Tesi sul tema "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.
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).
Koniaris, Christos. "Perceptually motivated speech recognition and mispronunciation detection". Doctoral thesis, KTH, Tal-kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102321.
QC 20120914
European Union FP6-034362 research project ACORNS
Computer-Animated language Teachers (CALATea)
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.
Zhou, Yun. "Embedded Face Detection and Facial Expression Recognition". Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/583.
Norris, Jeffrey S. (Jeffrey Singley) 1976. "Face detection and recognition in office environments". Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80108.
Aleixo, Patrícia Nunes. "Object detection and recognition for robotic applications". Master's thesis, Universidade de Aveiro, 2014. http://hdl.handle.net/10773/13811.
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 robotic soccer; Detection of objects in a service robot, that operates in a domestic environment. In each case, different methods are studied and applied, such as, Template Matching, Hough transform and visual descriptors (like SIFT and SURF). It was chosen the OpenCv library in order to use the data structures to image manipulation, as well as other structures for all information generated by the developed vision systems. Whenever possible, it was used the implementation of the described methods and have been developed new approaches, both in terms of pre-processing algorithms and in terms of modification of the source code in some used functions. Regarding the pre-processing algorithms, were used the Canny edge detector, contours detection, extraction of color information, among others. For the three problems, there are presented and discussed experimental results in order to evaluate the best method to apply in each case. The best method for each application is already integrated or in the process of integration in the described robots.
A visão por computador assume uma importante relevância no desenvolvimento de aplicações robóticas, na medida em que há robôs que precisam de usar a visão para detetar objetos, uma tarefa desafiadora e por vezes difícil. Esta dissertação foca-se no estudo e desenvolvimento de algoritmos para a deteção e identificação de objetos em imagem digital para aplicar em robôs que serão usados em casos práticos. São abordados três problemas: Deteção e identificação de pedras decorativas para a indústria têxtil; Deteção da bola em futebol robótico; Deteção de objetos num robô de serviço, que opera em ambiente doméstico. Para cada caso, diferentes métodos são estudados e aplicados, tais como, Template Matching, transformada de Hough e descritores visuais (como SIFT e SURF). Optou-se pela biblioteca OpenCv com vista a utilizar as suas estruturas de dados para manipulação de imagem, bem como as demais estruturas para toda a informação gerada pelos sistemas de visão desenvolvidos. Sempre que possivel utilizaram-se as implementações dos métodos descritos tendo sido desenvolvidas novas abordagens, quer em termos de algoritmos de preprocessamento quer em termos de alteração do código fonte das funções utilizadas. Como algoritmos de pre-processamento foram utilizados o detetor de arestas Canny, deteção de contornos, extração de informação de cor, entre outros. Para os três problemas, são apresentados e discutidos resultados experimentais, de forma a avaliar o melhor método a aplicar em cada caso. O melhor método em cada aplicação encontra-se já integrado ou em fase de integração dos robôs descritos.
Cohen, Gregory Kevin. "Event-Based Feature Detection, Recognition and Classification". Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066204/document.
One of the fundamental tasks underlying much of computer vision is the detection, tracking and recognition of visual features. It is an inherently difficult and challenging problem, and despite the advances in computational power, pixel resolution, and frame rates, even the state-of-the-art methods fall far short of the robustness, reliability and energy consumption of biological vision systems. Silicon retinas, such as the Dynamic Vision Sensor (DVS) and Asynchronous Time-based Imaging Sensor (ATIS), attempt to replicate some of the benefits of biological retinas and provide a vastly different paradigm in which to sense and process the visual world. Tasks such as tracking and object recognition still require the identification and matching of local visual features, but the detection, extraction and recognition of features requires a fundamentally different approach, and the methods that are commonly applied to conventional imaging are not directly applicable. This thesis explores methods to detect features in the spatio-temporal information from event-based vision sensors. The nature of features in such data is explored, and methods to determine and detect features are demonstrated. A framework for detecting, tracking, recognising and classifying features is developed and validated using real-world data and event-based variations of existing computer vision datasets and benchmarks. The results presented in this thesis demonstrate the potential and efficacy of event-based systems. This work provides an in-depth analysis of different event-based methods for object recognition and classification and introduces two feature-based methods. Two learning systems, one event-based and the other iterative, were used to explore the nature and classification ability of these methods. The results demonstrate the viability of event-based classification and the importance and role of motion in event-based feature detection
Dittmar, George William. "Object Detection and Recognition in Natural Settings". PDXScholar, 2013. https://pdxscholar.library.pdx.edu/open_access_etds/926.
Olsson, Oskar, e Moa Eriksson. "Automated system tests with image recognition : focused on text detection and recognition". Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160249.
Kou, Yufeng. "Abnormal Pattern Recognition in Spatial Data". Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/30145.
Ph. D.
Pavani, Sri-Kaushik. "Methods for face detection and adaptive face recognition". Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7567.
L'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.
Beyreuther, Moritz. "Speech Recognition based Automatic Earthquake Detection and Classification". Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-132557.
Khiari, El Hebri. "Text Detection and Recognition in the Automotive Context". Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32458.
