QR code is a commonly used two-dimensional barcode employed in a variety of applications. To simplify the detection and segmentation of QR code, its structure incorporates three finder patterns. Sometimes, code sides are cropped due to printing issues or overlapping. However, most recognition methods refuse to work if at least one of three finder patterns is missing or damaged. This paper proposes an approach that allows reading such codes. It is based on utilizing alignment patterns alongside with finder patterns and the RANSAC scheme to estimate a projective transform that maps the symbol modules to the input image. The effectiveness of the proposed approach is evaluated using the generated dataset called SE-QR-SYN-500. It simulates cropped QR codes, both with and without projective distortions, and is published for the open use. The existing open-source solutions show zero accuracy on this dataset. In comparison, our implementation of the proposed approach demonstrates 77.8% accuracy for straight QR codes and 76.4% accuracy for QR codes with projective distortions.
This paper presents a method for metric rectification of planar objects that preserves angles and length ratios. An inner structure of an object is assumed to follow the laws of Manhattan World i.e. the majority of line segments are aligned with two orthogonal directions of the object. For that purpose we introduce the method that estimates the position of two vanishing points corresponding to the main object directions. It is based on an original optimization function of segments that estimates a vanishing point position. For calculation of the rectification homography with two vanishing points we propose a new method based on estimation of the camera rotation so that the camera axis is perpendicular to the object plane. The proposed method can be applied for rectification of various objects such as documents or building facades. Also since the camera rotation is estimated the method can be employed for estimation of object orientation (for example, during a surgery with radiograph of osteosynthesis implants). The method was evaluated on the MIDV-500 dataset containing projectively distorted images of documents with complex background. According to the experimental results an accuracy of the proposed method is better or equal to the-state-of-the-art if the background occupies no more than half of the image. Runtime of the method is around 3ms on core i7 3610qm CPU.
The paper considers the problem of images cropping obtained by projective transformation of source images. The problem is highly relevant to analysis of projective distorted images. We propose two cropping algorithms based on estimation of pixel stretching under the transformation. The algorithms use the ratio of pixel neighborhood areas and the ratio of their chord lengths. The methods comparison is conducted by estimation of cropped background relative areas. The experiment uses real dataset containing projective distorted images of the pages of Russian civil passports. The method based on chord lengths ratio shows better results on highly distorted images.
The paper considers the problem of estimating a transform connecting two images of one plane object. The method based on RANSAC is proposed for calculating the parameters of projective transform which uses points and lines correspondences simultaneously. A series of experiments was performed on synthesized data. Presented results show that the algorithm convergence rate is significantly higher when actual lines are used instead of points of lines intersection. When using both lines and feature points it is shown that the convergence rate does not depend on the ratio between lines and feature points in the input dataset.
The important part of the system of a planar rectangular object analysis is the localization: the estimation of projective transform from template image of an object to its photograph. The system also includes such subsystems as the selection and recognition of text fields, the usage of contexts etc. In this paper three localization algorithms are described. All algorithms use feature points and two of them also analyze near-horizontal and near- vertical lines on the photograph. The algorithms and their combinations are tested on a dataset of real document photographs. Also the method of localization quality estimation is proposed that allows configuring the localization subsystem independently of the other subsystems quality.
KEYWORDS: Chromium, Detection and tracking algorithms, Chemical elements, Analytical research, Machine vision, Distortion, Target recognition, Denoising, Information science, Control systems
The work is devoted to the research on the calculation of a projective transformation, which arises in the problems in machine vision. The details of the calculation of projective transformation and found specificities of mathematical libraries implementations are carefully analyzed. The comparisons of different approaches are provided in terms of both productivity and accuracy, using both artificially generated and real data.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.