XXXX版英文翻译模板_简历_求职职场_实用文档.doc

英 文 翻 译姓 名： 张 衡学 号： 1102080234指导教师： 孙中桥专 业：信息管理与信息系统班 级： 2011级时 间：2015年6月25日信 息 管 理 系信息管理系英文翻译评价表学生姓名张衡性别男学号1102080234外文文献标题Fast Component-Based QR Code Detection in Arbitrarily Acquired Images外文文献出处 以下内容由指导教师填写（打勾“”选择）评价项目评价结论打勾评价结论打勾评价结论打勾是否外文期刊文献是否与本人论文相关完全相关一般不相关翻译工作量超负荷饱和不饱和翻译态度认真一般不认真翻译进度按计划执行一般未按计划执行翻译训练效果优良中差综 合 评 语（是否完成了规定任务、效果是否符合要求等）指导教师签名： 2015年 4 月 25日 制表：李铁治注1：此表与翻译文本一起装订；注2：为了加强学生外语应用能力的训练，每位同学至少选择毕业论文中一篇外文参考文献（10000英文字符），翻译成中文。外文文献及中文译文不装钉进论文中，只形成单行本放入档案袋即可。英文原文Fast Component-Based QR Code Detection in Arbitrarily Acquired ImagesAbstract Quick Response (QR) codes are a type of 2D bar- code that is becoming very popular, with several application possibilities. Since they can encode alphanumeric charac- ters, a rich set of information can be made available through encoded URL addresses. In particular, QR codes could be used to aid visually impaired and blind people to access web based voice information systems and services, and au- tonomous robots to acquire context-relevant information. However, in order to be decoded, QR codes need to be prop- erly framed, something that robots, visually impaired and blind people will not be able to do easily without guid- ance. Therefore, any application that aims assisting robots or visually impaired people must have the capability to de- tect QR codes and guide them to properly frame the code. A fast component-based two-stage approach for detecting QR codes in arbitrarily acquired images is proposed in this work. In the first stage, regular components present at three corners of the code are detected, and in the second stage ge- ometrical restrictions among detected components are veri- fied to confirm the presence of a code. Experimental results show a high detection rate, superior to 90 %, at a fast speed compatible with real-time applications.Keywords QR code Component-based detection Haar-like features Cascade classifier 1 IntroductionCompared to traditional 1D barcodes, 2D barcodes can en- code a larger amount of data, including alphanumeric char- acters. QR code, which stands for Quick Response Code, is a type of two-dimensional code introduced by Denso Wave in 1994 12. They have been designed to be easily found and to have its size and orientation determined under bad imaging conditions. In addition, ISO/IEC 18004 specifies an error correction scheme that can recover at most 30 % of occluded or damaged symbol area. These features make the QR code an extremely well succeeded technology in the area of barcodes. Figure 1 shows some examples of QR codes.Fig. 1 Samples of QR codeQR codes were initially used by the automotive industry to track vehicle parts during the manufacturing process 12. Nowadays, QR codes are most commonly used as “physical hyperlinks” (encoded hyperlinks), notably in the advertising industry, to connect places and objects to websites contain- ing additional context relevant information. Applications in education and entertainment 8, 23, 25, data and system se-curity 9, 16, 19, specific service offer 7, 27, among others are also emerging. Another possible application consists in helping visually impaired and blind people, or even robots, in several tasks such as indoor navigation, shopping, read- ing, and much more 2, 11, 13.The existing decoders, easily found for mobile devices, are able to work correctly only if codes are properly framed, with code region corresponding to at least 30 % of the im- age. When exploring an environment, visually impaired or robots will not be able to capture such images unless they are told where those codes are located. Thus, in order to make useful applications for them viable, detecting the presence of a code in an image is a necessary step prior to the decod- ing process.In related literature, the majority of work that mention QR code recognition or detection is actually concerned with improving image quality or determining the exact contours of the code rather than finding (i.e, deciding whether there is or there is not) a QR code symbol in an image 10, 20, 22. In fact, most of the images considered in those works are images acquired with the specific intent of capturing only the code symbol.Some few works that deal with the problem of finding QR codes propose solutions that rely on auxiliary informa- tion such as visual cues or RFID tags 11, 28. Although such approach is possible in controlled environments, it is not practical in general contexts.This work addresses the problem of detecting QR codes in arbitrarily acquired images without relying on auxiliary cues. The aim is not only to detect the presence of code symbols, but also to delimit their position within an image as accurately as possible. That would allow, additionally, to instruct an user or robot to properly approach the camera towards the code.To that end, a component-based, two-stage detection ap- proach is proposed. In the first stage, a square pattern, called finder pattern (FIP), located at three corners of any QR code is detected using a cascaded classifier trained according to the rapid object detection method proposed in 26. In the second stage, geometrical relationships among detected can- didate finder patterns are analyzed in order to verify if there are subgroups of three of them spatially arranged as three corners of a square region. A report on preliminary results of this approach has been previously published in 3. In this paper, a reformulated report of the approach and an extended set of results are presented. In particular, a detailed account of the second stage, including a formal description of the component aggregation algorithm, new results obtained with a larger training and test sets, discussions concerning addi- tional parameters that have not been examined in the previ- ous work, and quantitative results, not