Академия Intel:

Разработка мультимедийных приложений с использованием библиотек OpenCV и IPP

: Литература по курсу
[+]
Опубликован: 02.09.2013 | Доступ: свободный | Студентов: 429 / 54 | Длительность: 19:27:00
Тема: Программирование
Специальности: Программист, Системный архитектор
Теги: IPP, Visual Studio, c++, intel, OpenCV
  • 1.
    Otsu N
    A threshold selection method from gray-level histogram
    IEEE Trans. Syst. Man Cybern. 9:62-66;1979
  • 2.
    Дж. Стокман, Л. Шапиро
    Компьютерное зрение
    Пер. с англ. — М.: БИНОМ. Лаборатория знаний, 2006.
  • 3.
    K, S. and Abe, Suzuki
    Topological Structural Analysis of Digitized Binary Images by Border Following
    CVGIP 30 1, pp 32-46,1985
  • 4.
    A.Canny
    Computational Approach to Edge Detection
    IEEE Trans. on Pattern Analysis and Machine Intelligence, 8(6), pp. 679-698,1986
  • 5.
    Shi and C. Tomasi
    Good Features to Track. Proceedings of the Vision and Pattern Recognition
    IEEE Conference on Computer
  • 6.
    Bishop C. M
    Pattern Recognition and Machine Learning.
    Springer, 2006
  • 7.
    Breiman L
    Random Forests // Machine Learning
    2001. V. 45, №. 1, P. 5–32
  • 8.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J
    Classification and Regression Trees
    Wadsworth & Brooks, 1984
  • 9.
    Support-Vector Networks // Machine Learning
    V. 20, № 3. P. 273–297
  • 10.
    Cortes C., Vapnik V. N
    A Decision-Theoretic Generalization of Online Learning and an Application to Boosting
    Computer and System Sciences. Vol.55, . – pp. 119-139. ,1995
  • 11.
    Friedman J. H
    Greedy Function Approximation: a Gradient Boosting Machine
    Technical report
  • 12.
    Freund Y., Schapire R
    Friedman J. H. Stochastic Gradient Boosting. Technical report. Dept. of Statistics, Stanford
  • 13.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference
    Dept. of Statistics, Stanford University, 1999
  • 14.
    Mitchell T
    Machine Learning
    McGraw Hill, 1997
  • 15.
    Samuel A
    Some Studies in Machine Learning Using the Game of Checkers
    IBM Journal. V. 3, University, 1999. № 3. P. 210–229
  • 16.
    Agrawal R., Srikant R
    Fast algorithms for mining association rules in large databases
    Proc. 20th Int. Conf. on Very Large Data Bases, VLDB'94, Morgan Kaufmann, 487-499. 1994
  • 17.
    Alkhalid A, Chikalov I, Hussain S, Moshkov M
    Extensions of dynamic programming as a new tool for decision tree optimization
    In: Ramanna S, Howlett RJ, Jain LC (eds.) Emerging Paradigms in Machine Learning, Springer (to appear). (2011)
  • 18.
    Alkhalid A, Chikalov I, Moshkov M
    On algorithm for building of optimal ?-decision trees
    In: Szczuka MS, Kryszkiewicz M, Ramanna S, Jensen R, Hu Q (eds.) RSCTC 2010. LNCS, vol. 6086:438-445, Springer, Heidelberg. (2010)
  • 19.
    Alonso D., Nieto M, Saldaro L.
    Robust Vehicle Detection through Multidimensional Classification for On Broad Video Based Systems
    IEEE. – 2007
  • 20.
    Amit Y
    2D Object Detection and Recognition: models, algorithms and networks
    The MIT Press, 2002. – 325p
  • 21.
    Andrews S., Hofmann T, Tsochantaridis I.
    Support vector machines for multiple-instance learning. Advances in Neural Information Processing Systems
    2002. P. 561-568
  • 22.
    Alpaslan F., Apolloni B., Ghosh A., Jain L.C., Patnaik S
    Machine Learning and Robot Perception
    Springer, 2005
  • 23.
    Arrospide J., Jaureguizar F, Nieto M., Salgado L.
    Robust vehicle detection through multidimensional classification for on board video based systems
    ICIP, 2008. – pp. 2008-2011
  • 24.
    Arndt R., Lhlein O, Paulus D., Schweiger R. Ritter W.
    Detection and tracking of multiple pedestrians in automotive applications
    Intelligent Vehicles Symposium. 2007. – pp. 13-18
  • 25.
    Arth C., Bischof H. Real-Time License Plate, Limberger F.
    Recognition on an Embedded DSP-Platform
    Computer Vision and Pattern Recognition, 2007
  • 26.
    Aldrich C, Aureta L.
    Empirical comparison of tree ensemble variable importance measures
    Chemometrics and Intelligent Laboratory Systems. Vol.105. No.2, 2011. – pp. 157-170
  • 27.
    Ballard D.H., Brown C.M.
    Computer Vision
    Prentice Hall Inc., 1982. – 539p
  • 28.
    Bauer E., Kohavi R.
    An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
    Mach. Learn. 1999. V. 36, №. 1-2., P. 105-139
  • 29.
    Bay H., Ess A., Gool L.V, Tuytelaars T.
    SURF: speed up robust features
    Computer Vision and Image Understanding (CVIU), Vol.110, No.3, 2008. – pp. 346-359
  • 30.
    Bertozzi M., Broggi A., Chapuis R., Chausse F, Fascioli A., Tibaldi A.
    Pedestrin localization and tracking system with Kalman filtering Intelligent Vehicles Symposium
    2004. - pp. 584-589
  • 31.
    Binelli E., Broggi A., Fascioli A., Ghidoni S., Graf T., Grisleri P., Meinecke M
    A modular tracking system for far infrared pedestrian recognition
    Intelligent Vehicles Symposium. 2005. – pp. 759-764
  • 32.
    Boryczka U, Kozak J
    New algorithms for generation decision trees - Ant-Miner and its modifications
    Foundations of Computational Intelligence 6:229-262. (2009)
  • 33.
    Bosch A., Munoz X, Zisserman A.
    Image classification using random forests and ferns
    In 11th International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007
  • 34.
    Bradski G., Kaehler A.
    Learning OpenCV Computer Vision with OpenCV Library
    O' Reilly Media Publishers, 2008. – 571p
  • 35.
    Breiman L.
    Random Forests
    Mach. Learn. 2001. V. 45, №. 1, P. 5-32
  • 36.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J.
    Classification and Regression Trees.
    Wadsworth & Brooks, 1984
  • 37.
    Calonder M., Fua P, Lepetit V., Strecha C.
