805 research outputs found

    Intelligent crack extraction and analysis for tunnel structures with terrestrial laser scanning measurement

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    An automatic and intelligent method for crack detection is significantly important, considering the popularity of large constructions. How to identify the cracks intelligently from massive point cloud data has become increasingly crucial. Terrestrial laser scanning is a measurement technique for three-dimensional information acquisition which can obtain coordinates and intensity values of the laser reflectivity of a dense point cloud quickly and accurately. In this article, we focus on the optimal parameter of Gaussian filtering to balance the efficiency of crack identification and the accuracy of crack analysis. The innovation of this article is that we propose a novel view of the signal-to-noise ratio gradient for Gaussian filtering to identify and extract the cracks automatically from the point cloud data of the terrestrial laser scanning measurement

    Deep Learning for Scene Text Detection, Recognition, and Understanding

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    Detecting and recognizing texts in images is a long-standing task in computer vision. The goal of this task is to extract textual information from images and videos, such as recognizing license plates. Despite that the great progresses have been made in recent years, it still remains challenging due to the wide range of variations in text appearance. In this thesis, we aim to review the existing issues that hinder current Optical Character Recognition (OCR) development and explore potential solutions. Specifically, we first investigate the phenomenon of unfair comparisons between different OCR algorithms caused due to the lack of a consistent evaluation framework. Such an absence of a unified evaluation protocol leads to inconsistent and unreliable results, making it difficult to compare and improve upon existing methods. To tackle this issue, we design a new evaluation framework from the aspect of datasets, metrics, and models, enabling consistent and fair comparisons between OCR systems. Another issue existing in the field is the imbalanced distribution of training samples. In particular, the sample distribution largely depended on where and how the data was collected, and the resulting data bias may lead to poor performance and low generalizability on under-represented classes. To address this problem, we took the driving license plate recognition task as an example and proposed a text-to-image model that is able to synthesize photo-realistic text samples. By using this model, we synthesized more than one million samples to augment the training dataset, significantly improving the generalization capability of OCR models. Additionally, this thesis also explores the application of text vision question answering, which is a new and emerging research topic among the OCR community. This task challenges the OCR models to understand the relationships between the text and backgrounds and to answer the given questions. In this thesis, we propose to investigate evidence-based text VQA, which involves designing models that can provide reasonable evidence for their predictions, thus improving the generalization ability.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

