Academic literature on the topic 'Detection and recognition'
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Journal articles on the topic "Detection and recognition":
Sugiura, Hiroki, Shinichi Demura, Yoshinori Nagasawa, Shunsuke Yamaji, Tamotsu Kitabayashi, Shigeki Matsuda, Takayoshi Yamada, and Ning Xu. "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.
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 (October 1, 2011): 152–53. http://dx.doi.org/10.15373/2249555x/july2013/43.
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 (November 13, 2023): 3778–85. http://dx.doi.org/10.17485/ijst/v16i42.2005.
Shevtekar, Prof Sumit, and Shrinidhi kulkarni. "Traffic-sign Recognition and Detection using Yolo-v8." International Journal of Research Publication and Reviews 5, no. 5 (May 2, 2024): 1619–31. http://dx.doi.org/10.55248/gengpi.5.0524.1141.
Yamini, Maidam. "Number Plate Detection in an Image." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 09 (September 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem25883.
M C, Sohan, Akanksh A M, Anala M R, and Hemavathy R. "Banknote Denomination Recognition on Mobile Devices." ECS Transactions 107, no. 1 (April 24, 2022): 11781–90. http://dx.doi.org/10.1149/10701.11781ecst.
G., Nirmala Priya. "Comparison of Partially Occluded Face Detection and Recognition Methods." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 201–11. http://dx.doi.org/10.5373/jardcs/v12sp7/20202099.
C P, Anju, Andria Joy, Haritha Ashok, Joseph Ronald Pious, and Livya George. "Traffic Sign Detection and Recognition." International Journal of Innovative Science and Research Technology 5, no. 7 (August 10, 2020): 1143–46. http://dx.doi.org/10.38124/ijisrt20jul787.
Katkar, Aniruddha. "EYE DISEASE RECOGNITION SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 28, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32078.
Yu, Myoungseok, Narae Kim, Yunho Jung, and Seongjoo Lee. "A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar." Sensors 20, no. 8 (April 18, 2020): 2321. http://dx.doi.org/10.3390/s20082321.
Dissertations / Theses on the topic "Detection and recognition":
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.
Bashir, Sulaimon A. "Change detection for activity recognition." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3104.
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.
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.
Two approaches have been used to find out how good fingerprint recognition systems are in distinguishing between live fingers and artificial clones. The first approach is a literature study, while the second consists of experiments.
A literature study of liveness detection in fingerprint recognition systems has been performed. A description of different liveness detection methods is presented and discussed. Methods requiring extra hardware use temperature, pulse, blood pressure, electric resistance, etc., and methods using already existent information in the system use skin deformation, pores, perspiration, etc.
The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the CeBIT trade fair in Germany and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems werecircumvented with all subjects'artificial fingerprints, but with varying results. The results are analyzed and discussed, partly with help of the A/R value defined in this report.
Khan, Muhammad. "Hand Gesture Detection & Recognition System." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6496.
Zakir, Usman. "Automatic road sign detection and recognition." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/9733.
Park, Chi-youn 1981. "Consonant landmark detection for speech recognition." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44905.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 191-197).
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 recognition can be performed based on the landmarks. Three types of consonant landmarks are defined according to its characteristics -- glottal vibration, turbulence noise, and sonorant consonant -- so that the appropriate analysis method for each landmark point can be determined. A probabilistic knowledge-based algorithm is developed in three steps. First, landmark candidates are detected and their landmark types are classified based on changes in spectral amplitude. Next, a bigram model describing the physiologically-feasible sequences of consonant landmarks is proposed, so that the most likely landmark sequence among the candidates can be found. Finally, it has been observed that certain landmarks are ambiguous in certain sets of phonetic and prosodic contexts, while they can be reliably detected in other contexts. A method to represent the regions where the landmarks are reliably detected versus where they are ambiguous is presented. On TIMIT test set, 91% of all the consonant landmarks and 95% of obstruent landmarks are located as landmark candidates. The bigram-based process for determining the most likely landmark sequences yields 12% deletion and substitution rates and a 15% insertion rate. An alternative representation that distinguishes reliable and ambiguous regions can detect 92% of the landmarks and 40% of the landmarks are judged to be reliable. The deletion rate within reliable regions is as low as 5%.