    BRIEF: Binary Robust Independent Elementary Features
    11th European Conference on Computer Vision (ECCV), 2010
  • 38.
    Chikalov I
    Algorithm for constructing of decision trees with minimal number of nodes
    In: Ziarko W, Yao YY (eds.) RSCTC 2000, Revised Papers. LNCS, vol. 2005:139-143, Springer, Heidelberg. (2001)
  • 39.
    Chikalov I, Moshkov M, Zielosko B
    Online learning algorithm for ensemble of decision rules
    In: Kuznetsov SO, \'Sl ezak D, Hepting DH, Mirkin BG (eds.) RSFDGrC 2011. LNCS (LNAI), vol. 6743:310-313, Springer, Heidelberg. (2011)
  • 40.
    Comaniciu D., Meer P, Ramesh V.
    Real-time tracking of non-rigid objects using mean shift
    IEEE CVPR’2000, Vol.2, 2000. – pp. 142-149
  • 41.
    Dalal N., Triggs B
    Histograms of oriented gradients for human detection
    Computer Vision and Pattern Recognition (CVPR’05), 2005
  • 42.
    Bray C., Csurka G, Dance C., Fan L., Willamowski J.
    Visual categorization with bags of keypoints
    In: ECCV International Workshop on Statistical Learning in Computer Vision, 2004
  • 43.
    Berg A., Deng J., Fei-Fei L.
    Hierarchical Semantic Indexing for Large Scale Image Retrieval
    IEEE Computer Vision and Pattern Recognition (CVPR) – 2011
  • 44.
    Berg A., Deng J., Fei-Fei L., Li K.
    What does classifying more than 10,000 image categories tell us
    Proceedings of the 12th European Conference of Computer Vision (ECCV) – 2010
  • 45.
    Deng J., Dong W., Fei-Fei L, Li K., Li L., Socher R.
    ImageNet: A large-scale hierarchical image database
    In CVPR09. – 2009
  • 46.
    Belongie S., Dollar P., Perona P
    The fastest pedestrian detector in the west
    Machine Vision. 2010
  • 47.
    Dollar P., Perona P, Schiele B., Wojek C.
    Pedestrian Detection: An Evaluation of the State of the Art
    Pattern Analysis and Machine Intelligence (PAMI’11). 2011
  • 48.
    Druzhkov P. N., Eruhimov V. L., Kozinov E. A., Kustikova V. D., Meyerov I. B., Polovinkin A. N., Zolotykh N. Yu
    On some new object detection features in OpenCV Library
    Pattern Recognition and Image Analysis. 2011. V. 21, № 2. P. 377–379
  • 49.
    Duda R.O., Hart P.E., Stork D.G
    Pattern classification (2nd edition).
    Wiley, 2001
  • 50.
    Enzweiler M., Gavrila D. M
    Monocular Pedestrian Detection: Survey and Experiments
    IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, V. 31, N. 12, P. 2179-2195
  • 51.
    Ewens W.J., Grant G
    Grant Statistical Methods in Bioinformatics: An Introduction.
    Springer, 2005
  • 52.
    Bruns E., Exner D., Grundhofer A, Kurz D.
    Fast and robust CAMShift tracking
    IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2010. – pp. 9-16
  • 53.
    Fellbaum C
    WordNet: An Electronic Lexical Database.
    MIT Press, 1998
  • 54.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D
    Object Detection with Discriminatively Trained Part Based Models
    IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. Vol.32, No.9. – pp. 1627–1645
  • 55.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D
    Cascade object detection with deformable path model
    Computer Vision and Pattern Recognition (CVPR'10). 2010
  • 56.
    Frank A
    Asuncion A
    (2010) UCI Machine Learning Repository
  • 57.
    Franke U., Joss A
    Real-time stereo vision for urban traffic scene understanding
    Intelligent Vehicles Symposium. 2000. – pp. 273-278
  • 58.
    Adelson E, Freeman W.
    The design and use of steerable filters
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.13, No.9, 1991. – pp. 891-906
  • 59.
    Freund Y., Schapire R
    A decision-theoretic generalization of online learning and an application to boosting
    Computer and System Sciences. Vol.55, 1997. – pp. 119-139
  • 60.
    Friedman J
    Greedy function approximation: the gradient boosting machine
    Annals of Statistics. 2001. V. 29, N. 5. P. 1189-1232
  • 61.
    Friedman J. H
    Greedy Function Approximation: a Gradient Boosting Machine.
    Technical report. Dept. of Statistics, Stanford University, 1999
  • 62.
    Friedman J. H
    Stochastic Gradient Boosting.
    Technical report. Dept. of Statistics, Stanford University, 1999
  • 63.
    Friedman J.H., Popescu B.E
    Importance Sampled Learning Ensembles
    Technical report. Dept. of Statistics, Stanford University
  • 64.
    Gavrila D. M., Giebel J., Munder S
    Vision-based pedestrian detection: the protector system
    Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy. – 2004. – pp. 13-18
  • 65.
    Gavrila D.M
    Pedestrian detection from a moving vehicle
    Computer Vision. Vol.2. 2000. – pp. 37-49
  • 66.
    Geronimo D. A
    Pyramid match kernels: Discriminative classification with sets of image features
    In Proc. ICCV, 2005
  • 67.
    Darrell T, Grauman K.
    Pyramid match kernels: Discriminative classification with sets of image features
    In Proc. ICCV, 2005
  • 68.
    Grubb G., Nilsson L., Rilbe M, Zelinsky A.
    3D vision sensing for improved pedestrian safety Intelligent Vehicles Symposium.
    2004. – pp. 19-24
  • 69.
    Grunwald PD
    The Minimum Description Length Principle. Foreword by Jorma Rissanen. Adaptive Computation and Machine Learning.
    MIT Press
  • 70.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    Springer, 2001
  • 71.
    Armingol J.M., Collado J.M., Escalera A, Hilario C.
    Pyramidal Image Analysis for Vehicle Detection
    Proceedings to Intelligent Vehicles Symposium, 2005. – pp. 88-93
  • 72.
    Hama H, Hirose K., Torio T.
    Robust Extraction of Wheel Region for Vehicle Position Estimation using a Circular Fisheye Camera
    International Journal of Computer Science and Network Security, Vol.9
  • 73.
    Horn B., Schunk B
    Determing Optical Flow
    MIT Artificial Intelligence Laboratory, №572. – 1980
  • 74.
    Jensen R, Shen Q
    Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
    IEEE Transactions on Knowledge and Data Engineering 16:1457-1471
  • 75.
    Favaro P., Jin H., Soatto S
    Real-time tracking and outlier rejection with changes in illumination
    ICCV’01, Vol.1, 2001. – pp. 684-689
  • 76.
