Relevant bibliographies by topics / Object Detection and Recognition

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Object Detection and Recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Object Detection and Recognition"

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S., S., Vibu Krishnan S., Mathan Raj Kumar, Ashok .., and M. Janakiraman. "Object Detection Using Deep Learning." Journal of Cognitive Human-Computer Interaction 6, no. 1 (2023): 32–38. http://dx.doi.org/10.54216/jchci.060103.

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Object recognition is an important task in computer vision that involves identifying the objects such as digital images or videos. This research paper provides a comprehensive review of the different techniques and applications of object recognition. The paper first discusses the basic concepts of object recognition, including feature extraction and matching, classification, and detection. Next, the paper reviews the different techniques for object recognition, such as template matching, PCA-based recognition, and deep learning-based recognition. The paper then presents an overview of the diff
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Neha, Gupta, Kapoor Prakhar, and Thakur Sejal. "RECOGNITION AND DETECTION OF OBJECTS IN VIDEO USING OPENCV." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 223–31. https://doi.org/10.5281/zenodo.10935287.

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<em>Object detection is the process of identifying, locating, and detecting different objects. It can be done in a video or in an image. A large number of tools and technologies are used in detecting different objects. Tools like OpenCV, TensorFlow, YOLO[1]&nbsp;, SSD, etc. It displays the number of different objects present in the image or video. We use python as the programming language, as it has a large library that supports all the operations required for object detection. Here we have used coco dataset which consist of 80, distinct objects like person, chair, car, mobile, etc. it is a la
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Karne, Ms Archana, Mr RadhaKrishna Karne, Mr V. Karthik Kumar, and Dr A. Arunkumar. "Convolutional Neural Networks for Object Detection and Recognition." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 32 (February 4, 2023): 1–13. http://dx.doi.org/10.55529/jaimlnn.32.1.13.

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One of the essential technologies in the fields of target extraction, pattern recognition, and motion measurement is moving object detection. Finding moving objects or a number of moving objects across a series of frames is called object tracking. Basically, object tracking is a difficult task. Unexpected changes in the surroundings, an item's mobility, noise, etc., might make it difficult to follow an object. Different tracking methods have been developed to solve these issues. This paper discusses a number of object tracking and detection approaches. The major methods for identifying objects
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Mamta, A. Baitule* Prof. Mukund R. Joshi. "OBJECT DETECTION AND TRACKING ALGORITHM FOR LOW VISION VIDEO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 661–65. https://doi.org/10.5281/zenodo.51857.

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We propose a general framework for Object Recognition into regions and objects. In this framework, the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner .Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible. So we need an automated devise to process these videos. Object tracking is&nbsp; a process of segmenting a region of interest from a
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Rajab Asaad, Renas, Rasan Ismael Ali, Awaz Ahmad Shaban, and Merdin Shamal Salih. "Object Detection using the ImageAI Library in Python." Polaris Global Journal of Scholarly Research and Trends 2, no. 2 (2023): 1–9. http://dx.doi.org/10.58429/pgjsrt.v2n2a143.

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Recent progress in deep learning methods has shown that key steps in object detection and recognition, including feature extraction, region proposals, and classification, can be done using ImageAi libraries. Object detection is a computer vision technique that works to identify and locate objects within an image or video. Specifically, object detection draws bounding boxes around these detected objects, which allow us to locate where said objects are in a given scene. Object detection is commonly confused with image recognition, so before we proceed, it’s important that we clarify the distinct
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JAMMULA, SRINATH. "An Efficient Object Detection System for the Blind People." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29406.

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As object recognition technology has developed recently, various technologies have been applied to autonomous vehicles, robots, and industrial facilities. However, the benefits of these technologies are not reaching the visually impaired, who need it the most. In this research, researchers proposed a deep learning based on object identification system for the visually impaired. Voice recognition technology is used to know what objects a blind person wants, and then to find the objects via object recognition. Furthermore, a voice guidance technique is used to inform sight impaired persons as to
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Salunkhe, Akilesh, Manthan Raut, Shayantan Santra, and Sumedha Bhagwat. "Android-based object recognition application for visually impaired." ITM Web of Conferences 40 (2021): 03001. http://dx.doi.org/10.1051/itmconf/20214003001.

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Detecting objects in real-time and converting them into an audio output was a challenging task. Recent advancement in computer vision has allowed the development of various real-time object detection applications. This paper describes a simple android app that would help the visually impaired people in understanding their surroundings. The information about the surrounding environment was captured through a phone’s camera where real-time object recognition through tensorflow’s object detection API was done. The detected objects were then converted into an audio output by using android’s text-t
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Kalshetti, Mallinath. "Object Detection and Recognition Using Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30262.