Hayes, William S. "Pattern recognition and signal detection in gene finding". Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.
Liu, Sharlene Anne. "Landmark detection for distinctive feature-based speech recognition". Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11406.
Includes bibliographical references (leaves 187-190).
by Sharlene Anne Liu.
Ph.D.
Patel, Ravi L. "Security system with motion detection and face recognition". Thesis, California State University, Long Beach, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10251645.
Security is an essential criterion in all industries. This project develops a Security System that includes motion detection and face recognition. Motion detection is achieved by using the PIR (Passive Infrared) sensor, and face recognition is achieved by using the SIFT (Scale Invariant Feature Transform) algorithm.
The primary hardware components used in this system are a PIR sensor, microcontroller, relay, LCD (Liquid Crystal Display), buzzer, MAX232 IC, and GSM (Global System for Mobile Communication). The system incorporates the feature extraction method, which is utilized to identify the number of objects in an image, and the proposed SIFT algorithm is used for the face recognition. These two methods, the feature extraction method and SIFT algorithm, are implemented in MATLAB. The result shows that the efficiency and the recognition time of the proposed SIFT algorithm is better than its predecessors. This system can be used for industrial, hospital, or even residential purposes.
Halberstadt, Warren. "Pattern recognition in the detection of Tuberculous Meningitis". Master's thesis, University of Cape Town, 2005. http://hdl.handle.net/11427/3239.
Robertson, Curtis E. "Deep Learning-Based Speed Sign Detection and Recognition". University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1595500028808679.
Qiao, Long. "Structural damage detection using signal-based pattern recognition". Diss., Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1385.
Yousfi, Sonia. "Embedded Arabic text detection and recognition in videos". Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI069/document.
This thesis focuses on Arabic embedded text detection and recognition in videos. Different approaches robust to Arabic text variability (fonts, scales, sizes, etc.) as well as to environmental and acquisition condition challenges (contrasts, degradation, complex background, etc.) are proposed. We introduce different machine learning-based solutions for robust text detection without relying on any pre-processing. The first method is based on Convolutional Neural Networks (ConvNet) while the others use a specific boosting cascade to select relevant hand-crafted text features. For the text recognition, our methodology is segmentation-free. Text images are transformed into sequences of features using a multi-scale scanning scheme. Standing out from the dominant methodology of hand-crafted features, we propose to learn relevant text representations from data using different deep learning methods, namely Deep Auto-Encoders, ConvNets and unsupervised learning models. Each one leads to a specific OCR (Optical Character Recognition) solution. Sequence labeling is performed without any prior segmentation using a recurrent connectionist learning model. Proposed solutions are compared to other methods based on non-connectionist and hand-crafted features. In addition, we propose to enhance the recognition results using Recurrent Neural Network-based language models that are able to capture long-range linguistic dependencies. Both OCR and language model probabilities are incorporated in a joint decoding scheme where additional hyper-parameters are introduced to boost recognition results and reduce the response time. Given the lack of public multimedia Arabic datasets, we propose novel annotated datasets issued from Arabic videos. The OCR dataset, called ALIF, is publicly available for research purposes. As the best of our knowledge, it is first public dataset dedicated for Arabic video OCR. Our proposed solutions were extensively evaluated. Obtained results highlight the genericity and the efficiency of our approaches, reaching a word recognition rate of 88.63% on the ALIF dataset and outperforming well-known commercial OCR engine by more than 36%
IACONO, MASSIMILIANO. "Object detection and recognition with event driven cameras". Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1005981.
Higgs, David Robert. "Parts-based object detection using multiple views /". Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1000.
Ma, Chengyuan. "A detection-based pattern recognition framework and its applications". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33889.
Kelsey, Matthew Douglas. "THE DEVELOPMENT AND EVALUATION OF TECHNIQUES FOR USE IN MAMMOGRAPHIC SCREENING COMPUTER AIDED DETECTION SYSTEMS". OpenSIUC, 2011. https://opensiuc.lib.siu.edu/dissertations/331.
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.
Espinosa-Romero, Arturo. "Situated face detection". Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/6667.
Al, Qader Akram Abed Al Karim Abed. "Unconstrained road sign recognition". Thesis, De Montfort University, 2017. http://hdl.handle.net/2086/14942.
Chen, Datong. "Text detection and recognition in images and video sequences /". [S.l.] : [s.n.], 2003. http://library.epfl.ch/theses/?display=detail&nr=2863.
Bouganis, Christos-Savvas. "Multiple light source detection with application to face recognition". Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/11322.
Feng, Jingwen. "Traffic Sign Detection and Recognition System for Intelligent Vehicles". Thesis, Université d'Ottawa / University of Ottawa, 2014. http://hdl.handle.net/10393/31449.
Cheung, Karen. "Image processing for skin cancer detection, malignant melanoma recognition". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq29403.pdf.
GUPTA, MUSKAN. "FACIAL DETECTION and RECOGNITION". Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19521.
Jung-Chieh, Hsien. "Road Sign Detection and Recognition". 2003. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0009-0112200611353624.
Wang, Tsung-Jen, e 王宗任. "Traffic Sign Detection and Recognition". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/14675198330887014049.
