Relevant bibliographies by topics / Pattern recognition systems – Evaluation

Academic literature on the topic 'Pattern recognition systems – Evaluation'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Pattern recognition systems – Evaluation"

1

Elmqvist, Niklas, and Ji Soo Yi. "Patterns for visualization evaluation." Information Visualization 14, no. 3 (December 10, 2013): 250–69. http://dx.doi.org/10.1177/1473871613513228.

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We propose a pattern-based approach to evaluating data visualization: a set of general and reusable solutions to commonly occurring problems in evaluating visualization tools, techniques, and systems. Patterns have had significant impact in a wide array of disciplines, particularly software engineering, and we believe that they provide a powerful lens for characterizing visualization evaluation practices by offering practical, tried-and-tested tips, and tricks that can be adopted immediately. The 20 patterns presented here have also been added to a freely editable Wiki repository. The motivation for creating this evaluation pattern language is to (a) capture and formalize “dark” practices for visualization evaluation not currently recorded in the literature, (b) disseminate these hard-won experiences to researchers and practitioners alike, (c) provide a standardized vocabulary for designing visualization evaluation, and (d) invite the community to add new evaluation patterns to a growing repository of patterns.
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Dobler, Lorenz, Oganowski Marek, Eckard Rolf, Günsel Andreas, Müller Antje, Kemper Fritz Hubertus, and Wiesmüller Gerhard Andreas. "Rapid Evaluation of Human Biomonitoring Data Using Pattern Recognition Systems." Journal of Toxicology and Environmental Health, Part A 71, no. 11-12 (June 2, 2008): 816–26. http://dx.doi.org/10.1080/15287390801985778.

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Cao, Runsheng, and Thomas McAvoy. "Evaluation of a pattern recognition adaptive PID controller." Automatica 26, no. 4 (July 1990): 797–801. http://dx.doi.org/10.1016/0005-1098(90)90055-m.

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Nagaraju, C., D. Sharadamani, C. Maheswari, and D. Vishnu Vardhan. "Evaluation of LBP-Based Facial Emotions Recognition Techniques to Make Consistent Decisions." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 06 (August 12, 2015): 1556008. http://dx.doi.org/10.1142/s021800141556008x.

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Decision making is one of the smouldering problems in day to day works. Human emotions play crucial role in decision-making systems. While person is in high emotion he cannot make proper decision. Robust local binary pattern (RLBP) operator is more powerful to recognize the emotions and extends the features of local binary pattern (LBP). However, there are some precincts like discriminating bright faces against dark features and vice versa and intra-class variances increase. The RLBP solves this problem by finding minimum of LBP codes and their complements. However, it miss the mark for different local structures a similar feature is obtained, weak contrast local patterns and similar strong contrast local patterns. Hence, the discriminative robust local binary pattern (DRLBP) method is proposed to retain the contrast information of image patterns next to considering both edge and texture information. Nevertheless, LBP family methods are highly sensitive to noise. To trounce these drawbacks this paper extends fuzzy rule-based DRLBP which is more robust to noise, low contrasted, uneven lighting conditions, variations in expressions and rotation variant images.
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5

Chang, C. S. "Online transient stability evaluation of interconnected power systems using pattern recognition strategy." IEE Proceedings C Generation, Transmission and Distribution 140, no. 2 (1993): 115. http://dx.doi.org/10.1049/ip-c.1993.0016.

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6

Bennett, Laura F. "Knowledge-based evaluation of the segmentation component in automatic pattern recognition systems." Optical Engineering 30, no. 2 (February 1, 1991): 154. http://dx.doi.org/10.1117/12.55782.

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7

Fang, Chi, Changsong Liu, Liangrui Peng, and Xiaoqing Ding. "Automatic performance evaluation of printed Chinese character recognition systems." International Journal on Document Analysis and Recognition 4, no. 3 (March 1, 2002): 177–82. http://dx.doi.org/10.1007/s100320200068.

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8

Kavdır, İ., and D. E. Guyer. "Evaluation of different pattern recognition techniques for apple sorting." Biosystems Engineering 99, no. 2 (February 2008): 211–19. http://dx.doi.org/10.1016/j.biosystemseng.2007.09.019.

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9

Gao, Yuanheng, Leilei Wang, and Heqing Zhang. "Intelligent urban ecological suitability system based on pattern recognition." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5009–16. http://dx.doi.org/10.3233/jifs-179986.

