham; Moughyt, Salah; Zaz, Youssef; Schaefer, Gerald

doi:10.1109/iac.2016.7905704

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The rough location of the isolating switch employs the SVM algorithm 50 combined with the Hog eigenvector of the switch, and this rough location is not the focus of this article. Instead, suppose that the rough location is finished and then we focus on exploiting feature constraint-based Hough transform for obtaining the center line of switch arm.…”

Section: Center Line Extraction Of Switch Armmentioning

confidence: 99%

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Luo

et al. 2020

International Journal of Advanced Robotic Systems

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To better monitor the state of isolating switches, an efficient binocular vision-based state measurement system is proposed in this article. Two optimal cameras are selected as the vision of our inspection system. Firstly, stereo calibration and distortion rectification are performed on acquired image pair. Secondly, to recover the three-dimensional information of switch, we propose a semi-global stereo matching method by using data- and structure-driven cost volume fusion and then optimizing raw disparity map with weighted- and edge discriminated-smoothness prior. Gradient content is enforced on the weight for suppressing small-weight-accumulation problem in weak-textured regions. Besides, Hough transform with feature constraints is implemented for removing the chaotic lines and extracting center line of the switch arm. Finally, based on the center line and corresponding disparity map of the switch arm, triangulation principle is used for calculating the true angle between the switch arm and insulator such that whether or not the isolating switch is fully closed can be detected. The experimental results demonstrate that the proposed stereo matching method can achieve good performance in Middlebury v.3 data set and switch images, and the system can precisely measure the state of switches.

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Section: Center Line Extraction Of Switch Armmentioning

confidence: 99%

State measurement of isolating switch using cost fusion and smoothness prior based stereo matching

Luo

et al. 2020

International Journal of Advanced Robotic Systems

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“…The provided characteristics of the proposed technique meet notably our needs, since it allows representing each pattern by only seven coefficients (which are unchangeable under rotation, scale change and translation) instead of its original structure (whole pattern image). The incorporation of the discussed technique has decreased dramatically the processing time, the limitations that we envisaged previously in our earlier proposed papers [5], [6] have been outperformed. A throughout overview of the proposed system is shown in Fig.…”

Section: Introductionmentioning

confidence: 96%

“…grayscale conversion and binary one. These conversions are respectively defined by (3) and (5). Since the binarization conversion is mostly sensitive to the over and under illuminations, a contrast balancing is inevitably required to be performed for the grayscale representation of each interest region and this before skipping to its binary conversion.…”

Section: Qr Code Patterns Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

QR Code Patterns Localization based on Hu Invariant Moments

Tribak¹,

Zaz²

2017

ijacsa

Self Cite

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Abstract-The widespread utilization of QR code and its coincidence with the swift growth of e-commerce transactions have imposed the computer vision researchers to continuously devise a variety of QR code recognition algorithms. The latter performances are generally limited due to two main factors. Firstly, most of them are computationally expensive because of the implemented feature descriptor complexities. Secondly, the evoked algorithms are often sensitive to pattern geometric deformations. In this paper a robust approach is proposed, in which the architecture is based on three distinct treatments among others: 1) An image quality assessment stage which evaluates the quality of the captured image in consideration that the presence of blur decreases significantly the recognition accuracy. 2) This stage is followed by an image segmentation based on an achromatic filter through which only the regions of interest are highlighted and consequently the execution time is reduced. 3) Finally, the Hu invariant moments technique is used as feature descriptor permitting removing false positives. This technique is implemented to filter out the set of extracted candidate QR code patterns, which have been roughly extracted by a scanning process. The Hu moments descriptor is able to recognize patterns independently of the geometric transformations they undergo. The experiments show that the incorporation of the aforementioned three stages enhances significantly the recognition accuracy along with a notable diminution of processing time. This makes the proposed approach adapted to embedded systems and devices with limited performances.

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“…For the latter way, object detection can use non-deep learning algorithms, such as detectors such as VJ, HOG and DPM, or deep learning algorithms, such as deep learning models such as Convolutional Neural Network (CNN) and Transformer. Tribak H et al [2] used Histogram of Oriented Gradient (HOG) detector and Support Vector Machine (SVM) to locate the QR code, but the detection time is long, and it takes nearly two seconds to recognize a single image. Yang Qingjiang et al [3] proposed an improved YOLO V3-ms algorithm to locate QR codes.…”

Section: Introductionmentioning

confidence: 99%