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Object detection and recognition are critical problems in computer vision, with numerous applications in areas such as surveillance, autonomous systems, and medical imaging. This study provides a comprehensive overview of object detection and recognition utilizing image processing methods. Object detection is the process of finding and locating objects inside picture or video frames. Traditional approaches were based on handcrafted features and classifiers, but recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have changed the discipline. Architectures su
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Zhang, Fan, Jiaxing Luan, Zhichao Xu, and Wei Chen. "DetReco: Object-Text Detection and Recognition Based on Deep Neural Network." Mathematical Problems in Engineering 2020 (July 14, 2020): 1–15. http://dx.doi.org/10.1155/2020/2365076.

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Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection t
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Juyal, Amit. "A Deep Learning-Based Approach for Real-Time Object Detection and Recognition." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1304–14. http://dx.doi.org/10.17762/msea.v70i2.2322.

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Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, makin
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Dissertations / Theses on the topic "Object Detection and Recognition"

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Anwer, Rao Muhammad. "Color for Object Detection and Action Recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/120224.

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Detectar objetos en imágenes es un problema central en el campo de la visión por computador. El marco de detección basado en modelos de partes deformable es actualmente el más eficaz. Generalmente, HOG es el descriptor de imágenes a partir del cual se construyen esos modelos. El reconocimiento de acciones humanas es otro de los tópicos de más interés actualmente en el campo de la visión por computador. En este caso, los modelos usados siguen la idea de conjuntos de palabras (visuales), en inglés bag-of-words, en este caso siendo SIFT uno de los descriptor de imágenes más usados para dar soport
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Mahmood, Hamid. "Visual Attention-based Object Detection and Recognition." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-94024.

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This thesis is all about the visual attention, starting from understanding the human visual system up till applying this mechanism to a real-world computer vision application. This has been achieved by taking the advantage of latest findings about the human visual attention and the increased performance of the computers. These two facts played a vital role in simulating the many different aspects of this visual behavior. In addition, the concept of bio-inspired visual attention systems have become applicable due to the emergence of different interdisciplinary approaches to vision which leads t
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Aleixo, Patrícia Nunes. "Object detection and recognition for robotic applications." Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13811.

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Mestrado em Engenharia Eletrónica e Telecomunicações<br>The computer vision assumes an important relevance in the development of robotic applications. In several applications, robots need to use vision to detect objects, a challenging and sometimes difficult task. This thesis is focused on the study and development of algorithms to be used in detection and identification of objects on digital images to be applied on robots that will be used in practice cases. Three problems are addressed: Detection and identification of decorative stones for textile industry; Detection of the ball in ro
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Dittmar, George William. "Object Detection and Recognition in Natural Settings." PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/926.

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Much research as of late has focused on biologically inspired vision models that are based on our understanding of how the visual cortex processes information. One prominent example of such a system is HMAX [17]. HMAX attempts to simulate the biological process for object recognition in cortex based on the model proposed by Hubel & Wiesel [10]. This thesis investigates the ability of an HMAX-like system (GLIMPSE [20]) to perform object-detection in cluttered natural scenes. I evaluate these results using the StreetScenes database from MIT [1, 8]. This thesis addresses three questions: (1) Can
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Ridge, Douglas John. "Imaging for small object detection." Thesis, Queen's University Belfast, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295423.

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Higgs, David Robert. "Parts-based object detection using multiple views /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1000.

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IACONO, MASSIMILIANO. "Object detection and recognition with event driven cameras." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1005981.

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This thesis presents study, analysis and implementation of algorithms to perform object detection and recognition using an event-based cam era. This sensor represents a novel paradigm which opens a wide range of possibilities for future developments of computer vision. In partic ular it allows to produce a fast, compressed, illumination invariant output, which can be exploited for robotic tasks, where fast dynamics and significant illumination changes are frequent. The experiments are carried out on the neuromorphic version of the iCub humanoid platform. The robot is equipped with a nov
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Simonelli, Andrea. "3D Object Detection from Images." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/353602.

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Remarkable advancements in the field of Computer Vision, Artificial Intelligence and Machine Learning have led to unprecedented breakthroughs in what machines are able to achieve. In many tasks such as in Image Classification in fact, they are now capable of even surpassing human performance. While this is truly outstanding, there are still many tasks in which machines lag far behind. Walking in a room, driving on an highway, grabbing some food for example. These are all actions that feel natural to us but can be quite unfeasible for them. Such actions require to identify and localize objec
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Yoon, Taehun. "Object Recognition Based on Multi-agent Spatial Reasoning." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1206075792.

