Dissertations / Theses: 'AML pattern detection'

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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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Song, Keng Yew. "Surface defect detection on textured background." Thesis, University of Surrey, 1993. http://epubs.surrey.ac.uk/844113/.

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This thesis addresses the problem of defect detection on complex textural surfaces. In general, whether the texture to be inspected is regular or random, in image terms it is characterized by local variations in pixel grey level values. These normal variations render the problem of texture defect detection extremely difficult as defects are often manifested by grey level changes and their detection requires more than mere pixel comparisons. In the thesis, classical techniques on texture representation are studied and various existing texture defect detection algorithms are reviewed. Three novel algorithms have been developed to tackle the problem of defect detection on random or regular textures. The first two are devoted to the problem of crack detection and the third algorithm is devoted to the problem of detecting regional defects. For texture crack detection, a cojoint spatial and spatial frequency representation, that is, wigner distribution is proposed to model the inspected texture surface. A detailed analysis of the wigner distribution, its properties and the effect of windowing on its crack detection performance are carried out. Two postprocessing methods, ie, probabilistic relaxation labelling and linear filtering are incorporated into the crack detection algorithm to refine the results. The potential of the Wigner model has also been explored by modifying the crack detection algorithm so as to detect other types of defects. For real world applications, an efficient crack detection algorithm based on a new distribution is proposed. The algorithm is shown to produce comparable results but in much shorter time. For regional defect detection, a hybrid chromato-structural approach to colour texture representation is proposed where combined colour texture information is extracted from various chromatic classes associated with the inspected surface. In the approach, a unified defect detection framework which combines a new colour clustering scheme, morphological smoothing and blob analysis are used to capture the relevant combined colour texture information. With this framework, good defect detection results are obtained and presented in this thesis.

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Beattie, R. J. "Edge detection for semantically based early visual processing." Thesis, University of Edinburgh, 1985. http://hdl.handle.net/1842/26281.

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Vespa, Lucas. "Memory-efficient pattern matching for network intrusion detection /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1594486431&sid=15&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Demirel, Hasan. "Training set analysis for image-based facial feature detection." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264934.

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Parent, Pierre 1953. "Trace inference, curvature consistency, and curve detection." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66100.

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Green, Michael Antony. "A multiple resolution approach to feature detection in monocular greyscale images." Thesis, Keele University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387341.

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MOTA, GUILHERME LUCIO ABELHA. "PATTERN DETECTION IN BIDIMENSIONAL IMAGENS: CASES STUDY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2000. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7469@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO A presente dissertação estudo dois problemas de detecção de padrões em imagens com fundo complexo, casos onde os algoritmos de segmentação convencionais não podem proporcionar bons resultados: a localização de Unidades Estruturais (UE`s) em imagens obtidas por Microscópio Eletrônico de Transmissão em Alta Resolução, e a detecção de faces frontais na posição vertical em imagens. Apesar de serem abordados problemas diferentes, as metodologias empregadas na solução de ambos os problemas possuem semelhanças. Uma operação de vizinhança é aplicada a imagem de entrada em busca de padrões de interesse. Sendo cada região extraída desta imagem submetida a um operador matemático composto por etapas de pré-processamento, redução de dimensionalidade e classificação. Na detecção de UE`s foram empregados três métodos distintos de redução de dimensionalidade - Análise de Componentes Principais (PCA), PCA do conjunto de treinamento equilibrado (PCAEq), e um método inédito, eixos que maximizam a distância ao centróide de uma classe (MAXDIST) - e dois modelos de classificador - classificador baseado na distância euclideana (EUC) e rede neural back-propagation (RN). A combinação PCAEq/RN forneceu taxa de detecção de 88% para 25 componentes. Já a combinação MAXDIST/EUC com apenas uma atributo forneceu 82% de detecção com menos falsas detecções. Na detecção de faces foi empregada uma nova abordagem, que utiliza uma rede neural back-propagation como classificador. Aplica-se a sua entrada recebe a representação no subespaço das faces e o erro de reconstrução. Em comparação com os resultados de referência da literatura na área, o método proposto atingiu taxas de detecção similares. This dissertation studies two pattern detection problems in images with complex background, in which standard segmentation techniques do not provide good results: the detection of structural units (SU`s) in images obtained through High resolution transmission Electron Microscopy and the detection of frontal human faces in images. The methods employed in the solution of both problems have many similarities - a neighborhood operator, basically composed of pre-processing, dimensionality reduction and classification steps, scans the input image searching for the patterns of interest. For SU detection three dimensionality reduction methods - Principal Component Analysis (PCA), PCA of the balanced training set (PACEq), and a new method, axis that maximize the distance to a given class centroid (MAXDIST) -, and two classifiers - Euclidean Distance (EUC) and back-propagation neural network (RN). The MAXDIST/EUC combination, with just one component, provided a detection rate of 82% with less false detections. For face detection a new approach was employed, using a back-propagation neural network as classifier. It takes as input a representation in the so-called face space and the reconstruction error (DFFS). In comparison with benchmark results from the literature, the proposed method reached similar detection rates.