淡江大學
資訊工程學系碩士班
97
In this paper, we use color and shape to detect and classify traffic signs. Then, the message on the traffic sign is recognized for driver. The method consists of two phases: traffic sign detection and recognition. In the detection stage, we use the distribution of traffic sign on HSV color model to segment the regions of traffic sign, and then use connected component labeling and edge detection to find positions of traffic signs. In the recognition stage, the detected traffic signs are normalized and classified by shape detection. Finally, we input the result to template match system, so information on traffic signs is identified. Our system uses simple algorithm to achieve high detection rate. The format of input image is 640×480 true color bitmap. The average execution time for each image is 671.9ms, the detection rate is 95% and the recognition rate is 81%.
Hsien, Jung-Chieh, e 謝榮桀. "Road Sign Detection and Recognition". Thesis, 2003. http://ndltd.ncl.edu.tw/handle/52667283080897300108.
元智大學
資訊工程學系
91
This study proposes a novel road sign detection and recognition method. The position of road sign in an image is detected using the projection techniques. The features of road sign are then extracted using the techniques of projection, moment and Markov model, which, in turn are used to match the detected road sign to those in the database so that the goal of road sign recognition can be achieved. More specifically, the color images in terms of RGB color system are first converted to HSV color system and then quantized into specific colors existing in road signs. The horizontal and vertical projections of whole images in the specific colors are then used to detect the positions of road signs. In the recognition stage, only local features around the detected positions are used and two-step strategy is adopted. The horizontal and vertical projections of background in local area are used to prune irrelevant road signs. The candidate road signs are then sorted by the horizontal and vertical projections of foreground together with moment or by the techniques of Markov model. The two ranking results are integrated into the final consensus and the one with the first rank is regarded as the recognition result. The effectiveness of the proposed method has been demonstrated by various experiments.
Gill, HK. "Abalone tag detection and recognition". Thesis, 2009. https://eprints.utas.edu.au/19912/1/whole_GillHarpreetKaur2009_thesis.pdf.
Lin, Yuh-Ju, e 林育如. "Chinese Text Detection and Recognition". Thesis, 2019. http://ndltd.ncl.edu.tw/handle/37v6v3.
國立中央大學
資訊工程學系
107
Optical Character Recognition (OCR) is a big challenge of Computer Vision. The degree of challenge has become harder from the task of recognizing the English characters and numbers with specific font and some symbol to the task of detecting and recognizing the text in the wild. And in the domain of text detection and recognition, detecting and recognizing Chinese context is more complex than the English. First, the amount of Chinese character is much more than English, and the shape is much more complex, too. Different from English context, Chinese can be written from left to right, and from top to bottom, also, which makes Chinese text detection and recognition much harder. Training a model of OCR system needs a lot of data with label, both position of the character and what the character is, the more complex scene needs more data with label. We focus on simple task, we just detect and recognize the Chinese text with the scan files. Different from task of text in wild, the block of text is more structural in task that detecting text in scan files. Therefore, we can get a great result with a simple network for text detection. And we just need to separate each line from the region that we detected, and use the line as the input of text recognition. Then, combine the result of OCR and the position we detect, we can get all the text in the scan file. And maybe, with these results, it can develop more applications, file classification takes for an example.
Subudhi, K. Krishan Kumar, e Ramshankar Mishra. "Human Face Recognition and Detection". Thesis, 2011. http://ethesis.nitrkl.ac.in/2568/1/face_recognition_and_detection.pdf.
Jain, Deepak. "Moving Object Detection and Recognition". Thesis, 2017. http://ethesis.nitrkl.ac.in/8858/1/2017_MT_DJain.pdf.
Shen, Yu-sian, e 沈育賢. "Road Traffic Sign Detection and Recognition". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/n6uc3f.
國立臺灣科技大學
高分子系
95
Traffic sign detection and recognition is a difficult task in an outdoor environment. Complex background, weather, illumination-related problems, and even the covered, damaged and rotated signs may make traffic sign detection and recognition more difficult. In the detection, we use RGB and HSI color models to classify colors, and utilize the corner detector mask and shape characteristic to implement the detection components. In the recognition, we use Otsu statistical threshold selecting method and gray-level variance, together with the proposed feature extraction method, to achieve the recognition task. In the experiment, 139 images, including 165 road traffic signs, are to be detected and recognized. 126 traffic signs are accurately identified. Thus, this system yields a recognition rate of about 76%.
Chuang, Chia-Lung, e 莊佳龍. "Vehicle Detection and License Plate Recognition". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/57361447713712641354.
國立中正大學
光機電整合工程研究所
93
License plate recognition system has been extensively used in a variety of applications, such as parking lot management of community and buildings, and searching stolen car of police departments, roadway monitor, car management and so on。In the past study, the needed car is catched by the method of responding and activating. In this system, the video camera continuously shoot the motion-based vehicle and the video frames are analyzed by shadow detection, vehicle location, size of license plate to catch the needed image including vehicle automatically. Moreover, a car can be recognized more times according to the characteristics of Multiple-frames to increase the probability of recognition, since the system can be used in reality more practically than which using shooting statically.