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Relevant bibliographies by topics / Detection and recognition

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Academic literature on the topic 'Detection and recognition'

Author: Grafiati

Published: 15 June 2024

Last updated: 31 July 2025

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Journal articles on the topic "Detection and recognition"

1

Yekeen, S.A, and T.S Ibiyemi. "EDGE DETECTION ALGORITHM FOR YORUBA CHARACTER RECOGNITION." Advances in Vision Computing: An International Journal (AVC) 5, no. 1/2/3/4 (2018): 01–09. https://doi.org/10.5281/zenodo.3526822.

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Digital image processing for pattern recognition involves several processing and pre-processing steps. Edge detection stands a great position for accurate pattern recognition most especially in character recognition system. Many edge detection techniques were implemented with convolution mask and based on approximations to differential operators. For efficient Yoruba character recognition, compass edge detection algorithm was developed to enhance the recognition rate of Yoruba character. The algorithm developed achieved 0.923 edge detection error rate. The level of accuracy will have been bett

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Sugiura, Hiroki, Shinichi Demura, Yoshinori Nagasawa, et al. "Relationship between Extent of Coffee Intake and Recognition of Its Effects and Ingredients." Detection 01, no. 01 (2013): 1–6. http://dx.doi.org/10.4236/detection.2013.11001.

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Shah, Dr Dipti M., and Parul D. Sindha. "Color detection in real time traffic sign detection and recognition system." Indian Journal of Applied Research 3, no. 7 (2011): 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.

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Chandiramani, Gunjan, and M. R Dhotre. "Vision Based Road Sign Detection and Recognition." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 43–47. https://doi.org/10.70729/ijser15247.

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Tao, Hongjing, Lei Zhang, Zhipeng Sun, Xinchao Cui, and Weixun Yi. "Multi-Strategy Improvement of Coal Gangue Recognition Method of YOLOv11." Sensors 25, no. 7 (2025): 1983. https://doi.org/10.3390/s25071983.

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The current methods for detecting coal gangue face several challenges, including low detection accuracy, a high probability of missed detections, and inadequate real-time performance. These issues stem from the complexities associated with diverse industrial environments and mining conditions, such as the mixing of coal gangue and insufficient illumination within coal mines. A detection model, referred to as EBD-YOLO, is proposed based on YOLOv11n. First, the C3k2-EMA module is integrated with the EMA attention mechanism within the C3k2 module of the backbone network, thereby enhancing the mod

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Hu, Ting. "Anomalous pattern recognition based on image recognition in food safety detection." Journal of Combinatorial Mathematics and Combinatorial Computing 125 (March 30, 2025): 3–20. https://doi.org/10.61091/jcmcc125-01.

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In food processing, foreign matter inevitably contaminates packaged food. To ensure food safety, ray-based detection is used; however, the original images suffer from aberrations and noise that degrade quality and hinder further processing. Thus, images are preprocessed to enhance quality by highlighting key features and suppressing irrelevant ones before abnormal pattern recognition. Following image segmentation, a BP neural network algorithm is applied for foreign object detection. In tests with contaminants such as metal wires, stones, and glass, the algorithm identified distinct abnormal f

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Srilatha, J., T. S. Subashini, and K. Vaidehi. "Solid Waste Detection and Recognition using Faster RCNN." Indian Journal Of Science And Technology 16, no. 42 (2023): 3778–85. http://dx.doi.org/10.17485/ijst/v16i42.2005.

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Shevtekar, Prof Sumit, and Shrinidhi kulkarni. "Traffic-sign Recognition and Detection using Yolo-v8." International Journal of Research Publication and Reviews 5, no. 5 (2024): 1619–31. http://dx.doi.org/10.55248/gengpi.5.0524.1141.

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Yamini, Maidam. "Number Plate Detection in an Image." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25883.

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Automatic Vehicle license plate detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for license plate detection and recognitions. Due to the varying characteristics of the license plate like numbering system, colors, style and sizes of license plate, When detection and recognition are two separate jobs, which also results in a huge number of factors, there is an issue with identification. So,further research is still needed in this ar

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M C, Sohan, Akanksh A M, Anala M R, and Hemavathy R. "Banknote Denomination Recognition on Mobile Devices." ECS Transactions 107, no. 1 (2022): 11781–90. http://dx.doi.org/10.1149/10701.11781ecst.