presented before, on QR code detection performance are presented.This text is organized as follows. In Sect. 2, important structural aspects of QR codes which will be explored in the proposed approach are first described. Then, an overview of the proposed two-stage approach is presented, together with a brief review on Viola-Jones method for rapid object de- tection, used in the first stage of the approach. In Sect. 3, the problem of aggregating information obtained in the first stage of the detection process is expressed in graph-based formalism and a simple algorithm to solve the problem is proposed. The procedure to determine appropriate values for the training parameters of the FIP detector, in an OpenCV implementation of Viola-Jones method, is discussed and described in Sect. 4. Then, in Sect. 5 experimental results to assess influence of some parameters in FIP as well as QR code detection, and frame rates achieved in video process- ing are reported. Although extensive experimental variation was carried out, not all possibilities arising from parameter variations were evaluated. Nevertheless, QR code detection rate superior to 90 % was observed on test images, suggest- ing that even better rates can be achieved. Finally, in Sect. 6 a summary of the main contributions of this work and some issues for future investigation are listed.2 Component-Based Approach for QR Code DetectionIn this section, some structural aspects of QR codes and detection requirements in a real-time application scenario that have motivated the proposed two-stage approach are first described. Then, an overview of the proposed approach followed by a brief review on Viola-Jones method for object detection is presented.2.1 QR CodeA QR code symbol, according to the ISO/IEC standard 18004, has a general structure that comprises data, version information, error correction code words, and the following regions illustrated in Fig. 2:Fig. 2 QR code structure (FIP: Finder Pattern / TP: Timing Pattern /AP: Alignment Pattern) a quiet zone around the symbol, three finder patterns (FIP) in the corners, two timing patterns (TP) between the finder patterns, and a certain number of alignment patterns (AP) inside the data areaThe internal organization of a code with respect to its structural components may vary depending on the amount of data encoded. The smallest units (black or white squares) that compose a QR code are called modules. There are various versions of the symbols, from 1 to 40, having distinct information storage capabilities that are determined by predefined number of modules in symbol area. Figure 3 shows an example of a version 1 code, highlighting the number of modules. Figure 4 shows additional examples that illustrate the relation between number of modules and number of encoded characters. Note that the alignment patterns occur more times in larger versions of the code.Fig. 3 QR code version 1 is composed of 2121 modulesFig. 4 Different versions of QR code symbols and respective number of encoded alphanumeric charactersThere is a visually distinctive pattern at three corners of QR codes, known as finder pattern (FIP). FIPs have been specially designed to be found in any search direction as the sequence of black (b) and white (w) pixels along any scan line that passes through its center preserve the special sequence and size ratio w:b:w:bbb:w:b:w, as can be seen in Fig. 5.Fig. 5 Black and white pixel proportions in finder patterns for diagonal (d), vertical (v) and horizontal (h) scan lines2.2 Detection RequirementsOne of the first requirements considering real-time applications is fast detection. Moreover, since there is no prior knowledge about the size of a code, another important feature of the detector is scale invariance. In addition, in order to succeed in uncontrolled environments, the detector should be also robust with respect to variation in illumination, rotation, and perspective distortion.In order to establish some limits to the detection task, in this work attention is paid to scale invariance and robustness with respect to variation in illumination condition, while rotations and perspective distortions are assumed to be small. Further than that, it is assumed that QR codes are printed in black and white (or at least dark foreground and light background). Color is not an issue since all processing considers gray tone images.2.3 Overview of the Proposed ApproachDirect detection of the whole code may not be an easy task because the internal structural organization may vary from code to code, depending on code version. Moreover, extraction of features of the code area, such as histograms or frequency information, can be greatly affected by image resolution, code scale, or noise. For instance, algorithms for recognition of QR codes frequently rely on the regularity of scan lines over the FIPs (see Fig. 5) to find them and then determine the exact contours of the code. However, that regularity may be disrupted in low quality images. Thus, in order to deal with unconstrained images, the processing algorithm should not depend on fine details.On the other hand, FIPs are typically the largest structures within a code, have a fixed shape, and appear in exactly three corners of all codes. These characteristics of FIPs suggest a component-based detection approach to the problem of detecting QR codes. The main idea of component-based detection 21 is the detection of parts of the object and then the analysis of detected parts in order to find a coherent arrangement forming the integral object. Arrangement of the parts can take into consideration geometrical restrictions. When the relation between parts can not be easily stated, machine learning based approaches can be used as in 1, 14.Taking the above observations into consideration, the proposed approach