    Jurafsky D., Martin J.H
    Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Second Edition
    Prentice Hall, 2008
  • 77.
    Kalal Z., Matas J, Mikolajczyk K.
    Forward-backward error: automatic detection of tracking failures
    ICPR’10, 2010. – pp. 2756-2759
  • 78.
    Birchfield S. T, Kanhere N. K., Pundlik S. J.
    Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
    Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, California, June 2005. – 2005
  • 79.
    Ke Y., Sukthankar R
    PCA-SIFT: A more distinctive representation for local image descriptors
    Computer Vision and Pattern Recognition (CVPR’04), Vol.2, 2004. – pp. 506-513
  • 80.
    Kilian Q. Weinberger, Lawrence K
    Saul Distance Metric Learning for Large Margin Nearest Neighbor Classification
    JMLR. 2009 V. 10 P. 207-244
  • 81.
    Kim H.J, Kim J.B., Lee C.W., Lee K.M., Yun T.S.
    Wavelet-based Vehicle Tracking for Automatic Traffic Surveillance
    IEEE Catalogue No.01CH37239. – 2001
  • 82.
    Lazebnik S., Ponce J, Schmid C.
    Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
    In: CVPR, 2006
  • 83.
    Chiu T.H., Hung Y.P, Lee P.H., Lin Y.L.
    Real-time pedestrian and vehicle detection in video using 3D cues
    ICME’09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo. – 2009. – pp. 614-617
  • 84.
    Cornelis K., Cornelis N., Leibe B., Van Gool L
    Dynamic 3D scene analysis from a moving vehicle
    Computer Vision and Pattern Recognition (CVPR’07). 2007. – pp. 1-8
  • 85.
    Leibe B., Leonardis A., Schiele B
    Robust Object Detection with Interleaved Object Categoization and Segmentation
    Springler Science + Business Media, LLC. – 2007
  • 86.
    Leong C.W., Mihalcea R
    Measuring the semantic relatedness between words and images
    International Conference on Computational Semantics (IWCS) – 2011.
  • 87.
    Abbass HA, Liu B, McKay B
    Classification rule discovery with ant colony optimization
    In: Proc. IEEE/WIC Int. Conf. on Intelligent Agent Technology, IAT 2003, IEEE Computer Society, Washington, DC, 83-88
  • 88.
    Lowe D
    Distinctive image features from scale-invariant keypoints
    IJCV 60, P. 91–110, 2004
  • 89.
    Kanade T., Lucas B.D.
    An iterative image registration technique with an application to stereo vision
    IJCAI’81, Vol.2, 1981. – pp. 674-679
  • 90.
    Chum O., Matas J., PajdlaT, Urban M.
    Robust wide baseline stereo from maximally stable extremal regions
    British Machine Learning Conference, 2002. – pp. 384-393
  • 91.
    Michalski SR, Pietrzykowski J
    iAQ: A program that discovers rules, AAAI-07 AI Video Competition
    In: 22nd AAAI Conference on Artificial Intelligence, Vancouver, British Columbia, Canada. (2007)
  • 92.
    Mikolajczyk K., Schmid C. A
    Performance Evaluation of Local Descriptors
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.10, 2005. – pp. 1615-1630
  • 93.
    Mikolajczyk K., Schmid C
    Scale and affine invariant interest point detectors
    International Journal of Computer Vision, 60(1), 2004. – pp. 63-86
  • 94.
    Mitchell T
    Machine Learning.
    McGraw-Hill Science/Engineering/Math, 1997
  • 95.
    Chikalov I, Moshkov M
    On algorithm for constructing of decision trees with minimal depth. Fundamenta Informaticae
    41:295-299.(2000)
  • 96.
    Moshkov M, Piliszczuk M, Zielosko B
    Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
    Studies in Computational Intelligence, vol. 145. Springer, Heidelberg. (2008)
  • 97.
    Moshkov M, Zielosko B
    Combinatorial Machine Learning: A Rough Set Approach.
    Studies in Computational Intelligence, vol. 360. Springer, Heidelberg.(2011)
  • 98.
    Gavrila D.M, Munder S.
    An experimental study on pedestrian classification
    IEEE Pattern Analysis and Machine Intelligence (PAMI’06).Vol.28, No.11, 2006. pp. 1863-1868
  • 99.
    A., A.S, J.J., Lim, Myung Jin Choi, Torralba, Willsky
    Exploiting Hierarchical Contex on a large database of object categories
    IEEE Computer Vision and Pattern Recognition (CVPR’10). 2010. – pp. 129-136
  • 100.
    Neubeck A., Van Gool L
    Efficient Non-Maximum Supression
    International Conference on Pattern Recognition, 2006. – pp. 850-855
  • 101.
    Nguyen HS
    Approximate Boolean reasoning: foundations and applications in data mining
    In: Peters JF, Skowron A (eds.), Transactions on Rough Sets V. LNCS, vol. 4100:334-506, Springer, Heidelberg. (2006)
  • 102.
    Basu S., Bayardo R. J, Herbach J. S., Panda B.
    PLANET: Massively parallel learning of tree ensembles with MapReduce.
    Proceedings of the 35th International Conference on Very Large Data Base (VLDB). 2009. P. 1426-1437
  • 103.
    Huang Q., Jiang S, Pang J.
    Multiple instance boost using graph embedding based decision stump for pedestrian detection
    Computer Vision. Vol.4, 2008. - pp. 541-552
  • 104.
    Papageorgiou C., Poggio T
    A trainable system for object detection
    Computer Vision. Vol.38. No.1, 2000. – pp. 15-33
  • 105.
    Pawlak Z
    Rough Sets – Theoretical Aspects of Reasoning about Data
    Kluwer Academic Publishers
  • 106.
    Pawlak Z, Skowron A
    Rough sets and Boolean reasoning
    Information Sciences 177: 41-73. (2007)
  • 107.
    Choudhury T. Face, Pentland A.
    Recognition for Smart Environments
    IEEE Computer Vision. – 2000. – pp. 50-55
  • 108.
    Quinlan J.R
    Induction of decision trees
    Machine Learning 1, 1986. – pp. 81-106
  • 109.
    Quinlan JR
    C4.5: Programs for Machine Learning,
    Morgan Kaufmann Publishers, San Mateo. (1993)
  • 110.
    Rissanen J
    Modeling by shortest data description.
    Automatica 14:465-471. (1978)
  • 111.
    Drummond T, Rosten E.
    Machine Learning for high-speed corner detection
    9th European Conference on Computer Vision (ECCV 2006), 2006. – pp. 430-443
  • 112.
    Mori G, Sabzmeydani P.