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Today, with the rapid development of urbanization, the ecological and environmental problems of the city have become increasingly serious and have become the focus of the world. The most important issue facing the majority of ecological workers is how to apply the theory of ecology to solve today’s problems. The various environmental problems faced in urban life and the sustainable development of the city’s ecological civilization. How ecological planning is used to coordinate the relationship between people and the natural environment and natural resources is increasingly gaining attention and expanding the range and scope of its applications. However, as an ecological suitability analysis based on ecological planning, many analytical methods and systems are still being explored and developed due to the geographical complexity and factor diversity involved. In recent years, with the rapid development of computer hardware and software technology, pattern recognition has received more and more attention, pattern recognition and image processing technology has become more and more perfect, and has been successfully applied in more and more fields. This thesis begins to focus on the urban ecological suitability content based on pattern recognition technology and image processing. The main contents of this thesis include: introducing the background of urban ecological suitability and the status quo of ecological suitability analysis and existing research methods. According to the structure of the urban ecosystem and the national standards for the construction of ecological systems and ecological cities, an indicator system for ecological suitability evaluation is established. A pattern recognition system and common pattern recognition and image processing methods are introduced. Based on some common evaluation methods and models, the pattern recognition technology theory and image processing technology are introduced into the urban ecological suitability analysis. Based on the image system theory and vector projection principle, the ecological suitability analysis is established. Associated projection model. The model considers the evaluation sample and the quality standards at each level as vectors, and respectively projects the same vector ideal. Based on establishing the ecological suitability evaluation index system and standards, the ecological suitability was evaluated by using the model.
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CAPORASO, J. GREGORY, WILLIAM A. BAUMGARTNER, DAVID A. RANDOLPH, K. BRETONNEL COHEN, and LAWRENCE HUNTER. "RAPID PATTERN DEVELOPMENT FOR CONCEPT RECOGNITION SYSTEMS: APPLICATION TO POINT MUTATIONS." Journal of Bioinformatics and Computational Biology 05, no. 06 (December 2007): 1233–59. http://dx.doi.org/10.1142/s0219720007003144.

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The primary biomedical literature is being generated at an unprecedented rate, and researchers cannot keep abreast of new developments in their fields. Biomedical natural language processing is being developed to address this issue, but building reliable systems often requires many expert-hours. We present an approach for automatically developing collections of regular expressions to drive high-performance concept recognition systems with minimal human interaction. We applied our approach to develop MutationFinder, a system for automatically extracting mentions of point mutations from the text. MutationFinder achieves performance equivalent to or better than manually developed mutation recognition systems, but the generation of its 759 patterns has required only 5.5 expert-hours. We also discuss the development and evaluation of our recently published high-quality, human-annotated gold standard corpus, which contains 1,515 complete point mutation mentions annotated in 813 abstracts. Both MutationFinder and the complete corpus are publicly available at .
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Dissertations / Theses on the topic "Pattern recognition systems – Evaluation"

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Codrescu, Lucian. "An evaluation of the Pica architecture for an object recognition application." Thesis, Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15483.

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Eakins, John Paul. "Design and evaluation of a shape retrieval system." Thesis, University of Newcastle Upon Tyne, 1990. http://hdl.handle.net/10443/2056.

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While automated storage and retrieval systems for textual and numeric data are now commonplace, the development of analogous systems for pictorial data has lagged behind - not through the lack of need for such systems, but because their development involves a number of significant problems. The aim of this project is to investigate these problems by designing and evaluating an information retrieval system for a specific class of picture, 2-dimensional engineering drawings. This involves consideration of the retrieval capabilities needed by such· a system, what storage structures it would require, how the salient features of each drawing should be represented, how query and stored shapes should be matched, what features were of greatest importance in retrieval, and the interfaces necessary to formulate queries and display results. A form of hierarchical boundary representation has been devised for stored shapes, in which each boundary can be viewed as a series of levels of steadily increasing complexity. A set of rules for boundary and segment ordering ensures that as far as possible, each shape has a unique representation. For each level at which each boundary can be viewed, a set of invariant shape features characterizing that level is extracted and added to the shape representation stored in the database. Two classes of boundary feature have been defmed; global features, characteristic of the boundary as a whole, and local features, corresponding to individual fragments of the boundary. To complete the shape description, position features are also computed and stored, to specify the pattern of inner boundaries within the overall shape. Six different tYPes of shape retrieval have been distinguished, although the prototype system can offer only three of these - exact shape matching, partial shape matching and similarity matching. Complete or incomplete query shapes can be built up at a terminal, and subjected to a feature extraction process similar to that for stored drawings, yielding a query fIle that can be matched against the shape database. A variety of matching techniques is provided, including similarity estimation using global or local features, tests for the existence of specified local features in stored drawings, and cumulative angle vs distance matching between query and stored shape boundaries. Results can be displayed in text or graphical form. The retrieval performance of the system in similarity matching mode has been evaluated by comparing its rankings of shapes retrieved in response to test queries with those obtained by a group of human subjects faced with the same task. Results, expressed as normalized recall and precision, are encouraging, particularly for similarity estimation using either global or local boundary features. While the detailed results are of limited significance until confrrmed with larger test collections, they appear sufficiently promising to warrant the development of a more advanced prototype capable of handling 3-D geometric models. Some design aspects of the system would appear to be applicable to a wider range of pictorial information systems.
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Pettersson, Johan. "Real-time Object Recognition on a GPU." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10238.

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Shape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion.

The objects to be recognized can be translated, rotated and scaled.

The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation).

The similarity measure is based on dot products between normalized gradient directions in edges.

Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space.

A resolution pyramid is used to form a heuristic for the search that then gains real-time performance.

For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale.

We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required.

SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU.

This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds).

It is also very scalable and easily benefits from future devices without effort.

An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions.

The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.

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4

Orwin, Claire Nicola. "An evaluation of the performance of an optical measurement system for the three-dimensional capture of the shape and dimensions of the human body." Thesis, De Montfort University, 2000. http://hdl.handle.net/2086/4908.

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As the clothing industry moves away from traditional models of mass production there has been increased interest towards customised clothing. The technology to produce cost effective customised clothing is already in place however the prerequisite to customised clothing is accurate body dimensional data. In response, image capture systems have been developed which are capable of recording a three-dimensional image of the body, from which measurements and shape information may be extracted. The use of these systems for customised clothing has, to date, been limited due to issues of inaccuracy, cost and portability. To address the issue of inaccuracy a diagnostic procedure has been developed through the performance evaluation of an image capture system. By systematically evaluating physical and instrumental parameters the more relevant sources of potential error were identified and quantified and subsequently corrected to form a `closed loop' experimental procedure. A systematic test procedure is therefore presented which may be universally applied to image capture systems working on the same principle. The methodology was based upon the isolation and subsequent testing of variables that were thought to be potential sources of error. The process therefore included altering the physical parameters of the target object in relation to the image capture system and amending the configuration and calibration settings within the system. From the evaluation the most relevant sources of error were identified as the cosine effect, measurement point displacement, the dimensional differences between views and the influence of the operator in measurement. The test procedure proved to be effective in both evaluating the performance of the system under investigation and in enabling the quantification of errors. Both random and systematic errors were noted which may be quantified or corrected to enable improved accuracy in the measured results. Recommendations have been made for the improvement of the performance of the current image capture system these include the integration of a cosine effect correction algorithm and suggestions for the automation of the image alignment process. The limitations of the system such as its reliance on manual intervention for both the measurement and stitching processes, are discussed, as is its suitability for providing dimensional information for bespoke clothing production. Recommendations are also made for the creation of an automated test procedure for testing the performance of alternative image capture systems, which involves evaluating the accuracy of object replication both for multiple and single image capture units using calibration objects which combine a range of surfaces.
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Kluever, Kurt Alfred. "Evaluating the usability and security of a video CAPTCHA /." Online version of thesis, 2008. http://hdl.handle.net/1850/7886.

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Avan, Selcuk Kazim. "Feature Set Evaluation For A Generic Missile Detection System." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608130/index.pdf.

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Missile Detection System (MDS) is one of the main components of a self-protection system developed against the threat of guided missiles for airborne platforms. The requirements such as time critical operation and high accuracy in classification performance make the &lsquo
Pattern Recognition&rsquo
problem of an MDS a hard task. Problem can be defined in two main parts such as &lsquo
Feature Set Evaluation&rsquo
(FSE) and &lsquo
Classifier&rsquo
designs. The main goal of feature set evaluation is to employ a dimensionality reduction process for the input data set, while not disturbing the classification performance in the result. In this thesis study, FSE approaches are investigated for the pattern recognition problem of a generic MDS. First, synthetic data generation is carried out in software environment by employing generic models and assumptions in order to reflect the nature of a realistic problem environment. Then, data sets are evaluated in order to draw a baseline for further feature set evaluation approaches. Further, a theoretical background including the concepts of Class Separability, Feature Selection and Feature Extraction is given. Several widely used methods are assessed in terms of convenience for the problem by giving necessary justifications depending on the data set characteristics. Upon this background, software implementations are performed regarding several feature set evaluation techniques. Simulations are carried out in order to process dimensionality reduction. For the evaluation of the resulting data sets in terms of classification performance, software implementation of a classifier is realized. Resulting classification performances of the applied approaches are compared and evaluated.
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Befus, Chad R., and University of Lethbridge Faculty of Arts and Science. "Design and evaluation of dynamic feature-based segmentation on music." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science, c2010, 2010. http://hdl.handle.net/10133/2531.

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Segmentation is an indispensable step in the field of Music Information Retrieval (MIR). Segmentation refers to the splitting of a music piece into significant sections. Classically there has been a great deal of attention focused on various issues of segmentation, such as: perceptual segmentation vs. computational segmentation, segmentation evaluations, segmentation algorithms, etc. In this thesis, we conduct a series of perceptual experiments which challenge several of the traditional assumptions with respect to segmentation. Identifying some deficiencies in the current segmentation evaluation methods, we present a novel standardized evaluation approach which considers segmentation as a supportive step towards feature extraction in the MIR process. Furthermore, we propose a simple but effective segmentation algorithm and evaluate it utilizing our evaluation approach.
viii, 94 leaves : ill. ; 29 cm
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8

Ganapathy, Priya. "Development and Evaluation of a Flexible Framework for the Design of Autonomous Classifier Systems." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1261335392.

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9

Ihnatenko, N. V. "Systems for automatic pattern recognition." Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34837.

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Pattern recognition aims to make the process of learning and detection of patterns explicit, such that it can partially or entirely be implemented on computers. Automatic (machine) recognition, description, classification (grouping of patterns into pattern classes) have become important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. In almost any area of science in which observations are studied but the underlying mathematical or statistical models are not available, pattern recognition can be used to support human concept acquisition or decision making. Given a group of objects, there are two ways to build a classification or recognition system, supervised, i.e., with a teacher, or unsupervised, without the help of a teacher. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/34837
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Ferreira, Edgar Ricardo. "Procedimentos automáticos para apoio na avaliação de pavimentos com o uso de imagens digitais." Universidade Federal de Viçosa, 2010. http://locus.ufv.br/handle/123456789/819.

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Made available in DSpace on 2015-03-26T12:34:08Z (GMT). No. of bitstreams: 1 texto completo.pdf: 5587368 bytes, checksum: 8953571965e3894399a67c5d550889e4 (MD5) Previous issue date: 2010-02-09
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Pavements surface distresses are discontinuities on the road which affect the safety and comfort of the user, as well as to possible needs for interventions on the road. The knowledge concerning the pavement layers also works, along with other structural parameters, analyzing further actions on the track. This thesis proposes the use of digital pavement images to detect surface distresses and to find out the pavement layers. This study aims a modern alternative against the current traditional technique of pavement surface defects inspections in Brazil. Firstly, under the spectroradiometer, it was analyzed several patterns relative to the conditions of the asphalt pavement surface in order to define the spectral range that best discriminate against those patterns. By the technique of automatic pattern recognition, it was analyzed the orbital images of high resolution and ground images from the asphalt pavement, using as a rule of decision the algorithm Maximum Likelihood and Artificial Neural Networks. Regarding the features discrimination it applied spectral data to the multispectral images and textural information to the monochrome images. It was worked with a Ground Penetration Radar (GPR) with the purpose of describing the pavement layers determination, thus it acquires the the subsurface profile, resulting in a continuous image of the pavement layers and the automatic classification of images, and in the automatic classification of those images, it was also used textural information to get characteristics as well as the algorithm of the Maximum Likelihood and Artificial Neural Networks as rule of decision. Subsequently, the data obtained in the classification of images, the pavement distresses and its layers in order, set out to ascertain the possibility of using them in a trade Pavement Management System (PMS). The results of classification using orbital images of high resolution were not encouraging, however, when it worked with ground images, the results were surprisingly good, so it indicated a promising possibility in this way. In the classification of the subsurface image pavement, the results were so good so far pretty satisfactory, but lower than the defects classification. Regarding the use of results in the images classification and their use as data in a trade Management System Commercial, it had no major difficulties in this mentioned proceeding.
Defeitos nas superfícies de pavimentos asfálticos são descontinuidades na pista de rolamento que afetam a segurança e o conforto do usuário, além de indicativos da necessidade de intervenções na via. O conhecimento das camadas do pavimento também é utilizado, juntamente com outros parâmetros estruturais, na análise de uma futura intervenção na via. Nesta tese, propõe-se o uso de imagens digitais do pavimento para detectar defeitos superficiais e para identificarr as camadas do pavimento. Quanto aos defeitos superficiais, a motivação é a possibilidade de se obter uma alternativa à técnica de levantamento tradicional de defeitos superficiais em uso no Brasil. Inicialmente, com o auxílio de um espectrorradiômetro, analisaram-se vários padrões correspondentes às condições da superfície do pavimento asfáltico, com o intuito de definir a faixa espectral que melhor discrimine aqueles padrões. Usando-se a técnica de reconhecimento automático de padrões, analisaram-se imagens orbitais de altíssima resolução e terrestre do pavimento asfáltico, empregando como regra de decisão o algoritmo da Máxima Verossimilhança e Redes Neurais Artificiais. Para a fase de discriminação das características, utilizou-se, nas imagens multiespectrais, dados espectrais e nas imagens monocromáticas, informações texturais. Para a determinação das camadas do pavimento usou-se um Ground Penetration Radar (GPR) na aquisição do perfil subsuperficial, obtendo-se uma imagem contínua das camadas do pavimento, e na classificação automática dessas imagens, usou-se, também, informações texturais para extrair características, o algoritmo da Máxima Verossimilhança e Redes Neurais Artificiais como regra de decisão. Posteriormente, com os dados obtidos na classificação sobre as imagens, ou seja, os defeitos superficiais do pavimento e suas camadas, verifica-se a possibilidade de usá-los em um Sistema de Gerência de Pavimentos (SGP) comercial. Os resultados da classificação com o uso de imagem orbital não foram animadores. No entanto, quando se usou imagens terrestres para a classificação, os resultados foram surpreendentemente bons, indicando ser uma possibilidade bastante promissora. Na classificação da imagem subsuperficial do pavimento, os resultados foram razoavelmente satisfatórios, porém, inferiores ao da classificação dos defeitos. Quanto ao uso dos resultados obtidos na classificação das imagens e sua utilização como dados em um Sistema de Gerência de Pavimentos comercial, verificou-se, no sistema comercial analisado, não haver grandes dificuldades neste procedimento.
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Books on the topic "Pattern recognition systems – Evaluation"

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A, Sadjadi Firooz, ed. Selected papers on performance evaluation of signal and image processing systems. Bellingham, Wash., USA: SPIE Optical Engineering Press, 1993.

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Zi dong mu biao shi bie ping gu fang fa ji ying yong: Automatic target recognition evaluation method and its application. Beijing: Ke xue chu ban she, 2013.

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Grubb, Teryl G. Pattern recognition: A simple model for evaluating wildlife habitat. [Fort Collins, Colo.]: USDA Forest Service, Rocky Mountain Forest and Range Experiment Station, 1988.

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Rainer, Stiefelhagen, and Garofolo John S, eds. Multimodal technologies for perception of humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR 2006, Southampton, UK, April 6-7, 2006 : revised selected papers. Berlin: Springer, 2007.

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1967-, Koutroumbas Konstantinos, ed. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.

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1967-, Koutroumbas Konstantinos, ed. Pattern recognition. San Diego: Academic Press, 1999.

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Theodoridis, S. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.

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James, Mike. Pattern recognition. New York: Wiley, 1988.

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Morton, Nadler. Pattern recognition engineering. New York: Wiley, 1993.

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Magnini, Bernardo. Evaluation of Natural Language and Speech Tools for Italian: International Workshop, EVALITA 2011, Rome, January 24-25, 2012, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Book chapters on the topic "Pattern recognition systems – Evaluation"

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Grother, Patrick, and Mei Ngan. "Evaluation of Face Recognition Systems." In Advances in Computer Vision and Pattern Recognition, 381–403. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74697-1_17.

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Beltrán, Viviana, Mickaël Coustaty, Nicholas Journet, Juan C. Caicedo, and Antoine Doucet. "An Extended Evaluation of the Impact of Different Modules in ST-VQA Systems." In Pattern Recognition and Artificial Intelligence, 562–74. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59830-3_49.

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Sierszeń, Artur, and Łukasz Sturgulewski. "Evaluation of Reliability of a Decision-Making Process Based on Pattern Recognition." In Computer Recognition Systems 4, 109–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20320-6_12.

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Kushal, Krishna, Sujala D. Shetty, and Aljo Jose. "Performance Evaluation of a Progression of Recommender System Models." In Computational Intelligence in Pattern Recognition, 245–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2543-5_21.

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Melton, R. B. "Knowledge Based Systems in Nondestructive Evaluation(a)." In Signal Processing and Pattern Recognition in Nondestructive Evaluation of Materials, 199–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83422-6_15.

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Mandayam, S., L. Udpa, S. S. Udpa, and W. Lord. "Fuzzy Inference Systems for Invariant Pattern Recognition in MFL NDE." In Review of Progress in Quantitative Nondestructive Evaluation, 805–12. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0383-1_105.

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Marshakov, Daniil V., Vasily V. Galushka, Vladimir A. Fathi, and Denis V. Fathi. "Evaluation of Neural Network Output Results Reliability in Pattern Recognition." In Advances in Intelligent Systems and Computing, 503–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01818-4_50.

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Bhowmik, Tanima, Rohan Mojumder, Dibyendu Ghosh, and Indrajit Banerjee. "An Evaluative Review on Various Tele-Health Systems Proposed in COVID Phase." In Computational Intelligence in Pattern Recognition, 201–10. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_20.

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Liu, Tianliang, Congcong Liang, Xiubin Dai, and Jiebo Luo. "Arithmetic Evaluation System Based on MixNet-YOLOv3 and CRNN Neural Networks." In Pattern Recognition. ICPR International Workshops and Challenges, 344–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68821-9_31.

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Meza, Pablo, César San Martin, Esteban Vera, and Sergio Torres. "A Quantitative Evaluation of Fixed-Pattern Noise Reduction Methods in Imaging Systems." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 285–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16687-7_40.

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Conference papers on the topic "Pattern recognition systems – Evaluation"

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Minglun Ren, Juanjuan Duan, and Shanlin Yang. "Decision models evaluation using fuzzy pattern recognition." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443430.

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Chen, Zhaoyang, and Guilin Zhang. "General quantitative approach to performance evaluation of automatic target recognition (ATR) systems." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441576.

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Lee, Samuel C., Elisa T. Lee, and Yiming Wang. "New scientific accuracy measure for performance evaluation of human-computer diagnostic systems." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441586.

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Huang, Shike, Lijuan Li, Baoguo Chen, and Zhenyu Wang. "Performance evaluation system of signal processing algorithms." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441579.

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"Information Theoretic Text Classification Methods Evaluation." In 8th International Workshop on Pattern Recognition in Information Systems. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001740200770085.

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Impedovo, Donato, and Giuseppe Pirlo. "Generating Sets of Classifiers for the Evaluation of Multi-expert Systems." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.530.

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Liew, C. K., and M. Veidt. "Optimization of Neural Network Pattern Recognition Systems for Guided Waves Damage Identification in Beams." In REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2007. http://dx.doi.org/10.1063/1.2718029.

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Fujigaki, Motoharu, Masafumi Miwa, Atsushi Nakashima, Myung S. Kim, Masato Soga, and Hiroki Tanikawa. "Development of multispectral infrared camera system for plant health evaluation." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441398.

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Rao, Pranav, and J. Manikandan. "Design and evaluation of logistic regression model for pattern recognition systems." In 2016 IEEE Annual India Conference (INDICON). IEEE, 2016. http://dx.doi.org/10.1109/indicon.2016.7839010.

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Valarmathy, S., M. Arun Kumar, and R. Sangeetha. "Evaluation of face recognition using vector features in local pattern descriptors." In 2016 3rd International Conference on Devices, Circuits and Systems (ICDCS). IEEE, 2016. http://dx.doi.org/10.1109/icdcsyst.2016.7570615.

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Reports on the topic "Pattern recognition systems – Evaluation"

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Slyh, Raymond E., Eric G. Hansen, and Timothy R. Anderson. AFRL/HECP Speaker Recognition Systems for the 2004 NIST Speaker Recognition Evaluation. Fort Belvoir, VA: Defense Technical Information Center, December 2004. http://dx.doi.org/10.21236/ada430750.

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Bodson, D. Simulation and Evaluation of the AT&T Proposed Pattern Recognition Algorithm for Group 4 Facsimile. Fort Belvoir, VA: Defense Technical Information Center, June 1985. http://dx.doi.org/10.21236/ada160864.

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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Schneuwly, Sonja, and Caroline Chandler. Evaluation of transformational R&I policy: Lessons learned based on a retrospective review of food systems R&I investment in the EU. Fteval - Austrian Platform for Research and Technology Policy Evaluation, April 2022. http://dx.doi.org/10.22163/fteval.2022.549.

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This paper shares our experience of developing an EU-level baseline for research and innovation (R&I) in food systems, in support of the European Commission’s transformation agenda, with specific reference to the Food 2030 initiative. Food 2030 relates to the EU’s mission-oriented approach to R&I, viewing it within the context of a dynamic food system with multiple dependencies and many different actors. This approach aligns with a growing recognition that, in order to achieve transformational change, the interactions and interdependencies of all components within a given system and its relationship to other systems must be considered. In a transformative R&I system, innovation itself is no longer the endgoal but is viewed as an enabler to solve societal and environmental challenges (the end-objective). Linking such broader outcomes back to specific R&I inputs is not a straightforward endeavour. Furthermore, the inter- and transdisciplinary nature of a systems approach, as well as the nature of systems thinking itself, make it hard to define evaluative boundaries. Traditional public sector approaches to supporting R&I do not align well with such an approach, with implications for evaluating R&I policy. The paper focuses specifically on the novel aspects of the EU’s approach to framing food systems R&I and the evaluation challenges this presents, as well as how we have worked to mitigate these.
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Robinson, Andy. Monitoring and Evaluation for Rural Sanitation and Hygiene: Framework. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/slh.2021.027.

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The monitoring and evaluation (M&E) Guidelines and Framework presented in this document (and in the accompanying M&E Indicator Framework) aim to encourage stakeholders in the rural sanitation and hygiene sector to take a more comprehensive, comparable and people focused approach to monitoring and evaluation. Many M&E frameworks currently reflect the interests and ambitions of particular implementing agencies – that is, community-led total sanitation (CLTS) interventions focused on open-defecation free (ODF) outcomes in triggered communities; market-based sanitation interventions focused on the number of products sold and whether sanitation businesses were profitable; and sanitation finance interventions reporting the number of facilities built using financial support. Few M&E frameworks have been designed to examine the overall sanitation and hygiene situation – to assess how interventions have affected sanitation and hygiene outcomes across an entire area (rather than just in specific target communities); to look at who (from the overall population) benefitted from the intervention, and who did not; to report on the level and quality of service used; or examine whether public health has improved. Since 2015, the Sustainable Development Goals (SDGs) have extended and deepened the international monitoring requirements for sanitation and hygiene. The 2030 SDG sanitation target 6.2 includes requirements to: • Achieve access to adequate sanitation and hygiene for all • Achieve access to equitable sanitation and hygiene for all • End open defecation • Pay special attention to the needs of women and girls • Pay special attention to those in vulnerable situations The 2030 SDG sanitation target calls for universal use of basic sanitation services, and for the elimination of open defecation, both of which require M&E systems that cover entire administration areas (i.e. every person and community within a district) and which are able to identify people and groups that lack services, or continue unsafe practices. Fortunately, the SDG requirements are well aligned with the sector trend towards system strengthening, in recognition that governments are responsible both for the provision of sustainable services and for monitoring the achievement of sustained outcomes. This document provides guidelines on the monitoring and evaluation of rural sanitation and hygiene, and presents an M&E framework that outlines core elements and features for reporting on progress towards the 2030 SDG sanitation target (and related national goals and targets for rural sanitation and hygiene), while also encouraging learning and accountability. Given wide variations in the ambition, capacity and resources available for monitoring and evaluation, it is apparent that not all of the M&E processes and indicators described will be appropriate for all stakeholders. The intention is to provide guidelines and details on useful and progressive approaches to monitoring rural sanitation and hygiene, from which a range of rural sanitation and hygiene duty bearers and practitioners – including governments, implementation agencies, development partners and service providers – can select and use those most appropriate to their needs. Eventually, it is hoped that all of the more progressive M&E elements and features will become standard, and be incorporated in all sector monitoring systems.
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Robinson, Andy. Monitoring and Evaluation for Rural Sanitation and Hygiene: Framework. Institute of Development Studies (IDS), December 2021. http://dx.doi.org/10.19088/slh.2021.025.