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Several mobile applications have been developed to facilitate denomination detection for blind users. However, none of the existing applications allow for detecting multiple notes in a single frame and relaying the total denomination, nor is there a dataset available for the new Indian currency notes, annotated for object detection training. We describe the development of a detection application that aims to improve on the previously existing solutions by enabling multi-note detection, continuous audio feedback, automatic torch usage, and minimal user-application interaction. YOLOv4 allowed th

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Dissertations / Theses on the topic "Detection and recognition"

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O'Shea, Kieran. "Roadsign detection & recognition /." Leeds : University of Leeds, School of Computer Studies, 2008. http://www.comp.leeds.ac.uk/fyproj/reports/0708/OShea.pdf.

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Bashir, Sulaimon A. "Change detection for activity recognition." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3104.

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Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user haracteristics. However, the existing approaches to model adaptation in activity recognition are blin

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Sandström, Marie. "Liveness Detection in Fingerprint Recognition Systems." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2397.

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<p>Biometrics deals with identifying individuals with help of their biological data. Fingerprint scanning is the most common method of the biometric methods available today. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint recognition systems are evolving and this study will discuss the situation of today. </p><p>Two approaches have been used to find out how good fingerprint recognition systems are in distinguishing between live

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Khan, Muhammad. "Hand Gesture Detection & Recognition System." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6496.

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The project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it.A research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algori

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Zakir, Usman. "Automatic road sign detection and recognition." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/9733.

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Road Sign Detection and Recognition (RSDR) systems provide an additional level of driver assistance, leading to improved safety for passengers, road users and vehicles. As part of Advanced Driving Assistance Systems (ADAS), RSDR can be used to benefit drivers (specially with driving disabilities) by alerting them about the presence of road signs to reduce risks in situations of driving distraction, fatigue ,poor sight and weather conditions. Although a number of RSDR systems have been proposed in literature; the design of a robust algorithm still remains an open research problem. This thesis a

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Park, Chi-youn 1981. "Consonant landmark detection for speech recognition." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44905.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 191-197).<br>This thesis focuses on the detection of abrupt acoustic discontinuities in the speech signal, which constitute landmarks for consonant sounds. Because a large amount of phonetic information is concentrated near acoustic discontinuities, more focused speech analysis and recognit

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Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.

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<p> In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. </p><p> Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We teste

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Liu, Chang. "Human motion detection and action recognition." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1108.

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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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Wang, Ge. "Verilogo proactive phishing detection via logo recognition /." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/fullcit?p1477945.

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Thesis (M.S.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed July 16, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (leaves 38-40).

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Books on the topic "Detection and recognition"

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Cipolla, Roberto, Sebastiano Battiato, and Giovanni Maria Farinella. Computer vision: Detection, recognition and reconstruction. Springer, 2010.

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Peng, Kuan-Chuan, Yizhou Wang, Ziyue Li, et al., eds. Human Activity Recognition and Anomaly Detection. Springer Nature Singapore, 2025. http://dx.doi.org/10.1007/978-981-97-9003-6.

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

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6

Zourob, Mohammed, Souna Elwary, and Anthony Turner, eds. Principles of Bacterial Detection: Biosensors, Recognition Receptors and Microsystems. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75113-9.

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Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53047-1.

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Yang, Ming-Hsuan, and Narendra Ahuja. Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7.

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Mohammed, Zourob, Elwary Souna, and Turner Anthony, eds. Principles of bacterial detection: Biosensors, recognition receptors, and microsystems. Springer, 2008.

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10

Chen, Datong. Text detection and recognition in images and video sequences. EPFL, 2003.

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Book chapters on the topic "Detection and recognition"

1

Colmenarez, Antonio J., and Thomas S. Huang. "Face Detection and Recognition." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_9.

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Lu, Tong, Shivakumara Palaiahnakote, Chew Lim Tan, and Wenyin Liu. "Character Segmentation and Recognition." In Video Text Detection. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6515-6_6.

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Yu, Shiqi, Yuantao Feng, Hanyang Peng, Yan-ran Li, and Jianguo Zhang. "Face Detection." In Handbook of Face Recognition. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43567-6_4.