    Detecting pedestrians by learning shapelet features
    Computer Vision and Pattern Recognition (CVPR’07). 2007
  • 113.
    Schapire R
    The boosting approach to machine learning
    An overview MSRI workshop on Nonlinear Estimation and Classification. Springer, 2002
  • 114.
    Shi J., Tomasi C
    Good features to track
    IEEE, 1994. – pp. 593-600
  • 115.
    Blake A., Cipolla R, Shotton J.
    Contour-based Learning for Object Detection
    10th IEEE International Conference on Computer Vision (ICCV’05). 2005. Vol.1. – P. 503-510
  • 116.
    Skowron A
    Rough sets in KDD
    In: Shi Z, Faltings B, Musem M. (eds.) 16th World Computer Congress. Proc. Conf. Intelligent Information Processing, 1-17. Publishing House of Electronic Industry, Beijing. (2000)
  • 117.
    Rauszer C, Skowron A
    The discernibility matrices and functions in information systems
    In: Slowinski R (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, Kluwer Academic Publishers, Dordrecht, 331-362. (1992)
  • 118.
    Slezak D, Wroblewski J
    Order-based genetic algorithms for the search of approximate entropy reducts.
    In: Wang G, Liu Q, Yao Y, Skowron A (eds.) Proc. 9th Int. Conf. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2003. LNAI, vol. 2639:308-311, Springer, Heidelberg. (2003)
  • 119.
    Boyle R, Hlavac V., Sonka M.
    Image Processing, Analysis and Machine Vision.
    Thomson, 2008. – 866p
  • 120.
    Szeliski R
    Computer Vision: Algorithms and Applications.
    Springler, 2010. – 979p
  • 121.
    Aggarwal J.K, Tamersoy B.
    Robust Vehicle Detection for Tracking in Highway Surveillance Videos using unsupervised Learning
    Advanced Video and Signal Based Surveillance (AVSS '09), 2009. – pp.529-534
  • 122.
    Fua P, Lepetit V., Tola E.
    A Fast Local Descriptor for Dense Matching
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR’08), 2008. – pp. 1-8
  • 123.
    Freeman W.T., Murphy K.P., Rubin M.A, Torralba A.
    Contex-based Vision System for Place and Object Recognition
    9th IEEE International Conference on Computer Vision (ICCV’03). 2003. Vol.1. – pp. 273-283
  • 124.
    Mikolajczyk K, Tuytelaars T.
    Local Invariant Feature Detectors: A Survey
    Foundation and Trends in Computer Vision, Vol.3, No. 3, 2007. – pp. 177-280
  • 125.
    Agrawal K., Paykin J, Tyree S., Weinberger K. Q.
    Parallel boosted regression trees for web search ranking.
    Proceedings of the 20th international conference on World wide web. 2011. P. 387-396
  • 126.
    Chandler D.M, Vasu L.
    Vehicle Tracking Using a Human-Vision-Based Model of Visual Similarity
    IEEE. – 2010
  • 127.
    Jones M., Snow D, Viola P.
    Detecting pedestrians using patterns of motion and appearance
    Computer Vision. Vol.63. No.2, 2005. – pp. 153-161
  • 128.
    Jones M.J, Viola P.
    Rapid object detection using a boosted cascade of simple features
    In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition. – 2001
  • 129.
    Jones M.J, Viola P.
    Robust Real-Time Face Detection
    international Journal of Computer Vision 57(2). – 2004. – pp. 137-154
  • 130.
    Jones M.J., Snow D, Viola P.
    Detecting pedestrians using patterns of motion and appearance
    In: Proceedings of the 9th International Conference on Computer Vision (ICCV), Vol. 1 – 2003. – pp. 734-741
  • 131.
    Majer N., Schiele B, Schindler K., Walk S.
    New features and insights for pedestrian detection
    Computer Vision and Pattern Recognition (CVPR'10). 2010
  • 132.
    Wallace CS
    Statistical and Inductive Inference by Minimum Message Length.
    Information Science and Statistics. Springer, New York. (2005)
  • 133.
    Schiele B, Wojek C.
    A performance evaluation of single and multi-feature people detection
    DAGM-Symposium. 2008. – pp.82-91
  • 134.
    Schiele B, Walk S., Wojek C.
    Multi-cue onboard pedestrian detection
    Computer Vision and Pattern Recognition (CVPR'09). 2009. – pp. 794 - 801
  • 135.
    Wroblewski J
    Finding minimal reducts using genetic algorithm.
    In: Proc. 2nd Annual Join Conf. on Information Sciences, Wrightsville Beach, NC, 186-189. (1995)
  • 136.
    Nevatia R, Wu B.
    Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors
    Computer Vision. Vol.75. No.2, 2007. – pp. 247-266
  • 137.
    Frank E, Xu X.
    Logistic regression and boosting for labeled bags of instances
    Knowledge Discovery and Data Mining (KDDM’04), 2004
  • 138.
    Grossman R., Yike G.
    High Performance Data Mining: Scaling Algorithms, Applications and Systems
    Kluwer Academic Publishers, 1999
  • 139.
    Nevatia R, Wu B., Zhang L.
    Pedestrian detection in infrared images based on local shape features
    Computer Vision and Pattern Recognition (CVPR’07). 2007
  • 140.
    Bebis G., Miller R, Zehang Sun
    On-road vehicle detection using Gabor filters and support vector machines
    Digital Signal Processing,Vol.2, 2002.- pp. 1019-1022
  • 141.
    Avidan S., Cheng K, Yeh M., Zhu Q.
    Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
    Computer Vision and Pattern Recognition (CVPR’06). Vol.2, 2006. – pp. 1491-1498
  • 142.
    Chikalov I, Moshkov M, Zielosko B
    Optimization of decision rules based on methods of dynamic programming
    Vestnik of Lobachevsky State University of Nizhni Novgorod 6:195-200 (in Russian) (2010)
  • 143.
    Вапник В.Н., Червоненкис А.Я
    Теория распознавания образов. Статистические проблемы обучения.
    М.: Наука, 1974
  • 144.
    Дружков П. Н., Золотых Н. Ю., Половинкин А.Н
    Параллельная реализация алгоритма предсказания с помощью модели градиентного бустинга деревьев решений
    Вестник Южно-Уральского государственного университета. Серия: Математическое моделирование и программирование. 2011. No. 37 (254). С. 82-89
  • 145.
    Дружков П.Н., Золотых Н.Ю., Половинкин А.Н
    Программная реализация алгоритма градиентного бустинга деревьев решений.
    Вестник ННГУ, вып. 1, 2011. С. 193-200
  • 146.
    Котов Ю.Б
    Новые математические подходы к задачам медицинской диагностики.