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Bodén, Rikard, and Jonathan Pernow. "SORTED : Serial manipulator with Object Recognition Trough Edge Detection." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264513.

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Today, there is an increasing demand for smart robots that can make decisions on their own and cooperate with humans in changing environments. The application areas for robotic arms with camera vision are likely to increase in the future of artificial intelligence as algorithms become more adaptable and intelligent than ever. The purpose of this bachelor’s thesis is to develop a robotic arm that recognises arbitrarily placed objects with camera vision and has the ability to pick and place the objects when they appear in unpredictable positions. The robotic arm has three degrees of freedom and
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Books on the topic "Object Detection and Recognition"

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Bogusław Cyganek. Object Detection and Recognition in Digital Images. John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118618387.

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Jiang, Xiaoyue, Abdenour Hadid, Yanwei Pang, Eric Granger, and Xiaoyi Feng, eds. Deep Learning in Object Detection and Recognition. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4.

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Amit, Yali. 2D object detection and recognition: Models, algorithms, and networks. MIT Press, 2002.

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Wosnitza, Matthias Werner. High precision 1024-point FFT processor for 2D object detection. Konstanz, 1999.

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Mir, Roohie Naaz, Vipul Kumar Sharma, Ranjeet Kumar Rout, and Saiyed Umer. Advancement of Deep Learning and its Applications in Object Detection and Recognition. River Publishers, 2023. http://dx.doi.org/10.1201/9781003393658.

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Bennamoun, M., and G. J. Mamic. Object Recognition. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1.

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Grauman, Kristen, and Bastian Leibe. Visual Object Recognition. Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3.

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Strat, Thomas M. Natural Object Recognition. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2932-2.

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Bastian, Leibe, ed. Visual object recognition. Morgan & Claypool, 2011.

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Dawson, K. M. Object recognition techniques. Trinity College, Department of Computer Science, 1991.

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Book chapters on the topic "Object Detection and Recognition"

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Grauman, Kristen, and Bastian Leibe. "Generic Object Detection: Finding and Scoring Candidates." In Visual Object Recognition. Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-01553-3_9.

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Gollapudi, Sunila. "Object Detection and Recognition." In Learn Computer Vision Using OpenCV. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-4261-2_5.

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Košecká, Jana. "Object Detection and Recognition." In Encyclopedia of Robotics. Springer Berlin Heidelberg, 2021. http://dx.doi.org/10.1007/978-3-642-41610-1_99-1.

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Pei, Yuhang, Liming Xu, and Bochuan Zheng. "Improved YOLOv5 for Dense Wildlife Object Detection." In Biometric Recognition. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_58.

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Xia, Jingran, Guowen Kuang, Xu Wang, Zhibin Chen, and Jinfeng Yang. "ORION: Orientation-Sensitive Object Detection." In Pattern Recognition and Computer Vision. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18916-6_47.

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Zhou, Xian, You-Ji Feng, and Xi Zhou. "Real-Time Object Detection Using Efficient Convolutional Networks." In Biometric Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_68.

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Treiber, Marco. "Interest Point Detection and Region Descriptors." In An Introduction to Object Recognition. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-235-3_7.

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Heisele, Bernd, Ivaylo Riskov, and Christian Morgenstern. "Components for Object Detection and Identification." In Toward Category-Level Object Recognition. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_12.

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Torralba, Antonio, Kevin P. Murphy, and William T. Freeman. "Shared Features for Multiclass Object Detection." In Toward Category-Level Object Recognition. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_18.

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Hashimoto, Marcelo, and Roberto M. Cesar. "Object Detection by Keygraph Classification." In Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02124-4_23.

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Conference papers on the topic "Object Detection and Recognition"

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Pannoy, Nakul, Sarayut Nonsiri, and Supawee Makdee. "Object Detection for Retail Product Recognition." In 2024 9th International Conference on Business and Industrial Research (ICBIR). IEEE, 2024. https://doi.org/10.1109/icbir61386.2024.10875711.

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zhou, jie. "Deep learning in food category recognition." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050535.

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Hill, Paul, Alin Achim, David Bull, and Nantheera Anantrasirichai. "Automatic object detection in atmospheric turbulence-affected environments." In Automatic Target Recognition XXXV, edited by Kenny Chen, Timothy L. Overman, and Riad I. Hammoud. SPIE, 2025. https://doi.org/10.1117/12.3053981.