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Shen, Ze-ping. "An application of neural networks for the detection of coronary heart disease." Thesis, Brunel University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385186.

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Pan, Xiang. "Approaches for edge detection, pose determination and object representation in computer vision." Thesis, Heriot-Watt University, 1994. http://hdl.handle.net/10399/1378.

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Khashman, Adnan. "AEDS : an edge detection scheme using scale space analysis and neural network arbitration." Thesis, University of Nottingham, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339714.

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Adekunle, Carl Bunmi. "A technique for detecting feature interaction." Thesis, Royal Holloway, University of London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249335.

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Thiel, Stefan U. "The use of image processing techniques for the automated detection of blue-green algae." Thesis, University of South Wales, 1994. https://pure.southwales.ac.uk/en/studentthesis/the-use-of-image-processing-techniques-for-the-automated-detection-of-bluegreen-algae(fd73551d-72d8-46a1-a0e8-e3c08b51f03e).html.

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The determination of water quality in freshwater lakes and reservoirs is an important task, which must be carried out on a regular basis. Information about long term water quality must be provided by the existence of particular organisms, for example blue-green algae. Currently the detection of these algae is done in a very time consuming manual way, involving highly trained biologists, for example those employed by the National Rivers Authority. This thesis is a first investigation in the automatic detection of blue-green algae using image processing techniques. Samples of seven species of blue-green algae and two species of green algae were examined under a microscope and transferred to a computer. The micro scope pictures were then stored as digital images. In order to locate the organisms Image Segmentation routines were applied. In particular, a newly developed LoG Thresholding Operator proved to be effective for the segmentation of biological organisms. Image Enhancement improved the quality and appearance of the segmented species in the images. In the identification process the biological key, which describes some important features of each species, needed to be implemented. With the aid of shape algorithms and textural algorithms both occluding and non-occluding organisms were analyzed and meaningful features were extracted. The obtained features were then used to classify the organisms into different Species. Both, discriminant analysis and neural networks were used for classification purposes. A detection rate of approximately 90% was achieved. The approach has produced promising results and it is hoped that further Investigations will be encouraged.

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Smith, Robert John. "Real-time surface flaw detection for the leather and textile industries using machine vision techniques." Thesis, University of Surrey, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308520.

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Hatzipantelis, Eleftherios. "The design and implementation of a statistical pattern recognition system for induction machine condition monitoring." Thesis, University of Aberdeen, 1995. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU086061.

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Automated fault diagnosis in induction machines is a difficult task and normally requires background information of electrical machines. Here a different methodology to the condition monitoring problem is devised. The approach is based entirely on Digital Signal Processing (DSP) and Statistical Pattern Recognition (PR). Description of machine conditions is extracted from empirical data. The main tasks that must be carried out by a PR-based condition monitoring system are: condition identification, knowledge reinforcement and knowledge creation for previously unseen conditions. The DSP operations are employed to quickly isolate sensor faults and to remove noise using data acquired from a single channel. DSP transformations may seem promising in making the monitoring system portable. Most importantly, they can compensate for operational changes in the machine. These changes affect the supply line currents and the primary signal quantities to be measured, i.e. the current and the axial leakage flux. The data which is input to the statistical monitoring system may be transformed, in the form of features, or remain unaltered. The system exploits the statistical properties of the feature vectors. The particular features, namely the LAR coefficients, convey short-term, high-resolution spectral information. For a long record, the feature vector sequence may provide information about changes in the record spectral characteristics, with time. Many induction machine processes are stationary and they can be properly be dealt with by a simple statistical classifier, e.g. a Gaussian model. For nonstationary processes, the system may employ a more comprehensive tool, namely the Hidden Markov Model. which may track the changing behaviour of the process in question. Initially a limited number of machine conditions are available to the process engineer. By identifying their boundaries, new faulty conditions could be signalled for and adopted into the database.