    문서 경계와 3차원 재구성에 기반한 문서 이미지 평판화

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    학위논문(석사) -- 서울대학교대학원 : 자연과학대학 수리과학부, 2022. 8. 현동훈.In recent days, most of the scanned images are obtained from mobile devices such as cameras, smartphones, and tablets rather than traditional flatbed scanners. Contrary to the scanning process of the traditional scanners, capturing process of mobile devices might be accompanied by distortions in various forms such as perspective distortion, fold distortion, and page curls. In this thesis, we propose robust dewarping methods which correct such distortions based on the document boundary and 3D reconstruction. In the first method, we construct a curvilinear grid on the document image using the document boundary and reconstruct the document surface in the three dimensional space. Then we rectify the image using a family of local homographies computed from the reconstructed document surface. Although some of the steps of the proposed method have been proposed separately in other research, our approach exploited and combined their advantages to propose a robust dewarping process in addition to improving the stability in the overall process. Moreover, we refined the process by correcting the distorted text region boundary and developed this process into an independent dewarping method which is concise, straight-forward, and robust while still producing a well-rectified document image.최근에는 대부분의 스캔된 이미지들이 전통적인 평판스캐너가 아닌 카메라, 스마트폰, 태블릿 PC 등의 휴대기기들로부터 얻어진다. 이전 스캐너들의 스캐닝 과정과는 다르게 휴대기기들을 이용한 이미지 캡쳐링 과정은 원근왜곡, 종이의 접힘으로 인한 왜곡, 그리고 종이의 휘어짐으로 인한 왜곡 등 다양한 왜곡들을 수반할 수 있다. 이 논문에서는 이러한 왜곡들을 제거할 수 있는 문서 경계와 3차원 재구성에 기반한 강력한 디워핑 방법을 제안하고자 한다. 첫번째 방법에서는, 문서 경계를 이용하여 문서 이미지 위에 곡선으로 이루어진 그리드를 만들고, 3차원 공간 상의 문서 곡면을 재구성한다. 그리고 재구성된 문서 곡면으로부터 계산된 국소적 호모그래피들을 이용하여 이미지를 수정한다. 우리가 제안하는 방법의 몇몇 단계는 다른 연구에서 개별적으로 사용된 경우도 있지만, 우리는 전체적인 과정에서 안정성을 높이는 동시에 각 방법의 장점들을 이용하고 조합하여 강력한 디워핑 방법을 제안한다. 이에 더하여, 우리는 왜곡된 텍스트 영역의 경계를 수정하여 전체적인 과정을 보완하였고, 이 절차를 간결하고, 직관적이며, 강력하면서도 좋은 결과를 내는 독립적인 디워핑 방법으로 개발하였다.1. Introduction 1 2. Review on Camera Geometry 6 2.1. Basic Camera Model 6 2.2. 3D Reconstruction Problem 8 3. Related Works 10 3.1. Dewarping Methods based on the Text-lines 10 3.2. Dewarping Methods based on the Document Boundary 11 3.3. Dewarping Methods based on the Grid Construction 12 3.4. Dewarping Methods based on the Document Surface Model in 3D Space 13 4. Document Image Dewarping based on the Document Boundary and 3D Reconstruction 15 4.1. Input Document Image Processing 17 4.1.1. Binarization of the Input Document Image 17 4.1.2. Perspective Distortion Removal using the Document Boundary 19 4.2. Grid Construction on the Document Image 21 4.3. 3D Reconstruction of the Document Surface 23 4.3.1. Geometric Model 23 4.3.2. Normalization of the Grid Corners 24 4.3.3. 3D Reconstruction of the Document Surface 26 4.4. Rectification of the Document Image under a Family of Local Homographies 27 4.5. Global Rectification of the Document Image 29 5. Document Image Dewarping by Straightening Document Boundary Curves 33 6. Conclusion 37 Appendix A. 38 A.1. 4-point Algorithm 38 A.2. Optimization of the Cost Function 40 Bibliography 42 Abstract (in Korean) 47 Acknowledgement (in Korean) 48석

    Food Dimension Estimation from A Single Image Using Structured Lights

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    Two-thirds the population in the United States of America are overweight or obese. The annual medical expenditures attributable to obesity may be as high as $215 billion per year. Obesity has been linked to many types of diseases, including cancer, type 2 diabetes, cardiovascular diseases, respiratory diseases, stroke and birth defects. Deaths related to obesity are estimated at 300,000 each year in the United States. In order to understand the etiology of the obesity epidemic and develop effective weight management methods for obese patients, accurate dietary data is an essential requirement. However, the current dietary assessment methods, which depend on self-reported data by the respondents, have an estimated 20% to 50% discrepancy from the intake. This large error severely affects obesity research.The recent rapid advances in electrical engineering and information technology fields have provided sophisticated devices and intelligent algorithms for dietary assessment. Considering portability and battery-life, systems installed with a single camera have the advantages of low cost, space saving, and low power consumption. Although severalmethods have been proposed to estimate food quantities and dimensions, many of these methods cannot be used in practice because of their inconvenience, and the requirement of calibration andmaintenance. In this dissertation, we present several approaches to food dimensional estimation using two types of structured lights. These approaches are low in costand power consumption, and suitable for small and portable image acquisition devices.Our first design uses structured laser beams as reference lights. Three identical laser modules are structured to form an equilateraltriangle on the plane orthogonal to the camera optical axis. A new method based on orthogonallinear regression is proposed to release restrictions on the laserbeams, so that the precision requirement for equilateral triangle can be relaxed. Based on the perspective projectiongeometry, intersections of structured laser beams andperspective projection rays are estimated, which construct a spatial planecontaining the projection of the objects of interest. The dimensions of the objects on theobserved plane are then calculated. In the second design, an LED diode is used as a reference light. A new algorithm is developed to estimate the object plane using the deformation of the observed ellipse.In order to provide a precise system calibration between the structured lights and the camera, an orthogonal linear regression method is proposed to calibrate the structured lights. Characteristics of the reference features are investigated. A color-based thresholding method is proposed to segment features. An ellipse fitting method is used to extract feature parameters. The extraction results of our algorithms are very close to those manually performed by human.Several experiments are performed to test our designs using both artificial and real food. Our experimental results show an average estimation error of lessthan 10%

