Barcodes are used in many commercial applications, thus fast and robust reading is important. There are many different types of barcodes, some of them look similar while others are completely different. In this paper we introduce new fast and robust deep learning detector based on semantic segmentation approach. It is capable of detecting barcodes of any type simultaneously both in the document scans and in the wild by means of a single model. The detector achieves state-of-the-art results on the ArTe-Lab 1D Medium Barcode Dataset with detection rate 0.995. Moreover, developed detector can deal with more complicated object shapes like very long but narrow or very small barcodes. The proposed approach can also identify types of detected barcodes and performs at real-time speed on CPU environment being much faster than previous state-of-the-art approaches.
The work of Cresot , 2015 @cite_0 is a solid baseline for 1D barcode detection. They evaluated their approach on Muenster and on extended ArTe-Lab 1D Medium barcode database (Artelab) provided by Zamberletti @cite_3 outperforming him on both datasets. The solution in @cite_0 seems to outperform @cite_1 despite it is hard to compare as they were evaluated on different datasets using slightly different metrics. Cresot's algorithm detects dark bars of barcodes using Maximal Stable Extremal Regions (MSER) followed by finding imaginary perpendicular to bars center line in Hough space. In 2016 Cresot came with a new paper @cite_11 improving previous results using a new variant of Line Segment Detector instead of MSER, which they called Parallel Segment Detector. @cite_9 proposes another bars detection method for 1D barcode detection, which is reported to be absolutely precise in real-time applications.
@cite_9 @cite_1 @cite_3 @cite_0 @cite_11
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Barcode reading mobile applications that identify products from pictures taken using mobile devices are widely used by customers to perform online price comparisons or to access reviews written by others. Most of the currently available barcode reading approaches focus on decoding degraded barcodes and treat the underlying barcode detection task as a side problem that can be addressed using appropriate object detection methods. However, the majority of modern mobile devices do not meet the minimum working requirements of complex general purpose object detection algorithms and most of the efficient specifically designed barcode detection algorithms require user interaction to work properly. In this paper, we present a novel method for barcode detection in camera captured images based on a supervised machine learning algorithm that identifies one-dimensional barcodes in the two-dimensional Hough Transform space. Our model is angle invariant, requires no user interaction and can be executed on a modern mobile device. It achieves excellent results for two standard one-dimensional barcode datasets: WWU Muenster Barcode Database and ArTe-Lab 1D Medium Barcode Dataset. Moreover, we prove that it is possible to enhance the overall performance of a state-of-the-art barcode reading algorithm by combining it with our detection method. The linear 1D barcode is the main tagging system for billions of products sold each day. Barcodes have many advantages but require a laser scanner for fast and robust scanning. Solutions exist to read barcodes from cell phones but they assume a carefully framed image within the field of view. This undermines the true potential of barcodes in a wide range of scenarios. In this paper we present a real time technique to detect barcodes in the wild from video streams. Our technique outperforms the state-of-the-art passive techniques both in accuracy and speed. Potential commercial applications enabled by such passive scanning system are also discussed in this paper. With the proliferation of built-in cameras barcode scanning on smartphones has become widespread in both consumer and enterprise domains. To avoid making the user precisely align the barcode at a dedicated position and angle in the camera image, barcode localization algorithms are necessary that quickly scan the image for possible barcode locations and pass those to the actual barcode decoder. In this paper, we present a barcode localization approach that is orientation, scale, and symbology (1D and 2D) invariant and shows better blur invariance than existing approaches while it operates in real time on a smartphone. Previous approaches focused on selected aspects such as orientation invariance and speed for 1D codes or scale invariance for 2D codes. Our combined method relies on the structure matrix and the saturation from the HSV color system. The comparison with three other real-time barcode localization algorithms shows that our approach outperforms the state of the art with respect to symbology and blur invariance at the expense of a reduced speed. Barcode technology is essential in automatic identification, and is used in a wide range of real-time applications. Different code types and applications impose special problems, so there is a continuous need for solutions with improved performance. Several methods exist for code localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need reliable automatic barcode localization, e.g. conveyor belts and automated production, where missed detections cause loss of profit. Our goal is to detect automatically, rapidly and accurately the barcode location with the help of extracted image features. We propose a new algorithm variant, that outperforms in both accuracy and efficiency other detectors found in the literature using similar ideas, and also improves on the detection performance in detecting 