Введение в разработку мультимедийных приложений с использованием библиотек OpenCV и IPP

:

Введение в разработку мультимедийных приложений с использованием библиотек OpenCV и IPP

: Литература по курсу
[+]
Опубликован: 20.08.2013 | Уровень: для всех | Доступ: платный | ВУЗ: Новосибирский Государственный Университет
  • 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.
    Canny.
    A 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 IEEE Conference on Computer Vision and Pattern Recognition, pages 593-600, June 1994.
  • 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. Classi
    Classification and Regression Trees
    Wadsworth & Brooks, 1984.
  • 9.
    Cortes C., Vapnik V. N.
    Support-Vector Networks
    Machine Learning. 1995. V. 20, № 3. P. 273–297.
  • 10.
    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.
  • 11.
    Friedman J. H.
    Greedy Function Approximation: a Gradient Boosting Machine. Technical report
    Dept. of Statistics, Stanford University, 1999.
  • 12.
    Friedman J. H.
    Stochastic Gradient Boosting. Technical report.
    Dept. of Statistics, Stanford University, 1999.
  • 13.
    Friedman J., Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    Springer, 2001.
  • 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, № 3. P. 210–229.
  • 16.
    Agrawal R., Srikant R. (19
    Fast algorithms for mining association rules in large databases
    In: Bocca JB, Jarke M, Zaniolo C (eds.) Proc. 20th Int. Conf. on Very Large Data Bases, VLDB'94, Morgan Kaufmann, 487-499.
  • 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.
  • 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., Limberger F.
    Real-Time License Plate 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.-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.
  • 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.
  • 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.
  • 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.
    Asuncion A, Frank A
    UCI Machine Learning Repository
    Cited 2 July 2011.
  • 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, 2003.
  • 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 Global Approach to Vision Based Pedestrian Detection for Advanced Driver Assistance Systems, PhD Thesis
    Computer Vision Center Barcelona, February 2010.
  • 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, No.12. – 2009.
  • 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.
  • 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.
  • 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.
  • 97.
    Moshkov M, Zielosko B
    Combinatorial Machine Learning: A Rough Set Approach
    Studies in Computational Intelligence, vol. 360. Springer, Heidelberg.
  • 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. Ef
    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.
  • 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, Dordrecht.
  • 106.
    Pawlak Z, Skowron A
    Rough sets and Boolean reasoning.
    Information Sciences 177: 41-73.
  • 107.
    Choudhury T, Pentland A.
    Face 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.
  • 110.
    Rissanen J
    Modeling by shortest data description.
    Automatica 14:465-471.
  • 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.
  • 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.
  • 118.
    Slezak D, Wroblewski J
    rder-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.
  • 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.
  • 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.
  • 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)
  • 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
  • 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.
  • 152.
    D. P, Felzenszwalb, P. F. and Huttenlocher
    Pictorial structures for object recognition
    International Journal of Computer Vision, 61(1):55–79.
  • 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
  • 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.
  • 155.
    Carneiro, D, G. and Lowe
    Sparse flexible models of local features
    In Ninth European Conference on Computer Vision (ECCV 2006), pp. 29–43
  • 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., Lu Y., Tan F., 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.
    Сайт OpenCV
  • 173.
    Лучшая современная книга по компьютерному зрению
  • 174.
    «Официальная» книга про OpenCV:
  • 175.
    Примеры кода из книги выше:
  • 176.
    «Официальный» учебник по Питону на русском:
  • 177.
    Numpy – превращает Питон в аналог Матлаба (и необходим для использования OpenCV с Питоном)
  • 178.
    Intel® Integrated Performance Primitives (Intel® IPP) 7.1
  • 179.
    Intel® Integrated Performance Primitives for Linux* OS User's Guide
  • 180.
    Intel® Integrated Performance Primitives Reference Manual - Volume 1: Signal Processing
  • 181.
    Intel® Integrated Performance Primitives Reference Manual - Volume 2: Image and Video Processing
  • 182.
    Bradski G., Kaehler A
    Learning OpenCV
    O’Reilly, 2008. – 571p
  • 183.
    Официальная страница библиотеки OpenCV
  • 184.
    Официальная страница EmguCV
  • 185.
    Официальная страница CMake
  • 186.
    Сервер для загрузки последней версии исходных кодов библиотеки OpenCV
  • 187.
    Сервер для загрузки последней версии исходных кодов библиотеки OpenCV и дополнительных файлов (например, натренированные модели, файлы входных данных и результатов для автоматических тестов)
  • 188.
    Страница загрузки клиента Subversion
  • 189.
    Руководство по сборке и установке библиотеки OpenCV
  • 190.
    Официальная страница проекта Jasper
  • 191.
    Официальная страница проекта libjpeg
  • 192.
    Официальная страница проекта libpng
  • 193.
    Официальная страница проекта libtiff
  • 194.
    Официальная страница библиотеки zlib
  • 195.
    Описание базовых структур данных библиотеки OpenCV
  • 196.
    Описание функций преобразования изображений
  • 197.
    Обнаружение и локализация лица на изображении
  • 198.
    Описание функций отрисовки примитивов на изображении
  • 199.