(cont.) The resulting landmark sequences form a basis for a knowledge-based speech recognition system since the landmarks imply broad phonetic classes of the speech signal and indicate the points of focus for estimating detailed phonetic information. In addition, because the reliable regions generally correspond to lexical stresses and word boundaries, it is expected that the landmarks can guide the focus of attention not only at the phoneme-level, but at the phrase-level as well.
by Chiyoun Park.
Ph.D.
Ning, Guanghan. "Vehicle license plate detection and recognition." Thesis, University of Missouri - Columbia, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10157318.
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.
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 tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%.
After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably.
The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features.
Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%.
The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulate the process of vehicle retrieval.
Liu, Chang. "Human motion detection and action recognition." HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1108.
Anwer, Rao Muhammad. "Color for Object Detection and Action Recognition." Doctoral thesis, Universitat Autònoma de Barcelona, 2013. http://hdl.handle.net/10803/120224.
Recognizing object categories in real world images is a challenging problem in computer vision. The deformable part based framework is currently the most successful approach for object detection. Generally, HOG are used for image representation within the part-based framework. For action recognition, the bag-of-word framework has shown to provide promising results. Within the bag-of-words framework, local image patches are described by SIFT descriptor. Contrary to object detection and action recognition, combining color and shape has shown to provide the best performance for object and scene recognition. In the first part of this thesis, we analyze the problem of person detection in still images. Standard person detection approaches rely on intensity based features for image representation while ignoring the color. Channel based descriptors is one of the most commonly used approaches in object recognition. This inspires us to evaluate incorporating color information using the channel based fusion approach for the task of person detection. In the second part of the thesis, we investigate the problem of object detection in still images. Due to high dimensionality, channel based fusion increases the computational cost. Moreover, channel based fusion has been found to obtain inferior results for object category where one of the visual varies significantly. On the other hand, late fusion is known to provide improved results for a wide range of object categories. A consequence of late fusion strategy is the need of a pure color descriptor. Therefore, we propose to use Color attributes as an explicit color representation for object detection. Color attributes are compact and computationally efficient. Consequently color attributes are combined with traditional shape features providing excellent results for object detection task. Finally, we focus on the problem of action detection and classification in still images. We investigate the potential of color for action classification and detection in still images. We also evaluate different fusion approaches for combining color and shape information for action recognition. Additionally, an analysis is performed to validate the contribution of color for action recognition. Our results clearly demonstrate that combining color and shape information significantly improve the performance of both action classification and detection in still images.
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.
Title from first page of PDF file (viewed July 16, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (leaves 38-40).
Books on the topic "Detection and recognition":
Cipolla, Roberto, Sebastiano Battiato, and Giovanni Maria Farinella. Computer vision: Detection, recognition and reconstruction. Berlin: Springer, 2010.
Bogusław Cyganek. Object Detection and Recognition in Digital Images. Oxford, UK: John Wiley & Sons Ltd, 2013. http://dx.doi.org/10.1002/9781118618387.
Jiang, Xiaoyue, Abdenour Hadid, Yanwei Pang, Eric Granger, and Xiaoyi Feng, eds. Deep Learning in Object Detection and Recognition. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-10-5152-4.
Miller, Gary J. Drugs and the law: Detection, recognition & investigation. Charlottesville, VA: LexisNexis, 2014.
Miller, Gary J. Drugs and the law: Detection, recognition & investigation. [Altamonte Springs, FL]: Gould Publications, 1992.
Wosnitza, Matthias Werner. High precision 1024-point FFT processor for 2D object detection. Hartung-Gorre: Konstanz, 1999.
Zourob, Mohammed, Souna Elwary, and Anthony Turner, eds. Principles of Bacterial Detection: Biosensors, Recognition Receptors and Microsystems. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-75113-9.
Rajalingam, Mallikka. Text Segmentation and Recognition for Enhanced Image Spam Detection. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53047-1.