2D codes compared to our previous algorithm. There is increased interest in the use of color barcodes to encode more information per area unit than regular, black-and-white barcodes. For example, Microsoft's HCCB technology uses 4 or 8 colors per patch. Unfortunately, the observed color of a surface depends as much on the illuminant spectrum (and other viewing parameters) as on the surface reflectivity, which complicates the task of decoding the content of the barcode. A popular solution is to append to the barcode a “palette” with the reference colors. In this paper, we propose a new approach to color barcode decoding, one that does not require a reference color palette. Our algorithm decodes groups of color bars at once, exploiting the fact that joint color changes can be represented by a low-dimensional space. Decoding a group of bars (a “barcode element”) is thus equivalent to searching for the nearest subspace in a dataset. We also propose algorithms to select subsets of barcode elements that can be decoded with low error probability. Our experimental results show that our barcode decoding algorithm enables substantial information rate increase with respect to system that display a color palette, at a very low decoding error rate. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. This paper shows new algorithms and the implementations of image reorganization for EAN QR barcodes in mobile phones. The mobile phone system used here consists of a camera, mobile application processor, digital signal processor (DSP), and display device, and the source image is captured by the embedded camera device. The introduced algorithm is based on the code area found by four corners detection for 2D barcode and spiral scanning for 1D barcode using the embedded DSP. This algorithm is robust for practical situations and the DSP has good enough performance for the real-time recognition of the codes. The performance of our image processing is 66.7 frames sec for EAN code and 14.1 frames sec for QR code image processing, and this is sufficient performance for practical use. The released mobile phone had performance of 5-10 frames sec including OS and subsystem overheads. In this paper we present an algorithm for the recognition of 1D barcodes using camera phones, which is highly robust regarding the the typical image distortions. We have created a database of barcode images, which covers typical distortions, such as inhomogeneous illumination, reflections, or blurriness due to camera movement. We present results from experiments with over 1,000 images from this database using a Matlab implementation of our algorithm, as well as experiments on the go, where a Symbian C++ implementation running on a camera phone is used to recognize barcodes in daily life situations. The proposed algorithm shows a close to 100 accuracy in real life situations and yields a very good resolution dependent performance on our database, ranging from 90.5 (640 × 480) up to 99.2 (2592 × 1944). The database is freely available for other researchers. The popular h-index used to measure scientific output can be described in terms of a pool of evaluated objects (the papers), a quality function on the evaluated objects (the number of citations received by each paper) and a sentencing line crossing the origin, whose intersection with the graph of the quality function yields the index value (in the h-index this is a line with slope 1). Based on this abstraction, we present a new index, the c-index, in which the evaluated objects are the citations received by an author, a group of authors, a journal, etc., the quality function of a citation is the collaboration distance between the authors of the cited and the citing papers, and the sentencing line can take slopes between 0 and ∞. As a result, the new index counts only those citations which are significant enough, where significance is proportional to collaboration distance. Several advantages of the new c-index with respect to previous proposals are discussed. Linear barcodes are the principal labeling system for retail products. Barcode reader apps found on smartphones always assume that the localization and framing of the barcode is performed manually by a sighted human operator. This is problematic for visually-impaired people since they don't know where to position the camera to scan the barcode. To solve this problem we propose a hand-free interface to detect barcode using a wearable camera. The user rotate a query product in front of him her and is informed when and where the barcode is visible. The challenge is to detect small barcodes at arm's length in a video with potentially large motion blur. In this paper we propose a novel technique for barcode detection using very little computation (adapted to wearable systems), presenting very good robustness to blur and size variations, and able to run on HD video streams. The proposed system perform significantly better than the state-of-the-art methods on existing public datasets, while being much faster. A new and challenging egocentric product video dataset is also provided with this paper. Colorizing two–dimensional (2-D) barcodes is a promising modality for improvement on design and data capacity. The number of colors employed in recent 2-D color barcodes is limited less than 8. To increase the number of recognizable colors, we propose a color recognition method which classifies 64 colors, using an extended color space and pattern recognition techniques. The proposed method employs 4 seed colors embedded within 64-color code to prevent influences from environment illumination. In the proposed method, we extend the color representation for each code color in 2-D barcode from three components