    Advanced Radio Frequency Identification Design and Applications

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    Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID

    The visual standards for the selection and retention of astronauts

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    Literature search with abstracts on visual performance standards for selection and retention of astronaut

    Development of the new physical method for real time spot weld quality evaluation using ultrasound

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    Text-detection and -recognition from natural images

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    Text detection and recognition from images could have numerous functional applications for document analysis, such as assistance for visually impaired people; recognition of vehicle license plates; evaluation of articles containing tables, street signs, maps, and diagrams; keyword-based image exploration; document retrieval; recognition of parts within industrial automation; content-based extraction; object recognition; address block location; and text-based video indexing. This research exploited the advantages of artificial intelligence (AI) to detect and recognise text from natural images. Machine learning and deep learning were used to accomplish this task.In this research, we conducted an in-depth literature review on the current detection and recognition methods used by researchers to identify the existing challenges, wherein the differences in text resulting from disparity in alignment, style, size, and orientation combined with low image contrast and a complex background make automatic text extraction a considerably challenging and problematic task. Therefore, the state-of-the-art suggested approaches obtain low detection rates (often less than 80%) and recognition rates (often less than 60%). This has led to the development of new approaches. The aim of the study was to develop a robust text detection and recognition method from natural images with high accuracy and recall, which would be used as the target of the experiments. This method could detect all the text in the scene images, despite certain specific features associated with the text pattern. Furthermore, we aimed to find a solution to the two main problems concerning arbitrarily shaped text (horizontal, multi-oriented, and curved text) detection and recognition in a low-resolution scene and with various scales and of different sizes.In this research, we propose a methodology to handle the problem of text detection by using novel combination and selection features to deal with the classification algorithms of the text/non-text regions. The text-region candidates were extracted from the grey-scale images by using the MSER technique. A machine learning-based method was then applied to refine and validate the initial detection. The effectiveness of the features based on the aspect ratio, GLCM, LBP, and HOG descriptors was investigated. The text-region classifiers of MLP, SVM, and RF were trained using selections of these features and their combinations. The publicly available datasets ICDAR 2003 and ICDAR 2011 were used to evaluate the proposed method. This method achieved the state-of-the-art performance by using machine learning methodologies on both databases, and the improvements were significant in terms of Precision, Recall, and F-measure. The F-measure for ICDAR 2003 and ICDAR 2011 was 81% and 84%, respectively. The results showed that the use of a suitable feature combination and selection approach could significantly increase the accuracy of the algorithms.A new dataset has been proposed to fill the gap of character-level annotation and the availability of text in different orientations and of curved text. The proposed dataset was created particularly for deep learning methods which require a massive completed and varying range of training data. The proposed dataset includes 2,100 images annotated at the character and word levels to obtain 38,500 samples of English characters and 12,500 words. Furthermore, an augmentation tool has been proposed to support the proposed dataset. The missing of object detection augmentation tool encroach to proposed tool which has the ability to update the position of bounding boxes after applying transformations on images. This technique helps to increase the number of samples in the dataset and reduce the time of annotations where no annotation is required. The final part of the thesis presents a novel approach for text spotting, which is a new framework for an end-to-end character detection and recognition system designed using an improved SSD convolutional neural network, wherein layers are added to the SSD networks and the aspect ratio of the characters is considered because it is different from that of the other objects. Compared with the other methods considered, the proposed method could detect and recognise characters by training the end-to-end model completely. The performance of the proposed method was better on the proposed dataset; it was 90.34. Furthermore, the F-measure of the method’s accuracy on ICDAR 2015, ICDAR 2013, and SVT was 84.5, 91.9, and 54.8, respectively. On ICDAR13, the method achieved the second-best accuracy. The proposed method could spot text in arbitrarily shaped (horizontal, oriented, and curved) scene text.</div