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Li, Stan Z., and Jianxin Wu. "Face Detection." In Handbook of Face Recognition. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1_11.

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Amit, Yali, Donald Geman, and Bruno Jedynak. "Efficient Focusing and Face Detection." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_8.

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Shao, Li, Ronghang Zhu, and Qijun Zhao. "Glasses Detection Using Convolutional Neural Networks." In Biometric Recognition. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_78.

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Escalera, Sergio, Xavier Baró, Oriol Pujol, Jordi Vitrià, and Petia Radeva. "Traffic Sign Detection." In Traffic-Sign Recognition Systems. Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2245-6_3.

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Pan, Jiaxing, and Dong Liang. "Holistic Crowd Interaction Modelling for Anomaly Detection." In Biometric Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_69.

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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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Liu, Yangfan, Yanan Guo, Kangning Du, and Lin Cao. "Enhanced Memory Adversarial Network for Anomaly Detection." In Biometric Recognition. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8565-4_39.

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Conference papers on the topic "Detection and recognition"

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Pacaldo, Joren Mundane, Chi Wee Tan, Wah Pheng Lee, Dustin Gerard Ancog, and Haroun Al Raschid Christopher Macalisang. "Utilizing Synthetically-Generated License Plate Automatic Detection and Recognition of Motor Vehicle Plates in Philippines." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1022.

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We investigated the potential use of synthetic data for automatic license plate detection and recognition by detecting and clustering each of the characters on the license plates. We used 36 cascading classifiers (26 letters + 10 numbers) as an individual character to detect synthetically generated license plates. We trained our cascade classifier using a Local Binary Pattern (LBP) as the visual descriptor. After detecting all the characters individually, an investigation has been established in identifying and utilizing a clustering algorithm in grouping these characters for valid license pla

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Wu, Liyang, and Xiaofang Zhang. "An underwater polarimetric image descattering and material identification method based on unpaired multi-scale polarization fusion adversarial generative network." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3018076.

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Wang, Qixiang, Yannan Yang, and Wende Dong. "Image dehazing based on Uformer modified WGAN." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3016206.

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Ma, Ning, Yunan Wu, Wancheng Liu, Yining Yang, Jinjin Wang, and Xin Liu. "A fusion adaptive recognition network based on intensity and polarization imaging." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3025945.

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fan, bozhao, jing wang, yuan ma, et al. "Research on feature extraction method of space targets image based on Hu extension moment." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3013302.

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Xu, Yuan, Feng Li, Kaimin Shi, and Peikun Li. "Underwater image enhancement based on unsupervised adaptive uncertainty distribution." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3014202.

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Yu, Long, Xiangchun Shi, Jia Yu, Huiping Liu, Bin Guo, and Yao Fu. "Research on fringe projection profilometry for 3D reconstruction of target in turbid water." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3018075.

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Yao, XinYu, fengtao He, and binghui Wang. "Deep learning-based recurrent neural network for underwater image enhancement." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3018273.

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Guo, Ju Guang, Da yong Wang, Yun Xin Wang, Guang ping Wang, Wei Wei Jiang, and Zhi hui Yang. "Experimental study on anti-interference based on infrared radiation characteristics of jamming target." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3015538.

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Fang, Qipeng, Yongmo LV, Tao Tan, et al. "Diffraction efficiency control of liquid crystal polymer polarizing grating film layer through grating layer thinning." In Imaging Detection and Target Recognition, edited by Jiangtao Xu and Chao Zuo. SPIE, 2024. http://dx.doi.org/10.1117/12.3023676.

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Reports on the topic "Detection and recognition"

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Mouroulis, P. Visual target detection and recognition. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/5087944.

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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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Chun, Cornell S., and Firooz A. Sadjadi. Polarimetric Imaging System for Automatic Target Detection and Recognition. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada395219.

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Devaney, A. J., R. Raghavan, H. Lev-Ari, E. Manolakos, and M. Kokar. Automatic Target Detection And Recognition: A Wavelet Based Approach. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada329696.

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Hupp, N. A. Detection of Prosodics by Using a Speech Recognition System. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242432.

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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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Sherlock, Barry G. Wavelet Based Feature Extraction for Target Recognition and Minefield Detection. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada401966.

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Rangwala, Huzefa, and George Karypis. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada446086.

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