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Pentney, John Michael. "A hard wired image processor for a scintillating fibre detector of elementary particles." Thesis, University of Cambridge, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.334178.

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Abidogun, Olusola Adeniyi. "Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks." Thesis, University of the Western Cape, 2005. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Huge amounts of data are being collected as a result of the increased use of mobile telecommunications. Insight into information and knowledge derived from these databases can give operators a competitive edge in terms of customer care and retention, marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define usage patterns. Over a period of time, an individual phone generates a large pattern of use. While call data are recorded for subscribers for billing purposes, we are making no prior assumptions about the data indicative of fraudulent call patterns, i.e. the calls made for billing purpose are unlabeled. Further analysis is thus, required to be able to isolate fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process. This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a period of time in a mobile telecommunication network. Our study provides a comparative analysis and application of Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) recurrent neural networks algorithms to user call data records in order to conduct a descriptive data mining on users call patterns. Our investigation shows the learning ability of both techniques to discriminate user call patterns the LSTM recurrent neural network algorithm providing a better discrimination than the SOM algorithm in terms of long time series modelling. LSTM discriminates different types of temporal sequences and groups them according to a variety of features. The ordered features can later be interpreted and labeled according to specific requirements of the mobile service provider. Thus, suspicious call behaviours are isolated within the mobile telecommunication network and can be used to to identify fraudulent call patterns. We give results using masked call data from a real mobile telecommunication network.

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Vasconcelos, Germano Crispim. "An investigation of feedforward neural networks with respect to the detection of spurious patterns." Thesis, University of Kent, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262546.

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Nikitina, Asya F. "Design and implementation of pattern recognition algorithms for the detection of chemicals with a microcantilever sensor array." abstract and full text PDF (free order & download UNR users only), 2007. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1447605.

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Koester, Robert J. "Determining probabilistic spatial patterns of lost persons and their detection characteristics in land search & rescue." Thesis, University of Portsmouth, 2018. https://researchportal.port.ac.uk/portal/en/theses/determining-probabilistic-spatial-patterns-of-lost-persons-and-their-detection-characteristics-in-land-search--rescue(55ed5f1f-fc67-402e-8b24-eb1615d721d6).html.

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Search theory allows the optimization of scarce resources to find a missing person in the shortest possible time. This work focuses on the central paradigm in search theory from a land-based search and rescue perspective: A search planner must send a resource where the subject is located, which is unknown. Original research furthered search theoery in probability of area (POA) by adding the dementia subject category, development of new spatial models, and creating the International Search and Rescue Incident Database (145,000 incidents). Research methodology required developing new data standards to integrate multiple databases. Twenty new subject categories were formulated from the research, as well as heuristic rules based upon search theory. In addition, several models were developed to help predict the location of missing aircraft. A program called Map-Score was created which quantizes the effectiveness of the models. This information has been widely adopted by search and rescue planners around the world. The second component is the conditional probability that the searcher/sensor would detect the subject if the subject were in the search area; this is known as probability of detection. Sweep width experiments have been carried out in order to develop the methodology, create tools to simplify the process, determine actual sweep width values for visual search, and to examine various correction factors. The visual methodology was modified in order to carry out the two-way search problem of detecting subject shouts in response to whistle-blasts along with characterizing clues on a trail. Finally, with enough sweep width experiments completed it was possible to determine the correlation between the easily obtained range of detection and the experimentally derived sweep width, providing a quick field estimation tool. Optimal resource allocation can be obtained by maximizing the probability of success rate. This is determined by using previous research to determine POA, sweep width values and additional research that determined searcher velocity by GIS analysis. Ultimately, all search theory is integrated into a tactical decision aid, which for the first time will allow the search and rescue planner to easily use search theory.

