Implementation of Barcode Localization Technique using Morphological Operations

Abstract

Barcode Localization is an extremely important task in Barcode Reading system which depends highly on imaging conditions and methods used for barcode localization. In this paper, we have presented a method for barcode localization which is based on basic morphological operations. The method introduced is implemented in MATLAB 2012 and is then examined for different types of test images such as images with skewed, blurry or multiple barcodes in an image. The method is then compared with some existing methods of literature on the basis of these test images. It has been found that the performance of the algorithm depends upon the proper choice of the switching element.

A Low-Power Distributed Visual Sensor Network for Real-Time Barcode Localization and Identification

“…Barcode detection is a well-researched area. Many approaches are proposed based on blob detector [21], bottom-hat filter [22], and mathematical morphology [23]. In centralized or distributed VSN, the cooperative information from the neighboring nodes improves the accuracy of barcode localization and identification.…”

“…After the barcode's location is predicted, the barcode localization is continued with two steps: the barcode detection inspired by the work of [23] and the refinement of the detection. As depicted in Figure 3, the procedures are listed as follows:…”

Distributed barcode tracking in visual sensor networks

“…The algorithm performs the following steps: (i) black top-hat transform, (ii) low-intensity thresholding to remove areas that are not barcodes, (iii) Otsu thresholding, and (iv) morphological filtering. In contrast to [4], the proposed method, which uses an additional thresholding step, does not perform contrast stretching and adopts a different method to determine the barcode's bounding box and its orientation.…”

“…Barcode detection is inspired by [4]. The algorithm performs the following steps: (i) black top-hat transform, (ii) low-intensity thresholding to remove areas that are not barcodes, (iii) Otsu thresholding, and (iv) morphological filtering.…”

“…The blobs are then filtered based on their shape and converted to a feature space before feature clustering is applied. Recent advances include [4] which combines [1] and [2].…”