    М.: УРСС, 2004
  • 147.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.: Изд. д. Вильямс, 2004. – 465с
  • 148.
    Чубукова И. А
    Data Mining: учебное пособие
    М.: Интернет-университет информационных технологий: БИНОМ: Лаборатория знаний, 2006
  • 149.
    A, and Zisserman, Fergus, P., Perona, R.
    A sparse object category model for efficient learning and exhaustive recognition
    In IEEE Computer Society Conference onComputer Vision and Pattern Recognition (CVPR’2005), pp. 380–387, San Diego, CA (2005)
  • 150.
    A, Fergus, P. and Zisserman, R. and Perona
    Object Class Recognition by Unsupervised Scale-Invariant Learning
    In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2003, V.2, pp. 264-271
  • 151.
    and Huttenlocher, Crandall, D, D., Felzenszwalb, P.
    Spatial priors for part-based recognition using statistical models.
    In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’2005), pp. 10–17, San Diego, CA. (2005)
  • 152.
    D.P, Felzenszwalb, P. F. and Huttenlocher
    Pictorial structures for object recognition.
    International Journal of Computer Vision, 61(1):55–79. (2005)
  • 153.
    and Bray, C, C. R., Csurka, Dance, Fan, G., J., L., Willamowski
    Visual categorization with bags of keypoints
    In ECCV International Workshop on Statistical Learning in Computer Vision, Prague (2004)
  • 154.
    B, Bouchard, G. and Triggs
    Hierarchical part-based visual object categorization
    In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’2005), pp. 709–714, San Diego, CA.(2005)
  • 155.
    Carneiro, D, G. and Lowe
    Sparse flexible models of local features
    In Ninth European Conference on Computer Vision (ECCV 2006), pp. 29–43 (2006)
  • 156.
    Bishop, C. M
    Pattern Recognition and Machine Learning
    Springer, New York, NY
  • 157.
    Fischler, M. A. and Elschlager, R. A
    The representation and matching of pictorial structures
    IEEE Transactions on Computers, 22(1):67–92
  • 158.
    J. Winn and A. Criminisi, S. Savarese
    Discriminative Object Class Models of Appearance and Shape by Correlatons
    In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), New York, 2006
  • 159.
    Fei-Fei, J.C., L, Niebles
    A hierarchical model of shape and appearance for human action classification
    In: Proc. CVPR (2007)
  • 160.
    A summary of vehicle detection and Surveillance Technologies used in Intelligent Transportation Systems
  • 161.
    Application-Specific Integrated Circuit
  • 162.
    Cai B., Tan F., Lu Y., Zhang D. Knowledge Template Based Multi-perspective Car Recognition Algorithm
  • 163.
    Formal Description of Moravec detector
  • 164.
    Harris/Plessey Operator
  • 165.
    International Road Dynamics Inc.
  • 166.
    Marsh Products, Inc.
  • 167.
    RAI Products
  • 168.
    Traffic counting methods
  • 169.
    Vaxtor Systems
  • 170.
    ViNotion Inc.
  • 171.
    Архив новостей по рубрике Digital Signal Processing
  • 172.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение.
    М.: Бином. Лаборатория знаний, 2006. 752с
  • 173.
    Chris Stauffer
    W.E.L. Grimson Adaptive background mixture models for real-time tracking
    In Proceedings of the Conference on Computer Vision and Pattern Recognition, 1998. V. 2
  • 174.
    B. Vachon, F. El Baf, T. Bouwmans
    Background Modeling using Mixture of Gaussians for Foreground Detection -
    A Survey Recent Patents on Computer Science 1, 3. 2008. P. 219-237
  • 175.
    A. P. Dempster, D. B. Rubin, N. M. Laird
    Maximum Likelihood from Incomplete Data via the EM Algorithm
    Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1. (1977), pp. 1-38
  • 176.
    Szeliski R
    Computer Vision: Algorithms and Applications.
    Springler, 2010. 979p
  • 177.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход.
    М.: Изд. д. Вильямс, 2004. 465с
  • 178.
    D.Nister
    Preemptive RANSAC for live structure and motion estimation
    In the Proceedings of the IEEE International Conference on Computer Vision 2003 (ICCV’03). 2003. P.199-206
  • 179.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV
    Library. O' Reilly Media Publishers, 2008. 571p
  • 180.
    Boyle R, Hlavac V., Sonka M.
    Image Processing, Analysis and Machine
    Vision. Thomson. 2008. 866p
  • 181.
    Leibe B., Leonardis A., Schiele B. Robust Object
    Detection with Interleaved Object Categoization and Segmentation.
    Springler Science + Business Media, LLC, 2007
  • 182.
    Chiu T.H., Hung Y.P, Lee P.H., Lin Y.L.
    Real-time pedestrian and vehicle detection in video using 3D cues
    In Proceedings of the 2009 IEEE international conference on Multimedia and Expo (ICME’09). 2009. P. 614-617
  • 183.
    M. Van Droogenbroeck ViBe, O. Barnich
    Auniversal background subtraction algorithm for video sequences
    IEEE Transactions on Image Processing. 20(6).2011. P.1709-1724
  • 184.
    Horn B., Schunk B
    Determing Optical Flow
    MIT Artificial Intelligence Laboratory. 1980. №572
  • 185.
    Adelson E. H, J. Y. A., Wang
    Representing moving images with layers
    IEEE Transactions on Image Processing. 1994. 3(5). P. 625–638
  • 186.
    Kumar M.P., Torr P.H.S., Zisserman A
    Learning Layered Motion Segmentations of Video
    In International Journal of Computer Vision (IJCV). 2008. V.76, №3, P. 311-319
  • 187.
    Javed O., Shah M, Yilmaz A.
    Object tracking: A survey
    ACM Computing Surveys. 2006. V. 38. № 4, Article 13
  • 188.
    Backer E, Reinders M., Veenman C.
    Resolving motion correspondence for densely moving points.
    IEEE Trans. Pattern Analysis Machine Intelligence. 2001. V.23. № 1. P. 54-72
  • 189.
    Dezfoulian MirHossein, Fathi M., Salarpour A.
    Vehicle tracking using Kalman filter and features
    Signal & Image Processing: An International Journal (SIPIJ). 2011. V.2, №2
  • 190.
    Baojun Zh., Dan S., Linbo T. A Tracking
    Algorithm Based on SIFT and Kalman Filter
    In Proceedings The 2nd International Conference on Computer Application and System Modeling. 2012. – P.1563-1566
  • 191.
    Ning Li
    Corner feature based object tracking using Adaptive Kalman Filter
    In Proceedings of the 9th International Conference on Signal Processing (ICSP 2008). 2008. P. 1432-1435
  • 192.
    Blake A, Isard M.
    Condensation - conditional density propagation for visual tracking
    Int. J. Comput. Vision. 1998. V.29. № 1. P. 5-28
  • 193.
    Bergman N., Forssell U., Gunnarsson F., Gustafsson F., Jonas Jansson, Karlsson R., Nordlund P.J
    Particle Filters for Positioning, Navigation and Tracking
    IEEE Transactions on Signal Processing. 2002. V.2. Issue 2. P. 425-437
  • 194.
    Comaniciu D., Meer P, Ramesh V.
    Real-time tracking of non-rigid objects using mean shift
    In Proceedings of the CVPR’00. 2000. V.2, P. 142-149
  • 195.
    Bruns E., Exner D., Grundhofer A, Kurz D.
    Fast and robust CAMShift tracking
    In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2010. P. 9-16
  • 196.
    Horn B.K.P., Schunck B.G
    Determining optical flow
    MIT, Artificial Intelligence Laboratory. 1980
  • 197.
    Shi J., Tomasi C.
    Good features to track
    IEEE. 1994. P. 593-600
  • 198.
    Favaro P., Jin H., Soatto S
    Real-time tracking and outlier rejection with changes in illumination
    In Proceedings of the ICCV’01. 2001. V.1. P. 684
  • 199.
    Barron J. L., Beauchemin S. S, Fleet D. J.
    Performance of optical flow techniques
    International Journal of Computer Vision. 1994. V.12. № 1. P.43-77
  • 200.
    Freidman J, Hastie T., Tibshirani R.
    The elements of statistical learning
    Data mining, inference and prediction. 2001. 745p
  • 201.
    Г. Нуссбаумер
    Быстрое преобразование Фурье и алгоритмы вычисления сверток
    М.: «Радио и связь». 1985. 247с
  • 202.
    Дж. Голуб, Ч. Ван Лоун
    Матричные вычисления
    М.: Изд-во «Мир». 1999. 548с
  • 203.
    EM-алгоритм
  • 204.
    Particle Filter Object Tracking
  • 205.
    Gauglitz S., Holerer T., Turk M
    Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking
  • 206.
    Blake A, Isard M.
    Condensation - conditional density propagation for visual tracking
  • 207.
    Hartley R., Zisserman A
    Multiple View Geometry in Computer
    Cambridge University Press, 2004
  • 208.
  • 209.
    современная книга по компьютерному зрению
  • 210.
    «Официальная» книга про OpenCV
  • 211.
  • 212.
    Официальный» учебник по Питону на русском
  • 213.
    Numpy
  • 214.
    Intel® Integrated Performance Primitives (Intel® IPP)
  • 215.
    Intel® Integrated Performance Primitives for Linux* OS User's Guide
  • 216.
    Intel® Integrated Performance Primitives Reference Manual - Volume 1: Signal Processing
  • 217.
    Intel® Integrated Performance Primitives Reference Manual - Volume 2: Image and Video Processing
  • 218.
    Bradski G., Kaehler A
    Learning OpenCV.
    O’Reilly, 2008. – 571p
  • 219.
    Официальная страница библиотеки OpenCV
  • 220.
    Официальная страница EmguCV
  • 221.
    Официальная страница CMake
  • 222.
    Сервер для загрузки последней версии исходных кодов библиотеки
  • 223.
    Сервер для загрузки последней версии исходных кодов библиотеки OpenCV и дополнительных файлов (например, натренированные модели, файлы входных данных и результатов для автоматическихт тестов)
  • 224.
    Страница загрузки клиента Subversion
  • 225.
    Руководство по сборке и установке библиотеки OpenCV
  • 226.
    Официальная страница проекта Jasper
  • 227.
    Официальная страница проекта libjpeg
  • 228.
    Официальная страница проекта libpng
  • 229.
    Официальная страница проекта libtiff
  • 230.
    Официальная страница библиотеки zlib
  • 231.
    Описание базовых структур данных библиотеки OpenCV
  • 232.
    Описание функций преобразования изображений
  • 233.
    Обнаружение и локализация лица на изображении
  • 234.
    Описание функций отрисовки примитивов на изображении
  • 235.
    Jones M.J. Robust, Viola P.
    Real-Time Face Detection
    international Journal of Computer Vision 57(2). – 2004. – pp. 137-154
  • 236.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D
    Object Detection with Discriminatively Trained Part Based Models
    IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. Vol.32, No.9. – pp. 1627–1645
  • 237.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.: Изд. д. Вильямс, 2004. – 465с. 21. Suzuki S., Abe K. Topological Structural Analysis of Digitized Binary Images by Border Following CVGIP 30 1. – 1985. – pp. 32-46
  • 238.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение
    М.: Бином. Лаборатория знаний, 2006. – 752с
  • 239.
    Chin, Cho-Huak, Roland T, Teh
    On the detection of dominant points on digital curves
    IEEE Transactions on Pattern Analysis and Machine Intelligence. – Vol. II No. 8. – 1989. – pp. 859-872
  • 240.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV Library
    O' Reilly Media Publishers, 2008. 571p
  • 241.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение.
    М.: Бином. Лаборатория знаний, 2006. 752с
  • 242.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.:Изд. д. Вильямс, 2004. 465с
  • 243.
    Canny J
    A computational approach to edge detection
    IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. PAMI-8. No.6. 1986. P.679-698
  • 244.
    Официальная страница библиотеки OpenCV
  • 245.
    Лекция Интернет Университета «Фильтрация изображений»
  • 246.
    Документация модуля imgproc библиотеки OpenCV
  • 247.
    Схема работы детектора Канни, представленная в документации OpenCV
  • 248.
    Canny Edge Detection
  • 249.
    Документация OpenCV с описанием базовых структур данных
  • 250.
    Страница загрузки Qt libraries
  • 251.
    Описание функций библиотеки OpenCV, реализованных на базе Qt
  • 252.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning
    Data Mining, Inference, and Prediction. Springer, 2001
  • 253.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J
    Classification and Regression Trees
    Wadsworth & Brooks, 1984
  • 254.
    Druzhkov P.N., Eruhimov V.L., Kozinov E.A., Kustikova V.D., Meyerov I.B., Polovinkin A.N, Zolotykh N.Yu.
    New object detection features in the OpenCV Library
    Pattern Recognition and Image Analysis. 2011. V. 21, № 3. P. 384–386
  • 255.
    Ke Y., Lindeberg T, Sukthankar R
    Feature detection with automatic scale selection
    International Journal of Computer Vision. 1998. V.30. Issue
  • 256.
    Breiman L
    Random Forests
    Machine Learning 2001. V. 45, № 1. P. 5–32
  • 257.
    Arthur D., Vassilvitskii S
    k-means++: the advantages of careful seeding
    Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. 2007. P. 1027–1035
  • 258.
    Официальная страница библиотеки OpenCV
    М.: Издательство Московского университета, 2010. –272 с., ил. – (Серия «Супер-компьютерное образование»)
  • 259.
    Документация модуля ml библиотеки OpenCV
    IntelPress, 2004
  • 260.
    Agrawal M., Blas M, Konolige K.
    Censure: Center surround extremas for realtime feature detection and matching
    In European Conference on Computer Vision (ECCV 2008), pp. 102–115
  • 261.
    Bay H., Ess A., Gool L.V, Tuytelaars T.
    SURF: speed up robust features
    Computer Vision and Image Understanding (CVIU). 2008. V.110, № 3. P. 346-359
  • 262.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV
    Library. O' Reilly Media Publishers, 2008. 571p
  • 263.
    Calonder M., Fua P., Lepetit V., Strecha C.
    BRIEF: Binary Robust Independent Elementary Features
    In Proceedings of the 11th European Conference on Computer Vision (ECCV’10). 2010
  • 264.
    Drummond T. Machine, Lindeberg T, Rosten E.
    Learning for high-speed corner detection
    In Proceedings of the 9th European Conference on Computer Vision (ECCV’06). 2006. P. 430-443
  • 265.
    Drummond T. Machine, Friedman J, Hastie T., Rosten E., Tibshirani R.
    The Elements of Statistical Learning
    Springer, 2008
  • 266.
    Friedman J, Hastie T., Ke Y., Sukthankar R, Tibshirani R.
    PCA-SIFT: A more distinctive representation for local image descriptors
    In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’04). 2004. V.2. P. 506-513
  • 267.
    Lowe D
    Distinctive image features from scale-invariant keypoints
    International Journal of Computer Vision. 2004. № 60. P. 91–110
  • 268.
    Chum O., Matas J., PajdlaT, Urban M.
    Robust wide baseline stereo from maximally stable extremal regions
    British Machine Learning Conference. 2002. P. 384-393
  • 269.
    Mikolajczyk K., Schmid C
    Scale and affine invariant interest point detectors
    International Journal of Computer Vision. 2004. № 60(1). P. 63-86
  • 270.
    Bradski G, Konolige K., Rabaud V., Rublee E.
    ORB: an efficient alternative to SIFT or SURF
    In Proceedings of the International Conference on Computer Vision (CVPR’11). 2011. P. 2564-2571
  • 271.
    Szeliski R
    Computer Vision: Algorithms and Applications.
    Springler, 2010. 979p
  • 272.
    Caltech Datasets: object detection datasets
  • 273.
    Formal Description of Moravec detector
    М.: Изд. д. Вильямс, 2004. – 465с
  • 274.
    Harris/Plessey Operator
    Springler, 2010
  • 275.
    Документация модуля features2d библиотеки OpenCV
  • 276.
    Документация модуля nonfree библиотеки OpenCV
    2004
  • 277.
    Официальная страница библиотеки OpenCV
  • 278.
    Схема работы детектора GoodFeaturesToTrack, представленная в
    Computer Vision and Pattern Recognition (CVPR). 2005. V. 1. P. 886-893
  • 279.
    документации OpenCV
    Pattern Analysis and Machine Intelligence. 2009. V. 31, № 12. P. 2179-2195
  • 280.
    Стокман Дж., Шапиро Л.
    Компьютерное зрение
    М.: Бином. Лаборатория знаний. 2006. 752с
  • 281.
    Boyle R, Hlavac V., Sonka M.
    Image Processing, Analysis and Machine Vision
    Thomson. 2008. 866p
  • 282.
    Intel® Integrated Performance Primitives. Reference Manual, Volume 2: Image and Video Processing
    Pattern Recognition and Image Analysis. 2011. V. 21, № 3. P. 384–386
  • 283.
    Страница для загрузки Intel® Parallel Studio XE 2013
  • 284.
    Официальная страница библиотеки Intel® Integrated Performance Primitives
  • 285.
    Официальная страница библиотеки OpenCV
  • 286.
    Сетевой журнал «Компьютерная графика и мультимедиа»
    in IEEE Conf. Computer Vision and Pattern Recognition, 2009
  • 287.
    Документация к функции ippiCanny
    in British Machine Vision Conf., 2005
  • 288.
    Пример применения детектора Канни ippiCanny
    CVPR, Fort Collins, Colorado, USA, pages 87-93, 1999
  • 289.
    Описание функции задания количества потоков при разработке приложений с использованием Intel® Integrated Performance Primitives
  • 290.
    Кормен Т., Лейзерсон Ч., Ривест Р
    Алгоритмы. Построение и анализ
    М.: МЦНМО, 2001
  • 291.
    Coppersmith D., Winograd S
    Matrix Multiplication via Arithmetic Progressions
    J. Symbolic Computation, 9(3). – P. 251–280, 1990
  • 292.
    Касперски К
    Техника оптимизации программ. Эффективное использование памяти.
    BHV, 2003
  • 293.
    Bik A.J.C., Gerber R., Smith K.B., Tian X
    The Software Optimization Cookbook. High-Performance Recipes for the Intel® Architecture
    Intel Press, 2006
  • 294.
    Bik A.J.C
    The software vectorization handbook. Applying Multimedia Extensions for Maximum Performance
    IntelPress, 2004
  • 295.
    Вудхалл А, Таненбаум Э.
    Операционные системы. Разработка и реализация 3-е изд
    СПб.: Питер, 2007
  • 296.
    Hennessy J., Patterson D
    Computer Architecture: A Quantitative Approach
    Morgan Kaufmann, 2006
  • 297.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.: Изд. д. Вильямс, 2004. – 465с
  • 298.
    Szeliski R
    Computer Vision: Algorithms and Applications
    Springer, 2010
  • 299.
    P. Viola and M. Jones
    Rapid object detection using a boosted cascade of simple features
    In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, Hawaii, USA, Vol. 1, 2001, pp. 511-518
  • 300.
    Официальная страница библиотеки Intel® Math Kernel Library
    International Journal of Computer Vision. 2004. № 60. P. 91–110
  • 301.
    Официальная страница библиотеки OpenCV
    British Machine Learning Conference. 2002. P. 384-393
  • 302.
    Intel® Integrated Performance Primitives (Intel® IPP)
    International Journal of Computer Vision. 2004. № 60(1). P. 63-86
  • 303.
    Intel® Integrated Performance Primitives for Linux* OS User's Guide
    In Proceedings of the International Conference on Computer Vision (CVPR’11). 2011. P. 2564-2571
  • 304.
    Intel® Integrated Performance Primitives Reference Manual - Volume 1: Signal Processing
    Springler, 2010. 979p
  • 305.
    Intel® Integrated Performance Primitives Reference Manual - Volume 2:Image and Video Processing
  • 306.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход.
    М.: Изд. д. Вильямс, 2004. – 465с
  • 307.
    Szeliski R
    Computer Vision: Algorithms and Applications.
    Springler, 2010
  • 308.
    Мееров И.Б, Сиднев А.А., Сысоев А.В.
    Intel® Threading Building Blocks. Reference Manual. Version 1.6
    Intel® Corporation, 2007
  • 309.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D., Мееров И.Б, Сиднев А.А., Сысоев А.В.
    Лабораторная работа №3 – Параллельная сортировка вещественных чисел за линейное время
    Материалы образовательного комплекса «Параллельные численные методы». – Нижний Новгород, 2010
  • 310.
    Felzenszwalb P. F., Girshick R. B., McAllester D., P. Viola and M. Jones, Ramanan D.
    Object Detection with Discriminatively Trained Part Based Models
    IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010. Vol.32, No.9. – P. 1627–1645
  • 311.
    Intel® Threading Building Blocks. Tutorial. Version 1.6
    Intel® Corporation, 2007
  • 312.
    А. А., А. В. Сысоев, И. Б. Мееров, Сиднев
    Библиотека Intel Threading Building Blocks – краткое описание. Материалы образовательного комплекса «Технологии разработки параллельных программ».
    Нижний Новгород (2007)
  • 313.
    Корняков К. В., Кустикова В. Д., Мееров И. Б., Сиднев А. А., Сысоев А. В., Шишков А. В
    Инструменты параллельного программирования в системах с общей памятью
    М.: Издательство Московского университета, 2010. –272 с., ил. – (Серия «Супер-компьютерное образование»)
  • 314.
    Extensions for Maximum Performance
    IntelPress, 2004
  • 315.
    Bik A.J.C., Gerber R., Smith K.B., Tian X
    The Software Optimization Cookbook. High-Performance Recipes for the Intel® Architecture
    Intel Press, 2006
  • 316.
    Касперски К
    Техника оптимизации программ. Эффективное использование памяти
    BHV, 2003
  • 317.
    Druzhkov P. N., Eruhimov V. L., Kozinov E. A., Kustikova V. D., Meyerov I. B., Polovinkin A. N., Zolotykh N. Yu
    On some new object detection features in OpenCV Library
    Pattern Recognition and Image Analysis.2011. V. 21, № 2. P. 377–379
  • 318.
    Официальный сайт OpenMP
    In Proceedings of the 11th European Conference on Computer Vision (ECCV’10). 2010
  • 319.
    Страница библиотеки TBB на сайте корпорации Intel
  • 320.
    D.P. Huttenlocher, P.F. Felzenszwalb
    Distance Transforms of Sampled Functions
    2004
  • 321.
    Документация к NVIDIA CUDA SDK
  • 322.
    Официальная страница Intel® OpenCL SDK
  • 323.
    Официальная страница библиотеки Intel® Math Kernel Library
    М.: МЦНМО, 2001
  • 324.
    Страница интерфейса Cilk Plus на сайте корпорации Intel
    J. Symbolic Computation, 9(3). – P. 251–280, 1990
  • 325.
    Документация к Intel® Cilk Plus
    BHV, 2003
  • 326.
    Официальная страница конкурса Visual Object Challenge
    Intel Press, 2006
  • 327.
    A Shape-Based Object Class Model for Knowledge Transfer
    IntelPress, 2004
  • 328.
    Antonio Torralba, Kevin P. Murphy, William T. Freeman and Mark A
    Rubin Context-Based Vision System for Place and Object Recognition
    СПб.: Питер, 2007
  • 329.
    Описание состава VOC Development Kit
    Morgan Kaufmann, 2006
  • 330.
    D. M. Gavrila and V. Philomin
    Real-time object detection for smart vehicles.
    CVPR, Fort Collins, Colorado, USA, pages 87-93, 1999
  • 331.
    and B. Schiele, B. Leibe, E. Seemann, K. Mikolajczyk
    “An evaluation of local shape-based features for pedestrian detection,”
    in British Machine Vision Conf., 2005
  • 332.
    and J. Malik, C. Gu, J. J. Lim, P. Arbelaez
    Recognition using regions
    in IEEE Conf. Computer Vision and Pattern Recognition, 2009
  • 333.
    Dalal N., Triggs B
    Histograms of oriented gradients for human detection
    Computer Vision and Pattern Recognition (CVPR). 2005. V. 1. P. 886-893
  • 334.
    Enzweiler M., Gavrila D.M.
    Monocular Pedestrian Detection: Survey and Experiments
    Pattern Analysis and Machine Intelligence. 2009. V. 31, № 12. P. 2179-2195
  • 335.
    Dollar P., Perona P. Pedestrian Detection:, Schiele B., Wojek C.
    An Evaluation of the State of the Art
    Pattern Analysis and Machine Intelligence. 2012. V. 34, № 4. P. 743-761
  • 336.
    Felzenszwalb P.F., Girshick R.B., McAllester D., Ramanan D
    Image Processing, Analysis and Machine Vision
    Pattern Analysis and Machine Intelligence. 2010. V. 32, № .9. P. 1627-1645.
  • 337.
    Druzhkov P.N., Eruhimov V.L., Kozinov E.A., Kustikova V.D., Meyerov I.B., Polovinkin A.N., Zolotykh N.Yu.
    New object detection features in the OpenCV Library
    Pattern Recognition and Image Analysis. 2011. V. 21, № 3. P. 384–386
  • 338.
    INRIA Person Dataset
  • 339.
    Daimler Monocular Pedestrian Detection Benchmark Data Set
  • 340.
    OpenCV Extras
  • 341.
    Matlab evaluation/labeling code (3.0.1)
    М.: Изд. д. Вильямс, 2004. – 465с

Вопросы и ответы

вопросов: 2
Андрей Терёхин
Андрей Терёхин

Нахожу в тесте вопросы, которые в принципе не освещаются в лекции. Нужно гуглить на других ресурсах, чтобы решить тест, или же он всё же должен испытывать знания, полученные в ходе лекции?

ответить
Демянчик Иван
Демянчик Иван

В главе 14 мы видим понятие фильтра, но не могу разобраться, чем он является в теории и практике.

" Искомый объект можно описать с помощью фильтра F= \lbrace f_{x',y'},x' \in \lbrace0, ...,w_f \rbrace , y' \in \lbrace 0,...,h_f \rbrace \rbrace "

ответить