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LIU, Haitao, Lifen Wang, Zeng Gao, Xiuqian Li, Yanxia Yang, and Jun Li. "Lightweight satellite recognition algorithm based on improved YOLOv5." In 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), edited by Bin Liu and Lu Leng. SPIE, 2024. http://dx.doi.org/10.1117/12.3050441.

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Lee, Hojun, Suyoung Kim, Junhoo Lee, Jaeyoung Yoo, and Nojun Kwak. "Coreset Selection for Object Detection." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00764.

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Smogavec, Primož, Božidar Potočnik, and Dušan Gleich. "Subsurface Object Recognition using YOLOv8." In International Conference IcETRAN. ETRAN Society, Academic Mind, Belgrade, 2024. https://doi.org/10.69994/11ic24052.

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This paper presents the implementation and evaluation of the YOLOv8 detection method within the domain of ground-penetrating radar (GPR) imagery analysis. YOLOv8, an evolution of previous YOLO models, is employed for object detection tasks, providing efficient and accurate results. The study focuses on detecting and classifying three distinct objects: corner reflector, anti-tank (AT) mine, and glass bottle. Training data preparation involves annotating radar images and splitting them into training, validation and testing sets. The YOLOv8 model is trained on labeled data, and its performance is
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Sun, Qiyang, Xia Wang, Ronghua Su, and Yuqing Deng. "Frequency-aware natural camouflage object segmentation." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3016580.

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Ayman, Shehab Eldeen, Walid Hussein, and Omar H. Karam. "Depth-Based Region Proposal: Multi-Stage Real-Time Object Detection." In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131305.

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Many real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in fulldimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a two-dimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these tec
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Pardo, Alejandro, Mengmeng Xu, Ali Thabet, Pablo Arbelaez, and Bernard Ghanem. "BAOD: Budget-Aware Object Detection." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202106254.

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We study the problem of object detection from a novel perspective in which annotation budget constraints are taken into consideration, appropriately coined Budget Aware Object Detection (BAOD). When provided with a fixed budget, we propose a strategy for building a diverse and informative dataset that can be used to optimally train a robust detector. We investigate both optimization and learning-based methods to sample which images to annotate and what type of annotation (strongly or weakly supervised) to annotate them with. We adopt a hybrid supervised learning framework to train the object d
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He, Peng, Weidong Chen, Lan Pang, et al. "The survey of one-stage anchor-free real-time object detection algorithms." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3012931.

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Reports on the topic "Object Detection and Recognition"

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Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45902.

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The ability to detect and classify buried objects using thermal infrared imaging is affected by the environmental conditions at the time of imaging, which leads to an inconsistent probability of detection. For example, periods of dense overcast or recent precipitation events result in the suppression of the soil temperature difference between the buried object and soil, thus preventing detection. This work introduces an environmentally informed framework to reduce the false alarm rate in the classification of regions of interest (ROIs) in thermal IR images containing buried objects. Using a da
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Grenander, Ulf. Foundations of Object Detection and Recognition,. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada352287.

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Dittmar, George. Object Detection and Recognition in Natural Settings. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.926.

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Zhao, Ruyin. CSI-based Gesture Recognition and Object Detection. Iowa State University, 2021. http://dx.doi.org/10.31274/cc-20240624-456.

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Clausen, Jay, Vuong Truong, Sophia Bragdon, et al. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, th
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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Bishop, Megan, Vuong Truong, Sophia Bragdon, and Jay Clausen. Comparing the thermal infrared signatures of shallow buried objects and disturbed soil. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49415.

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The alteration of physical and thermal properties of native soil during object burial produces a signature that can be detected using thermal infrared (IR) imagery. This study explores the thermal signature of disturbed soil compared to buried objects of different compositions (e.g., metal and plastic) buried 5 cm below ground surface (bgs) to better understand the mechanisms by which soil disturbance can impact the performance of aided target detection and recognition (AiTD/R). IR imagery recorded every five minutes were coupled with meteorological data recorded on 15-minute intervals from 1
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Yan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, 2021. http://dx.doi.org/10.17760/d20410114.

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Abstract:
Recent advances in visual sensing technology have gained much attention in the field of bridge inspection and management. Coupled with advanced robotic systems, state-of-the-art visual sensors can be used to obtain accurate documentation of bridges without the need for any special equipment or traffic closure. The captured visual sensor data can be post-processed to gather meaningful information for the bridge structures and hence to support bridge inspection and management. However, state-of-the-practice data postprocessing approaches require substantial manual operations, which can be time-c
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9

Wells, III, and William M. Statistical Object Recognition. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada270887.

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10

Weiss, Isaac. Geometric Invariants and Object Recognition. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada255317.

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