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Joseph, Shari. "Similar detection patterns between children with autism and typically developing children." Thesis, McGill University, 2004. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85016.

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Children with and without autism were evaluated on two change detection tasks that entailed responding to 2 side-by-side images that were displayed on a computer screen. In Experiment 1, a distracter object that remained unchanged was displayed next to a target object that changed in 1 of 3 ways, a global position change, a color change, or a local deletion change. The stimuli consisted of photographs and drawings that were presented at blank intervals of either 50 or 250 ms. In Experiment 2, color and deletion changes were compared in photographs of objects and people. Children with autism were expected to demonstrate enhanced change detection across both experiments, as well as better detection of local than global changes, and superior processing of changes to objects compared to people. Across tasks, both groups performed comparably in change detection ability. Children with autism did not demonstrate enhanced visual detection, and evidenced similar patterns of visual discrimination compared to typically developing children. They also exhibited similar processing of changes to objects and people. Age was positively associated with improvements in change detection among both groups of children.

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Gabelli, Filippo. "Security analysis of physical attacks to offshore O&G facilities." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Chemical and petrochemical plants are susceptible to malicious acts due to the attractiveness brought by the substances handled, the possibility to thieve secret information, and their strategic importance. Thus, a number of qualitative and semi-quantitative methodologies have been developed for the assessment of vulnerabilities of such sites. Similarly, offshore facilities, that are central in the production of hydrocarbons, were subjected to physical and cyber security events as is outlined in relevant past accident analysis. However, there are just few methodologies tailored for the identification of vulnerabilities in that specific environment. The current study is aimed at closing such gap by building a proper technique for modeling attacks towards offshore sites. The method has been developed just for physical attacks and has been drafted with reference to the ASD model, for rendering the adversary pattern, and to the single path computer model (EASI), for the estimation of the PPS effectiveness. The methodology is intended as a tool for supporting standard SVA/SRA procedures, with particular reference to the API RP 780, therefore its purpose is to provide a systematic approach to the analysis. It has been applied to a case study, represented by an offshore production platform, to point out the results obtained and to outline the link with the API SRA methodology.

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Li, Jie Sheng, and 李界昇. "Generic virus detection & pattern generation through software emulation." Thesis, 1995. http://ndltd.ncl.edu.tw/handle/75787932507790184192.

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Zhu, Yuliang. "Hand detection and tracking in an active vision system /." 2003. http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99410.

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Thesis (M.Sc.)--York University, 2003. Graduate Programme in Computer Science. Typescript. Includes bibliographical references (leaves 104-111). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99410

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Wang, Ting-Chih, and 王鼎智. "Image Forgery Detection Based on Local Binary Pattern and Gradient Operator." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394067%22.&searchmode=basic.

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碩士 國立中興大學 資訊科學與工程學系所 107 In this age of advanced Internet, tampered image have become the most dangerous method to mislead humans or cause panic, especially in this era of platform for online socializing. The above description shows that the importance of forgery image detection. Our proposed method uses YCbCr color model for pre-process. In order to enhance the accuracy of the forgery image deteciotn, we use two methods to get the features.The first method uses Local Binary Pattern for image to extract the local texture feature of image.The second is to sharpen the image and then use the Gradient Operator to find the edge. The main purpose of these two steps is to first enhance the details of the image before finding the edge of the image. The outstanding effect of block discrete cosine transform (BDCT) in detecting the image decorrelation is used in our method. So we calculate the standard deviations of the BDCTs got from the pre-processing computed separately are considered as the features for forgery detection in our method. We can get two different sets of features by the above two methods, and our proposed method combines the two features. Because the dimension of the feature is too large, we use feature selection to reduce the dimension. Finally, we can see from the experimental results that our method can effectively increase the detection accuracy by adding the edge of the enhanced image.

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McDonough, Colleen. "The role of prosody and complexity in infants' detection of repeated speech patterns /." Diss., 2004. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3147326.

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Fay, Justin Campbell. "Detecting natural selection from patterns of DNA polymorphism and divergence /." 2001. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3019915.

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Dissertations / Theses on the topic 'AML pattern detection'

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