Yang, Ming-Hsuan, and Narendra Ahuja. Face Detection and Gesture Recognition for Human-Computer Interaction. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7.
Chen, Datong. Text detection and recognition in images and video sequences. Lausanne: EPFL, 2003.
Book chapters on the topic "Detection and recognition":
Colmenarez, Antonio J., and Thomas S. Huang. "Face Detection and Recognition." In Face Recognition, 174–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_9.
Lu, Tong, Shivakumara Palaiahnakote, Chew Lim Tan, and Wenyin Liu. "Character Segmentation and Recognition." In Video Text Detection, 145–68. London: Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-6515-6_6.
Li, Stan Z., and Jianxin Wu. "Face Detection." In Handbook of Face Recognition, 277–303. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1_11.
Yu, Shiqi, Yuantao Feng, Hanyang Peng, Yan-ran Li, and Jianguo Zhang. "Face Detection." In Handbook of Face Recognition, 103–35. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43567-6_4.
Amit, Yali, Donald Geman, and Bruno Jedynak. "Efficient Focusing and Face Detection." In Face Recognition, 157–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_8.
Shao, Li, Ronghang Zhu, and Qijun Zhao. "Glasses Detection Using Convolutional Neural Networks." In Biometric Recognition, 711–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46654-5_78.
Escalera, Sergio, Xavier Baró, Oriol Pujol, Jordi Vitrià, and Petia Radeva. "Traffic Sign Detection." In Traffic-Sign Recognition Systems, 15–52. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2245-6_3.
Pan, Jiaxing, and Dong Liang. "Holistic Crowd Interaction Modelling for Anomaly Detection." In Biometric Recognition, 642–49. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69923-3_69.
Pei, Yuhang, Liming Xu, and Bochuan Zheng. "Improved YOLOv5 for Dense Wildlife Object Detection." In Biometric Recognition, 569–78. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20233-9_58.
Liu, Yangfan, Yanan Guo, Kangning Du, and Lin Cao. "Enhanced Memory Adversarial Network for Anomaly Detection." In Biometric Recognition, 417–26. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8565-4_39.
Conference papers on the topic "Detection and recognition":
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.
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.
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.
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.
fan, bozhao, jing wang, yuan ma, bida su, teng sun, yue peng, and hong chen. "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.
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.
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.
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.
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.
Fang, Qipeng, Yongmo LV, Tao Tan, Zhanjun Yan, Jianjun Chen, Xiuhui Sun, Chao Hu, and Shaoyun Yin. "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.
Reports on the topic "Detection and recognition":
Mouroulis, P. Visual target detection and recognition. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/5087944.
Grenander, Ulf. Foundations of Object Detection and Recognition,. Fort Belvoir, VA: Defense Technical Information Center, August 1998. http://dx.doi.org/10.21236/ada352287.
Dittmar, George. Object Detection and Recognition in Natural Settings. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.926.
Chun, Cornell S., and Firooz A. Sadjadi. Polarimetric Imaging System for Automatic Target Detection and Recognition. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada395219.
Devaney, A. J., R. Raghavan, H. Lev-Ari, E. Manolakos, and M. Kokar. Automatic Target Detection And Recognition: A Wavelet Based Approach. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada329696.
Hupp, N. A. Detection of Prosodics by Using a Speech Recognition System. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada242432.
Bragdon, Sophia, Vuong Truong, and Jay Clausen. Environmentally informed buried object recognition. Engineer Research and Development Center (U.S.), November 2022. http://dx.doi.org/10.21079/11681/45902.
Sherlock, Barry G. Wavelet Based Feature Extraction for Target Recognition and Minefield Detection. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada401966.
Rangwala, Huzefa, and George Karypis. Building Multiclass Classifiers for Remote Homology Detection and Fold Recognition. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada446086.
Sherlock, Barry G. Wavelet Based Feature Extraction for Target Recognition and Minefield Detection. Fort Belvoir, VA: Defense Technical Information Center, November 1999. http://dx.doi.org/10.21236/ada371103.