to higher number component. And a classifier is trained using the extended colors captured from several environmental lighting conditions. The classifier is selectable. In this paper, artificial neural network (ANN), support vector machine (SVM) and nearest neighbor (NN) are employed as the classifier. The experimental results shows that the proposed method achieves MSER less than 3.0 . Comparing the number of seed colors, 4 seed color is comparable to that using 8 seed color. SVM achieves the best recognition performance of 1.17 SER. These results suggest that 64-color recognition is possible by the proposed method. Computing the epipolar geometry between cameras with very different viewpoints is often problematic as matching points are hard to find. In these cases, it has been proposed to use information from dynamic objects in the scene for suggesting point and line correspondences. We propose a speed up of about two orders of magnitude, as well as an increase in robustness and accuracy, to methods computing epipolar geometry from dynamic silhouettes. This improvement is based on a new temporal signature: motion barcode for lines. Motion barcode is a binary temporal sequence for lines, indicating for each frame the existence of at least one foreground pixel on that line. The motion barcodes of two corresponding epipolar lines are very similar, so the search for corresponding epipolar lines can be limited only to lines having similar barcodes. The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. With the ubiquitous of cellular phones, mobile applications with 2D barcodes have drawn a lot of attentions in recent years. However, the previous works for extracting 2D barcodes from an image do not consider the distortion resulted from camera shake. Moreover, the previous works for extracting 2D barcodes from an image do not take a complex background into account. In this paper, therefore, we propose an efficient and effective algorithm to extract 2D barcode from a complex background in a camera-shaken image. Compared with previous approaches, our algorithm outperforms in not only smaller running time but also higher accuracy of the barcode recognition. Context: Duplicate detection is a fundamental part of issue management. Systems able to predict whether a new defect report will be closed as a duplicate, may decrease costs by limiting rework and collecting related pieces of information. Goal: Our work explores using Apache Lucene for large-scale duplicate detection based on textual content. Also, we evaluate the previous claim that results are improved if the title is weighted as more important than the description. Method: We conduct a conceptual replication of a well-cited study conducted at Sony Ericsson, using Lucene for searching in the public Android defect repository. In line with the original study, we explore how varying the weighting of the title and the description affects the accuracy. Results: We show that Lucene obtains the best results when the defect report title is weighted three times higher than the description, a bigger difference than has been previously acknowledged. Conclusions: Our work shows the potential of using Lucene as a scalable solution for duplicate detection. Computing the epipolar geometry between cameras with very different viewpoints is often very difficult. The appearance of objects can vary greatly, and it is difficult to find corresponding feature points. Prior methods searched for corresponding epipolar lines using points on the convex hull of the silhouette of a single moving object. These methods fail when the scene includes multiple moving objects. This paper extends previous work to scenes having multiple moving objects by using the “Motion Barcodes”, a temporal signature of lines. Corresponding epipolar lines have similar motion barcodes, and candidate pairs of corresponding epipoar lines are found by the similarity of their motion barcodes. As in previous methods we assume that cameras are relatively stationary and that moving objects have already been extracted using background subtraction. Abstract The wide-baseline stereo problem, i.e. the problem of establishing correspondences between a pair of images taken from different viewpoints is studied. A new set of image elements that are put into correspondence, the so called extremal regions , is introduced. Extremal regions possess highly desirable properties: the set is closed under (1) continuous (and thus projective) transformation of image coordinates and (2) monotonic transformation of image intensities. An efficient (near linear complexity) and practically fast detection algorithm (near frame rate) is presented for an affinely invariant stable subset of extremal regions, the maximally stable extremal regions (MSER). A new robust similarity measure for establishing tentative correspondences is proposed. The robustness ensures that invariants from multiple measurement regions (regions obtained by invariant constructions from extremal regions), some that are significantly larger (and hence discriminative) than the MSERs, may be used to establish tentative correspondences. The high utility of MSERs, multiple measurement regions and the robust metric is demonstrated in wide-baseline experiments on image pairs from both indoor and outdoor scenes. Significant change of scale (3.5×), illumination conditions, out-of-plane rotation, occlusion, locally anisotropic scale change and 3D translation of the viewpoint are all present in the test problems. Good estimates of epipolar geometry (average distance from corresponding points to the epipolar line below 0.09 of the inter-pixel distance) are obtained. With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance.