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Relevant bibliographies by topics / Nearest neighbor analysis (Statistics)

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Academic literature on the topic 'Nearest neighbor analysis (Statistics)'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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Journal articles on the topic "Nearest neighbor analysis (Statistics)"

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Cao, Shuchao, Feiyang Sun, Mohcine Chraibi, and Rui Jiang. "Spatial analysis for crowds in multi-directional flows based on large-scale experiments." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 11 (November 1, 2021): 113407. http://dx.doi.org/10.1088/1742-5468/ac3660.

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Abstract In this paper, spatial analysis for the nearest neighbors is performed in the unidirectional, bidirectional and crossing flows. Based on the intended direction given in the experiment, different types of neighbors such as U-ped (neighbor with the same intended direction), B-ped (neighbor with the opposite intended direction) and C-ped (neighbor with the intersecting intended direction) are defined. The preferable positions of these neighbors during movement are investigated under various conditions. The spatial relation is quantified by calculating the distance and angle between the reference pedestrian and neighbors. The results indicate that the distribution of neighbors is closely related to the neighbor’s order, crowd density, neighbor type and flow type. Furthermore, the reasons that result in these distributions for different neighbors are explored. Finally neighbor distributions for different flows are compared and the implications of this research are discussed. The spatial analysis sheds new light on the study of pedestrian dynamics in a different perspective, which can help to develop and validate crowd models in the future.

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Ogita, Toshiro, Hidetomo Ichihashi, Akira Notsu, and Katsuhiro Honda. "Improvement of PCA-Based Approximate Nearest Neighbor Search Using Distance Statistics." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 4 (July 20, 2014): 658–64. http://dx.doi.org/10.20965/jaciii.2014.p0658.

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In many computer vision applications, nearest neighbor searching in high-dimensional spaces is often the most time consuming component and we have few algorithms for solving these high-dimensional nearest neighbor search problems that are faster than linear search. Approximately nearest neighbor search algorithms can play an important role in achieving significantly faster running times with relatively small errors. This paper considers the improvement of the PCA-tree nearest neighbor search algorithm [1] by employing nearest neighbor distance statistics. During the preprocessing phase of the PCA-tree nearest neighbor search algorithm, a data set is partitioned into clusters by successive use of Principal Component Analysis (PCA). The search performance is significantly improved if the data points are sorted by leaf node, and the threshold value is updated each time a smaller distance is found. The threshold is updated by the ε-approximate nearest neighbor approach together with the fixed-threshold approach. Performance can be further improved by the annulus bound approach. Moreover, nearest neighbor distance statistics is employed for further improving the efficiency of the search algorithm and the several experimental results are shown for demonstrating how its efficiency is improved.

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Biau, Gérard, Benoît Cadre, and Laurent Rouvière. "Statistical analysis of k-nearest neighbor collaborative recommendation." Annals of Statistics 38, no. 3 (June 2010): 1568–92. http://dx.doi.org/10.1214/09-aos759.

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Stute, Winfried, and Wenceslao González Manteiga. "Nearest neighbor smoothing in linear regression." Journal of Multivariate Analysis 34, no. 1 (July 1990): 61–74. http://dx.doi.org/10.1016/0047-259x(90)90061-l.

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Tran, L. T., and S. Yakowitz. "Nearest Neighbor Estimators for Random Fields." Journal of Multivariate Analysis 44, no. 1 (January 1993): 23–46. http://dx.doi.org/10.1006/jmva.1993.1002.

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Guerre, Emmanuel. "Design Adaptive Nearest Neighbor Regression Estimation." Journal of Multivariate Analysis 75, no. 2 (November 2000): 219–44. http://dx.doi.org/10.1006/jmva.2000.1901.

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Puchkin, Nikita, and Vladimir Spokoiny. "An adaptive multiclass nearest neighbor classifier." ESAIM: Probability and Statistics 24 (2020): 69–99. http://dx.doi.org/10.1051/ps/2019021.

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We consider a problem of multiclass classification, where the training sample S_n={(Xi,Yi)}ni=1 is generated from the model ℙ(Y = m|X = x) = ηm(x), 1 ≤ m ≤ M, and η1(x), …, ηM(x) are unknown α-Holder continuous functions. Given a test point X, our goal is to predict its label. A widely used k-nearest-neighbors classifier constructs estimates of η1(X), …, ηM(X) and uses a plug-in rule for the prediction. However, it requires a proper choice of the smoothing parameter k, which may become tricky in some situations. We fix several integers n1, …, nK, compute corresponding nk-nearest-neighbor estimates for each m and each nk and apply an aggregation procedure. We study an algorithm, which constructs a convex combination of these estimates such that the aggregated estimate behaves approximately as well as an oracle choice. We also provide a non-asymptotic analysis of the procedure, prove its adaptation to the unknown smoothness parameter α and to the margin and establish rates of convergence under mild assumptions.

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Cutler, Colleen D., and Donald A. Dawson. "Nearest-Neighbor Analysis of a Family of Fractal Distributions." Annals of Probability 18, no. 1 (January 1990): 256–71. http://dx.doi.org/10.1214/aop/1176990948.

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Dikta, Gerhard. "Bootstrap approximation of nearest neighbor regression function estimates." Journal of Multivariate Analysis 32, no. 2 (February 1990): 213–29. http://dx.doi.org/10.1016/0047-259x(90)90082-s.

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Burman, P., and D. Nolan. "Location-adaptive density estimation and nearest-neighbor distance." Journal of Multivariate Analysis 40, no. 1 (January 1992): 132–57. http://dx.doi.org/10.1016/0047-259x(92)90095-w.

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Dissertations / Theses on the topic "Nearest neighbor analysis (Statistics)"

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Shen, Qiong Mao. "Group nearest neighbor queries /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20SHEN.

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Hui, Michael Chun Kit. "Aggregate nearest neighbor queries /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?COMP%202004%20HUI.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2004.
Includes bibliographical references (leaves 91-95). Also available in electronic version. Access restricted to campus users.

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Xie, Xike, and 谢希科. "Evaluating nearest neighbor queries over uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B4784954X.

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Nearest Neighbor (NN in short) queries are important in emerging applications, such as wireless networks, location-based services, and data stream applications, where the data obtained are often imprecise. The imprecision or imperfection of the data sources is modeled by uncertain data in recent research works. Handling uncertainty is important because this issue affects the quality of query answers. Although queries on uncertain data are useful, evaluating the queries on them can be costly, in terms of I/O or computational efficiency. In this thesis, we study how to efficiently evaluate NN queries on uncertain data. Given a query point q and a set of uncertain objects O, the possible nearest neighbor query returns a set of candidates which have non-zero probabilities to be the query answer. It is also interesting to ask \which region has the same set of possible nearest neighbors", and \which region has one specific object as its possible nearest neighbor". To reveal the relationship between the query space and nearest neighbor answers, we propose the UV-diagram, where the query space is split into disjoint partitions, such that each partition is associated with a set of objects. If a query point is located inside the partition, its possible nearest neighbors could be directly retrieved. However, the number of such partitions is exponential and the construction effort can be expensive. To tackle this problem, we propose an alternative concept, called UV-cell, and efficient algorithms for constructing it. The UV-cell has an irregular shape, which incurs difficulties in storage, maintenance, and query evaluation. We design an index structure, called UV-index, which is an approximated version of the UV-diagram. Extensive experiments show that the UV-index could efficiently answer different variants of NN queries, such as Probabilistic Nearest Neighbor Queries, Continuous Probabilistic Nearest Neighbor Queries. Another problem studied in this thesis is the trajectory nearest neighbor query. Here the query point is restricted to a pre-known trajectory. In applications (e.g. monitoring potential threats along a flight/vessel's trajectory), it is useful to derive nearest neighbors for all points on the query trajectory. Simple solutions, such as sampling or approximating the locations of uncertain objects as points, fails to achieve a good query quality. To handle this problem, we design efficient algorithms and optimization methods for this query. Experiments show that our solution can efficiently and accurately answer this query. Our solution is also scalable to large datasets and long trajectories.
published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy

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Zhang, Jun. "Nearest neighbor queries in spatial and spatio-temporal databases /." View abstract or full-text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20ZHANG.

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Ram, Parikshit. "New paradigms for approximate nearest-neighbor search." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49112.

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Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new paradigms for nearest-neighbor search (along with new algorithms and theory in these paradigms) that make nearest-neighbor search more usable and accurate. First, I consider a new notion of search error, the rank error, for an approximate neighbor candidate. Rank error corresponds to the number of possible candidates which are better than the approximate neighbor candidate. I motivate this notion of error and present new efficient algorithms that return approximate neighbors with rank error no more than a user specified amount. Then I focus on approximate search in a scenario where the user does not specify the tolerable search error (error constraint); instead the user specifies the amount of time available for search (time constraint). After differentiating between these two scenarios, I present some simple algorithms for time constrained search with provable performance guarantees. I use this theory to motivate a new space-partitioning data structure, the max-margin tree, for improved search performance in the time constrained setting. Finally, I consider the scenario where we do not require our objects to have an explicit fixed-length representation (vector data). This allows us to search with a large class of objects which include images, documents, graphs, strings, time series and natural language. For nearest-neighbor search in this general setting, I present a provably fast novel exact search algorithm. I also discuss the empirical performance of all the presented algorithms on real data.

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Zhang, Peiwu, and 张培武. "Voronoi-based nearest neighbor search for multi-dimensional uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49618179.

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In Voronoi-based nearest neighbor search, the Voronoi cell of every point p in a database can be used to check whether p is the closest to some query point q. We extend the notion of Voronoi cells to support uncertain objects, whose attribute values are inexact. Particularly, we propose the Possible Voronoi cell (or PV-cell). A PV-cell of a multi-dimensional uncertain object o is a region R, such that for any point p ∈ R, o may be the nearest neighbor of p. If the PV-cells of all objects in a database S are known, they can be used to identify objects that have a chance to be the nearest neighbor of q. However, there is no efficient algorithm for computing an exact PV-cell. We hence study how to derive an axis-parallel hyper-rectangle (called the Uncertain Bounding Rectangle, or UBR) that tightly contains a PV-cell. We further develop the PV-index, a structure that stores UBRs, to evaluate probabilistic nearest neighbor queries over uncertain data. An advantage of the PV-index is that upon updates on S, it can be incrementally updated. Extensive experiments on both synthetic and real datasets are carried out to validate the performance of the PV-index.
published_or_final_version
Computer Science
Master
Master of Philosophy

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Wong, Wing Sing. "K-nearest-neighbor queries with non-spatial predicates on range attributes /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?COMP%202005%20WONGW.

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Yiu, Man-lung. "Advanced query processing on spatial networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36279365.

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Yiu, Man-lung, and 姚文龍. "Advanced query processing on spatial networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36279365.

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Dastile, Xolani Collen. "Improved tree species discrimination at leaf level with hyperspectral data combining binary classifiers." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1002807.

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The purpose of the present thesis is to show that hyperspectral data can be used for discrimination between different tree species. The data set used in this study contains the hyperspectral measurements of leaves of seven savannah tree species. The data is high-dimensional and shows large within-class variability combined with small between-class variability which makes discrimination between the classes challenging. We employ two classification methods: G-nearest neighbour and feed-forward neural networks. For both methods, direct 7-class prediction results in high misclassification rates. However, binary classification works better. We constructed binary classifiers for all possible binary classification problems and combine them with Error Correcting Output Codes. We show especially that the use of 1-nearest neighbour binary classifiers results in no improvement compared to a direct 1-nearest neighbour 7-class predictor. In contrast to this negative result, the use of neural networks binary classifiers improves accuracy by 10% compared to a direct neural networks 7-class predictor, and error rates become acceptable. This can be further improved by choosing only suitable binary classifiers for combination.

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Books on the topic "Nearest neighbor analysis (Statistics)"

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A spatial analysis of artifact distribution on a Boreal forest archaeological site. Edmonton, Alta: Alberta Culture, 1985.

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Weber, Roger. Similarity search in high dimensional vector spaces. Berlin: Aka, 2001.

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Kramer, Oliver. Dimensionality Reduction with Unsupervised Nearest Neighbors. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Die Verteilung von Steinartefakten in Grabungsflächen: Ein Modell zur Organisation alt- und mittelsteinzeitlicher Siedlungsplätze. Tübingen: Archaeologica Venatoria, 1985.

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Manolopoulos, Yannis, and Apostolos N. N. Papadopoulos. Nearest Neighbor Search : : A Database Perspective. Springer, 2010.

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V, Dasarathy Belur, ed. Nearest neighbor (NN) norms: Nn pattern classification techniques. Los Alamitos, Calif: IEEE Computer Society Press, 1991.

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Papadopoulos, Apostolos N., and Yannis Manolopoulos. Nearest Neighbor Search : : A Database Perspective. Springer London, Limited, 2006.

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Dasarathy, Belur V. Nearest Neighbor: Pattern Classification Techniques (Nn Norms : Nn Pattern Classification Techniques). Ieee Computer Society, 1990.

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Flores-Roux, Ernesto M. Estimation of the nearest neighbor distribution for spatial point processes. 1993.

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Dietterich, Thomas G., Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk, and Michael I. Jordan. Nearest-Neighbor Methods in Learning and Vision: Theory and Practice. MIT Press, 2006.

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Book chapters on the topic "Nearest neighbor analysis (Statistics)"

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Alam, Mahmudul, Md Monirul Islam, Md Rokunojjaman, Sharmin Akter, Md Belal Hossain, and Jia Uddin. "Electrocardiogram Signal Analysis Based on Statistical Approaches Using K-Nearest Neighbor." In Bangabandhu and Digital Bangladesh, 148–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17181-9_12.

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Smith, Wayne W. "Nearest neighbor analysis." In Encyclopedia of Tourism, 657–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-01384-8_380.

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Smith, Wayne W. "Nearest neighbor analysis, tourism." In Encyclopedia of Tourism, 1–2. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-01669-6_380-1.

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Trybus, E., and G. Trybus. "Computational Aspects of the Nearest Neighbor Statistics." In Computational Statistics, 95–100. Heidelberg: Physica-Verlag HD, 1992. http://dx.doi.org/10.1007/978-3-662-26811-7_15.

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Biau, Gérard, and Luc Devroye. "Order statistics and nearest neighbors." In Lectures on the Nearest Neighbor Method, 3–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25388-6_1.

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Biau, Gérard, and Luc Devroye. "Advanced properties of uniform order statistics." In Lectures on the Nearest Neighbor Method, 165–73. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25388-6_13.

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Zhu, Wei, and M. Victor Wickerhauser. "Wavelet Transforms by Nearest Neighbor Lifting." In Excursions in Harmonic Analysis, Volume 2, 173–92. Boston: Birkhäuser Boston, 2012. http://dx.doi.org/10.1007/978-0-8176-8379-5_9.

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Borgohain, Olimpia, Meghna Dasgupta, Piyush Kumar, and Gitimoni Talukdar. "Performance Analysis of Nearest Neighbor, K-Nearest Neighbor and Weighted K-Nearest Neighbor for the Classification of Alzheimer Disease." In Advances in Intelligent Systems and Computing, 295–304. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7394-1_28.

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Globig, Christoph, and Stefan Wess. "Symbolic Learning and Nearest-Neighbor Classification." In Studies in Classification, Data Analysis, and Knowledge Organization, 17–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-46808-7_2.

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Penrose, Mathew D., and J. E. Yukich. "Laws of Large Numbers and Nearest Neighbor Distances." In Advances in Directional and Linear Statistics, 189–99. Heidelberg: Physica-Verlag HD, 2010. http://dx.doi.org/10.1007/978-3-7908-2628-9_13.

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Conference papers on the topic "Nearest neighbor analysis (Statistics)"

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Chelidze, David. "Statistical Characterization of Nearest Neighbors to Reliably Estimate Minimum Embedding Dimension." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34746.

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False nearest neighbors (FNN) is one of the essential methods used in estimating the minimally sufficient embedding dimension in delay coordinate embedding of deterministic time series. Its use for stochastic and noisy deterministic time series is problematic and erroneously indicates a finite embedding dimension. Various modifications to the original method have been proposed to mitigate this problem, but those are still not reliable for noisy time series. Nearest neighbor statistics are studied for uncorrelated random time series and contrasted with the deterministic statistics. A new FNN metric is constructed and its performance is evaluated for deterministic, stochastic, and random time series. The results are also contrasted with surrogate data analysis and show that the new metric is robust to noise. It also clearly identifies random time series as not having a finite embedding dimension and provides information about the deterministic part of stochastic processes. The new metric can also be used for differentiating between chaotic and random time series.

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Fuadah, Y. N., A. W. Setiawan, and T. L. R. Mengko. "Performing high accuracy of the system for cataract detection using statistical texture analysis and K-Nearest Neighbor." In 2015 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2015. http://dx.doi.org/10.1109/isitia.2015.7219958.

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Vicentini, M., R. Lecourt, O. Rouzaud, V. Bodoc, and O. Simonin. "Experimental investigation of spray combustion regimes in aeroengine combustors." In Progress in Propulsion Physics – Volume 11. Les Ulis, France: EDP Sciences, 2019. http://dx.doi.org/10.1051/eucass/201911677.

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At ONERA Fauga-Mauzac center, a new air-breathing propulsion test setup, Prométhée-LACOM, has been recently developed. In this paper, both nonreacting and reacting two-phase flows (nonpremixed spray) were investigated. Under reactive conditions, simultaneous OH-PLIF (planar laser-induced fluorescence) and Mie scattering imaging were implemented in order to characterize the spray flame structure. The different behaviors observed in this study seem to support the existence of spray combustion regimes. Moreover, statistical analysis was performed on the spatial distribution of droplets and indicated that the center-to-center interdroplet distance (nearest neighbor) could be described by means of a perfectly random distribution.

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Wu, Ernest, Franco Stellari, Takashi Ando, Peilin Song, and Martin Frank. "Development of spatial nearest-neighbor analysis and Clustering/Gibbs statistical methodology for filament percolation in dielectric breakdown and forming process in ReRAM devices." In 2021 IEEE International Electron Devices Meeting (IEDM). IEEE, 2021. http://dx.doi.org/10.1109/iedm19574.2021.9720584.

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Zhang, Yu, Miguel Martínez-García, and Anthony Latimer. "Selecting Optimal Features for Cross-Fleet Analysis and Fault Diagnosis of Industrial Gas Turbines." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75286.

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This paper studies the behavior of Industrial Gas Turbines (IGTs) based on time-series measurements with low sampling rates. The aim is to find the most suitable set of statistical/time-domain features derived from the measurements, which can represent the characteristic behavior of the IGTs, or alternatively, which can discriminate between different engines or different states of an engine. For this end, a scheme of optimal feature selection process is proposed in the paper. For cross-fleet analysis, signals from a group of inter-duct thermocouples on IGT engines are studied. A feature matrix is formulated at each sliding time step, by calculating the statistical features of the sensor group, after the time-domain features of the individual sensor measurements are calculated. Feature matrix values from different engines are then clustered, and a modified Davies–Bouldin index is introduced to measure the quality of the clusters. Finally, grid search is run to find the optimal set of the features, which form the clusters with the most similarity, or otherwise, the most discrepancy across the IGT engines. The window size effect is also investigated. To demonstrate that the optimal feature selection process is also useful for fault diagnosis of IGTs, the proposed scheme is then applied on a group of different measurements on an IGT, i.e. from burner tip thermocouples, in a fault diagnostic scenario, which is subsequently validated using a k-nearest neighbor classification algorithm. The case studies have demonstrated that, ultimately, the developed techniques can be broadly applied to other groups of measurements for both cross-fleet analysis and fault diagnosis of IGTs.

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Okfalisa, Ikbal Gazalba, Mustakim, and Nurul Gayatri Indah Reza. "Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification." In 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). IEEE, 2017. http://dx.doi.org/10.1109/icitisee.2017.8285514.

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Khastavaneh, Hassan, Hossein Ebrahimpour-Komleh, and Amin Hanaee-Ahwaz. "Unknown aware k nearest neighbor classifier." In 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA). IEEE, 2017. http://dx.doi.org/10.1109/pria.2017.7983027.

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Rahman, Rifqi Aulya, Kusman Sadik, and Anwar Fitrianto. "Simulation for time series classification using feature covariance matrices with K-nearest neighbor." In INTERNATIONAL CONFERENCE ON STATISTICS AND DATA SCIENCE 2021. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0108204.

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Li, Jing, and Ming Cheng. "Analysis of the k-nearest neighbor classification." In 2013 International Conference of Information Science and Management Engineering. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/isme132482.

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Sadjadi, Seyed Omid, Jason W. Pelecanos, and Sriram Ganapathy. "Nearest neighbor discriminant analysis for language recognition." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178763.

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Reports on the topic "Nearest neighbor analysis (Statistics)"

1

Weingart, M., and S. Selvin. Nearest neighbor analysis in one dimension. Office of Scientific and Technical Information (OSTI), February 1995. http://dx.doi.org/10.2172/33152.

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Kiessling, L. L., and P. B. Dervan. Analysis of Nearest Neighbor Interactions in the Pyrimidine Triple Helix Motif by Affinity Cleaving. Fort Belvoir, VA: Defense Technical Information Center, June 1991. http://dx.doi.org/10.21236/ada237527.

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.

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