Research on QR 2-D Code Graphics Correction Algorithms Based on Morphological Expansion Closure and Edge Detection

Liu, Peng; Wen, Liu; Li, Qiang; Deng, Min; Dai, Yue; Nian, Yiying

doi:10.1007/978-981-15-3867-4_23

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…However, it is difficult to find the four midpoints w 5 , K, w 8 . In [9], the bent QR code is first roughly rectified using the four-vertex shape function, the four midpoints are then calculated using the rectification result, and further rectification is applied for the resulting image with an eight-node shape function. However, there is no guarantee that the midpoints obtained in the first step are accurate; hence, there is a problem with the rectification accuracy.…”

Section: First-stage Rectification Using Shape Functionmentioning

confidence: 99%

“…However, certain conditions are required, including knowledge of the radius of the cylinder [1,4] and the normal of the surface of the cylinder being perpendicular to the image plane of the camera [5,6]. In addition, various methods have employed linear transformation formulas for the rectification obtained from the correspondence of four vertices [7,8] and a transformation through the shape function [9], which expresses the deformation of a rectangle [10]; however, these methods have an accuracy problem when the curvature is large.…”

Section: Introductionmentioning

confidence: 99%

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Detection and rectification method for bent QR code recognition using convolutional neural networks

Tanaka

2023

Eng. Res. Express

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This paper proposes a method for decoding a bent quick-response code attached to a cylinder. The proposed method consists of two-stage image rectification using the shape function employed in a finite-element-method-based deformation analysis and a pix2pix network, which is a type of generative adversarial network. Rectification based on the shape function requires eight feature points, called nodes, of the bent code. A stacked hourglass network, a convolutional neural network used for human pose estimation, is used to detect these eight nodes. The experimental results show that, compared with other methods, the proposed method can more accurately decode bent codes with larger degrees of curvature.

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Section: First-stage Rectification Using Shape Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and rectification method for bent QR code recognition using convolutional neural networks

Tanaka

2023

Eng. Res. Express

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“…Li et al [27] proposed a method of correcting the QR code using the radius of cylinder to recognize the QR code whose curvature distortion occurred from being attached to the cylinder. Peng et al [28] corrected the distortion of the QR code by applying affine transformation to the contour of the QR code detected by the edge detection algorithm as the minimum bounding box surrounding the QR code.…”

Section: Position Estimation Based On the Qr Code Recognitionmentioning

confidence: 99%

“…In this study, the QR code area in the video taken with the monocular camera mounted on the drone is detected using the segmentation model. If the QR code is detected using the object detection model, the minimum bounding box containing the QR code is detected [24,28]. The bounding box, which is a result of the object detection model, includes a background.…”

Section: Qr Code Segmentation and Decodingmentioning

confidence: 99%

3D Position Estimation of Objects for Inventory Management Automation Using Drones

Yoon,

Kim,

Youn

et al. 2023

Applied Sciences

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With the recent development of drone technology, drones are being used in various fields. Drones have the advantage of being equipped with various devices to move freely and perform various tasks. In the field of inventory management, many studies have been conducted into management automation based on the drone. Drones scan a marker, such as a quick response code (QR code), attached to the shelves to obtain location information of the shelves on which the inventory is loaded. At the same time, drones perform inventory management by scanning the marker attached to the inventory to obtain inventory information. However, unlike indoor warehouses, where grids or shelves are well-defined, a storage yard is not fixed in the location where the inventory is stored. It is difficult to recognize the loading position from the marker for a storage yard without shelves and grids. Furthermore, the loading position of the inventory is not fixed. For the automation of inventory management of warehouses where shelves and grids are undefined, this paper proposes a framework that estimates the inventory 3D position in the video frame based on a deep learning model. The proposed framework uses the image segmentation model to detect and decode the marker in the video frame to estimate the 3D position of a drone and inventory. In addition, the estimated inventory 3D position is corrected using the continuity of the video frame. Experiment results on the video dataset verified that the proposed framework improved the 3D position estimation performance of the inventory. Consequently, efficient inventory management based on drones can be performed through the proposed framework for the 3D position estimation of inventory in all types of warehouses.

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Design and Development of a QR-Based Automated Form Filling Application

Mangla,

Ojasvi,

Singh

et al. 2024

2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO

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Research on QR 2-D Code Graphics Correction Algorithms Based on Morphological Expansion Closure and Edge Detection

Cited by 4 publications

References 8 publications

Detection and rectification method for bent QR code recognition using convolutional neural networks

Detection and rectification method for bent QR code recognition using convolutional neural networks

3D Position Estimation of Objects for Inventory Management Automation Using Drones

Design and Development of a QR-Based Automated Form Filling Application

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