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The monitoring and evaluation (M&E) Guidelines and Framework presented in this document (and in the accompanying M&E Indicator Framework) aim to encourage stakeholders in the rural sanitation and hygiene sector to take a more comprehensive, comparable and people focused approach to monitoring and evaluation. Many M&E frameworks currently reflect the interests and ambitions of particular implementing agencies – that is, community-led total sanitation (CLTS) interventions focused on open-defecation free (ODF) outcomes in triggered communities; market-based sanitation interventions focused on the number of products sold and whether sanitation businesses were profitable; and sanitation finance interventions reporting the number of facilities built using financial support. Few M&E frameworks have been designed to examine the overall sanitation and hygiene situation – to assess how interventions have affected sanitation and hygiene outcomes across an entire area (rather than just in specific target communities); to look at who (from the overall population) benefitted from the intervention, and who did not; to report on the level and quality of service used; or examine whether public health has improved. Since 2015, the Sustainable Development Goals (SDGs) have extended and deepened the international monitoring requirements for sanitation and hygiene. The 2030 SDG sanitation target 6.2 includes requirements to: • Achieve access to adequate sanitation and hygiene for all • Achieve access to equitable sanitation and hygiene for all • End open defecation • Pay special attention to the needs of women and girls • Pay special attention to those in vulnerable situations The 2030 SDG sanitation target calls for universal use of basic sanitation services, and for the elimination of open defecation, both of which require M&E systems that cover entire administration areas (i.e. every person and community within a district) and which are able to identify people and groups that lack services, or continue unsafe practices. Fortunately, the SDG requirements are well aligned with the sector trend towards system strengthening, in recognition that governments are responsible both for the provision of sustainable services and for monitoring the achievement of sustained outcomes. This document provides guidelines on the monitoring and evaluation of rural sanitation and hygiene, and presents an M&E framework that outlines core elements and features for reporting on progress towards the 2030 SDG sanitation target (and related national goals and targets for rural sanitation and hygiene), while also encouraging learning and accountability. Given wide variations in the ambition, capacity and resources available for monitoring and evaluation, it is apparent that not all of the M&E processes and indicators described will be appropriate for all stakeholders. The intention is to provide guidelines and details on useful and progressive approaches to monitoring rural sanitation and hygiene, from which a range of rural sanitation and hygiene duty bearers and practitioners – including governments, implementation agencies, development partners and service providers – can select and use those most appropriate to their needs. Eventually, it is hoped that all of the more progressive M&E elements and features will become standard, and be incorporated in all sector monitoring systems.
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Pyta, V., Bharti Gupta, Shaun Helman, Neale Kinnear, and Nathan Stuttard. Update of INDG382 to include vehicle safety technologies. TRL, July 2020. http://dx.doi.org/10.58446/thco7462.

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Driving is one of the riskiest work tasks, accounting for around one third of fatal crashes in the UK. Organisations are expected to manage work-related road safety (WRRS) in the same way that they manage other health and safety risks. The Health and Safety Executive (HSE) and Department for Transport (DFT) issue joint guidance on this in INDG382 ‘Driving at work: managing work-related road safety’. HSE and DFT were seeking to update INDG382 to include reference to vehicle safety technologies that could enable employers to monitor safety related events or driver behaviours, to support learning and safety improvements. They commissioned TRL to - Conduct a literature review focused on evaluations of the impact of these technologies on work-related road safety (specifically, crash risk) Lead in-depth interviews with representatives of organisations who had implemented technology-based safety monitoring in their fleet and stakeholders and experts who provided further insights into factors affecting successful implementation. TRL found that telematics systems, drowsiness and distraction recognition systems, and collision warning systems have significant potential safety benefits, but rigorous published evaluation of safety-focused telematics in the fleet context is limited. There is good evidence for the safety benefits of intelligent speed assist in private and fleet vehicles. Successful implementation relies on procuring systems that match needs, managing the potential for data to overwhelm and embedding monitoring and driver feedback within good management systems and strong safety leadership. This report provides recommendations for updating guidance for organisations considering implementing vehicle safety monitoring technologies (telematics).
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