QR Code Patterns Localization based on Hu Invariant Moments

Abstract

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.

Recognition of Perspective Distorted QR Codes with a Partially Damaged Finder Pattern in Real Scene Images

“…Prior published approaches for recognizing QR Codes in images can be divided into Finder Pattern based location methods [6][7][8][9][10][11][12] and QR Code region based location methods [13][14][15][16][17][18]. The first group locates a QR Code based on the location of its typical Finder Patterns that are present in its three corners.…”

“…In [12] (Tribak and Zaz) seven Hu invariant moments are applied to the Finder Pattern candidates obtained by initial scanning of an image and using Euclidean metrics they are compared with Hu moments of the samples. If the similarity is less than experimentally determined threshold, then the candidate is accepted.…”

Identification of QR Code Perspective Distortion Based on Edge Directions and Edge Projections Analysis

“…In Tribak and Zaz [ 10 ] instead of PCA, the authors use 7 Hu invariant moments as feature descriptors to preliminarily localized FP. The obtained feature vector is compared with feature vectors of set of training samples using Euclidean distance.…”

Intensity gradient based edge detection for pixelated communication systems

“…However, the QR code detection is still problem at low contrast and variable illumination [15–19]. To detect the four vertex coordinates of QR code image, many effective algorithms are proposed in the literature [20–25].…”