for detecting QR codes consists of a twostage method. In the first stage the aim is to detect FIPs, and in the second stage the goal is to integrate information about detected FIPs and decide whether subsets of three of them correspond to a QR code or not.In the first stage, it is desirable to maximize true positives (TP) while maintaining a controlled number of false positives (FP). Thus, for the detection of FIPs, some mechanism to control TP and FP should be available, and the detection process should fulfill the requirements listed above. In particular, it should be fast and invariant to scale and variation of illumination conditions. To meet these requirements, Viola-Jones rapid object detection method 26 is proposed for the first stage.In the second stage, the algorithm should be able to find sets of three FIPs that are the corners of a same QR code. To that end, geometrical restrictions as well as size related restrictions are considered, as detailed in Sect. 3.2.4 A Review on Viola-Jones Object Detection FrameworkViola-Jones object detection framework has the following characteristics: it uses simple features (Haar-like); it performs fast computation of features using integral image; it is based on a cascaded approach to train a classifier: each stage is trained in such a way as to achieve fixed TP and false alarm (FA) rates, using a boosting algorithm; it is very fast in practice since features are computed rapidly and the cascade is designed to discard the majority of negative samples in its early stages, eliminating the need for calculating responses of all stages to every sample considered.The Viola-Jones framework can be illustrated with the following concise description: The detection process consists in acquiring a complete sample set from the input image and submitting each sample to a cascade classifier whose stages have been trained by a boosting scheme that aggregates weak classifiers made of Haar-like features. These features are calculated in constant time using a matrix called integral image. Below some key concepts are reviewed and the detection process is explained.Sample set A sample consists of a sub-region of the image restricted to a window and the complete sample set of the image is obtained by sliding the window on the image and varying its size from a given minimum to the largest possible size in the image.Cascade classifier A cascade consists of a series of consecutive classifiers (stages) trained to reject samples that do not match the searched pattern while accepting the majority of positive samples and passing them to the next stage. A sample is said to be detected when it is accepted from the first to the last cascade stages, without rejection.Each stage is built from a set of weak classifiers aggregated in a committee by a boosting algorithm and can be seen as an independent classifier designed to obtain a very high hit rate (typically 99 % or more) with an acceptable false alarm rate (typically between 40 % and 60 %). Figure 6 illustrates the concept of a cascade classifier.Fig. 6 Illustration of the cascade approach to detectionBoosting Boosting works in rounds, iteratively training a set of weak classifiers while reweighting the training samples so that “hard” samples will have increased relative importance in the set. In each round the best weak classifier is selected to compose the resulting classifier 15. Thus, boosting can achieve the specified hit rate and false alarm rate as it increases the weak classifiers in the combination (as long as the features used have enough representational power). Every weak classifier is typically the result of the computation of a Haar-like feature followed by a binary threshold, although they can have more sophisticated forms like simple trees.Haar-like features The features used by the classifiers proposed in 26, inspired from 24, are based on feature prototypes shown in Fig. 7. In 18, this set has been extended with additional 45 degree rotated prototypes, shown in Fig. 8.Fig. 7 Basic feature prototype setFig. 8 Additional rotated feature prototypes in the extended setGiven a square window, all features used are derived from these prototypes by defining its width, height and relative position within the window. Some examples are shown in Fig. 9. Note, for instance, that features in Fig. 9(a) and Fig. 9(b) are both based on the same prototype.Fig. 9 Examples of Haar-like featuresFeature values are computed with respect to a sample that corresponds to a sub-region of the image under a sliding evaluation window. By definition, the value of a feature for each sample is the sum of the pixel values in the white rectangle area subtracted from the corresponding summation in the black rectangle area.3 Aggregation algorithmOnce FIP candidates are detected in the first stage, arrangement of three candidate FIPs that are likely to correspond to the corners of a QR code must be examined in the second stage. In this section, an algorithm that considers size, distance and angle restrictions to perform such examination is proposed. Note that besides these geometrical restrictions, additional information such as the number of overlapping detections or texture around the FIPs could be also used to rule out some subsets or support others. However, taking into consideration the requirement for fast processing, only size, distance, and angle information are considered.The proposed solution for aggregating FIP candidates is presented in Algorithm 1. Lines 1 to 3 correspond to vertex set creation. Lines 4 to 13 correspond to the creation of edges, whenever a pair of vertices satisfy the size and the distance criteria, with respective tolerances. The loop structure from line 14 to 23 is the main part of the algorithm, where cliques of size three whose edges pairwise satisfy the orien