    Ein mobiler Serviceroboter zur Automatisierung der Probenahme und des Probenmanagements in einem biotechnologischen Pilotlabor

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    Scherer T. A mobile service robot for automisation of sample taking and sample management in a biotechnological pilot laboratory. Bielefeld (Germany): Bielefeld University; 2004.In biotechnologischen Laboratorien ist die Qualität der typischerweise pharmazeutischen Produkte ein wortwörtlich lebenswichtiges Ziel. Die Qualität der Zellkultivierungen wurde historisch nur durch off-line Messungen von physikalischen Prozessparametern wie pH und pO2 sichergestellt. Biologische Parameter wie die Zelldichte und -viabilität wurden nur off-line gemessen, weil das dazu notwendige Probenmanagement hochkomplizierte Manipulationen und Analysen beinhaltet und deshalb nicht automatisiert werden konnte. Es gibt zwar mehrere automatisierte Geräte, um einem Labortechniker zu assistieren, aber kein System, welches das gesamte Probenmanagement automatisiert. In dieser Arbeit wird ein neuer Typ von Serviceroboter präsentiert, der aus einem auf einer mobilen Plattform montierten Roboterarm besteht und diese Lücke schließt. Dieser Roboter muss eine ganze Reihe von Problemen bewältigen: Er muss seine Position im Labor bestimmen können (Lokalisation), er muss eine kollisionsfreie Bahn zu den beteiligten Geräten finden können (Bahnplanung mit Hindernisvermeidung), er darf bei seinen Bewegungen keine Menschen gefährden oder Laborausrüstung beschädigen (Kollisionsvermeidung), er muss die zu bedienenden Geräte erkennen und ihre Position präzise messen können (Bildverarbeitung), er muss sie bedienen können (Armsteuerung), er muss Objekte greifen können (Greifer und Finger) und er muss sie gefügig handhaben können, um sie nicht zu beschädigen (Kraftregelung). Er muss autonom sein, um nur die allernotwendigste Menge an Benutzereingriffen zu benötigen, und doch durch ein Laborsteuerprogramm kontrollierbar sein, um Eingriffe zu erlauben. Schließlich muss er einfach durch ungeschultes Personal zu warten sein. All diese Aspekte werden von dem in dieser Arbeit präsentierten neuen Robotersystem abgedeckt.In biotechnolgical laboratories, the quality of the typically pharmaceutical product is a literally life-important goal. Historically, the quality of the cell cultivations was ensured by on-line measurements of physical process parameters like pH and pO2 only. Biological parameters like cell density and viability were only measured off-line, because the necessary sample management involves highly complicated manipulations and analyses and could therefore not be automated. Various automated devices to assist a laboratory technician do exist, but so far no system to automate the entire sample management. In this work a novel type of service robot consisting of a robot arm mounted on a mobile platform is presented that closes this gap. This robot has to master a multitude of problems: It must be able to locate its position in the laboratory (localisation), it must be able to find a collision-free path to the involved devices (path planning with obstacle avoidance), it must not endanger humans or damage laboratory equipment while moving (collision avoidance), it must be able to recognize the devices to be manipulated and measure their precise position (computer vision), it must be able to manipulate them (arm control), it must be able to grasp objects (gripper and fingers) and it must be able to handle them with compliance in order to not damage them (force control). It must be autonomous in order to only require the least possible amount of user intervention, and yet controllable by a laboratory control program in order to allow intervention. Finally, it must be easily maintainable by non-expert personell. All these aspects are covered by the novel robot system presented in this thesis
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