    Jones M.J, Viola P.
    Robust Real-Time Face Detection
    international Journal of Computer Vision 57(2). – 2004. – pp. 137-154.
  • 200.
    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.
  • 201.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.: Изд. д. Вильямс, 2004. – 465с.
  • 202.
    Abe K, Suzuki S.
    Topological Structural Analysis of Digitized Binary Images by Border Following
    CVGIP 30 1. – 1985. – pp. 32-46.
  • 203.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение.
    М.: Бином. Лаборатория знаний, 2006. – 752с.
  • 204.
    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.
  • 205.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV Library
    O' Reilly Media Publishers, 2008. 571p.
  • 206.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение.
    М.: Бином. Лаборатория знаний, 2006. 752с.
  • 207.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.: Изд. д. Вильямс, 2004. 465с.
  • 208.
    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.
  • 209.
    Официальная страница библиотеки OpenCV
  • 210.
    Лекция Интернет Университета «Фильтрация изображений»
  • 211.
    Документация модуля imgproc библиотеки OpenCV
  • 212.
    Схема работы детектора Канни, представленная в документации OpenCV
  • 213.
    Canny Edge Detection
  • 214.
    Документация OpenCV с описанием базовых структур данных
  • 215.
    Страница загрузки Qt libraries
  • 216.
    Описание функций библиотеки OpenCV, реализованных на базе Qt
  • 217.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
    Springer, 2001.
  • 218.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J
    Classification and Regression Trees
    Wadsworth & Brooks, 1984.
  • 219.
    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.
  • 220.
    Breiman L
    Random Forests
    Machine Learning 2001. V. 45, № 1. P. 5–32.
  • 221.
    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.
  • 222.
    Официальная страница библиотеки OpenCV
  • 223.
    Документация модуля ml библиотеки OpenCV
  • 224.
    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.
  • 225.
    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.
  • 226.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV Library
    O' Reilly Media Publishers, 2008. 571p.
  • 227.
    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.
  • 228.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning.
    Springer, 2008.
  • 229.
    Ke Y., Sukthankar 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.
  • 230.
    Lindeberg T
    Feature detection with automatic scale selection
    International Journal of Computer Vision. 1998. V.30. Issue 2.
  • 231.
    Lowe D. Di
    Distinctive image features from scale-invariant keypoints
    International Journal of Computer Vision. 2004. № 60. P. 91–110.
  • 232.
    Chum O., Matas J., PajdlaT, Urban M.
    Robust wide baseline stereo from maximally stable extremal regions
    British Machine Learning Conference. 2002. P. 384-393.
  • 233.
    Mikolajczyk K., Schmid C
    Scale and affine invariant interest point detectors
    International Journal of Computer Vision. 2004. № 60(1). P. 63-86.
  • 234.
    Drummond T, Rosten E.
    Machine Learning for high-speed corner detection
    In Proceedings of the 9th European Conference on Computer Vision (ECCV’06). 2006. P. 430-443.
  • 235.
    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
  • 236.
    Szeliski R
    Computer Vision: Algorithms and Applications.
    Springler, 2010. 979p.
  • 237.
    Caltech Datasets: object detection datasets
  • 238.
    Formal Description of Moravec detector
  • 239.
    Harris/Plessey Operator
  • 240.
    Документация модуля features2d библиотеки OpenCV
  • 241.
    Документация модуля nonfree библиотеки OpenCV
  • 242.
    Официальная страница библиотеки OpenCV
  • 243.
    Схема работы детектора GoodFeaturesToTrack, представленная в документации OpenCV
  • 244.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение
    М.: Бином. Лаборатория знаний. 2006. 752с.
  • 245.
    Boyle R, Hlavac V., Sonka M.
    Image Processing, Analysis and Machine Vision
    Thomson. 2008. 866p.
  • 246.
    Intel®
    Integrated Performance Primitives
    Reference Manual, Volume 2: Image and Video Processing
  • 247.
    Страница для загрузки Intel® Parallel Studio XE 2013
  • 248.
    Официальная страница библиотеки Intel® Integrated Performance Primitives
  • 249.
    Официальная страница библиотеки OpenCV
  • 250.
    Сетевой журнал «Компьютерная графика и мультимедиа»
  • 251.
    Документация к функции ippiCanny_<…>
  • 252.
    Пример применения детектора Канни ippiCanny_<…>
  • 253.
    Описание функции задания количества потоков при разработке приложений с использованием Intel® Integrated Performance Primitives
  • 254.
    Кормен Т., Лейзерсон Ч., Ривест Р
    Алгоритмы. Построение и анализ.
    М.: МЦНМО, 2001.
  • 255.
    Coppersmith D., Winograd S, Касперски К
    Техника оптимизации программ. Эффективное использование памяти.
    BHV, 2003.
  • 256.
    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.
  • 257.
    Bik A.J.C
    The software vectorization handbook. Applying Multimedia Extensions for Maximum Performance.
    IntelPress, 2004.
  • 258.
    Вудхалл А, Таненбаум Э.
    Операционные системы. Разработка и реализация
    3-е изд. – СПб.: Питер, 2007.
  • 259.
    Hennessy J., Patterson D
    Computer Architecture: A Quantitative Approach
    –Morgan Kaufmann, 2006.
  • 260.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
    М.: Изд. д. Вильямс, 2004. – 465с.
  • 261.
    Szeliski R, Мееров И.Б, Сиднев А.А., Сысоев А.В.
    Computer Vision: Algorithms and Applications
    Springer, 2010.
  • 262.
    Szeliski R, 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.
  • 263.
    Понс Ж, Форсайт Д.
    Официальная страница библиотеки Intel® Math Kernel Library
    М.: Изд. д. Вильямс, 2004. – 465с.
  • 264.
    Hennessy J., Patterson D
    Официальная страница библиотеки OpenCV
    –Morgan Kaufmann, 2006.
  • 265.
    Вудхалл А, Таненбаум Э.
    Intel® Integrated Performance Primitives (Intel® IPP) 7.1
    3-е изд. – СПб.: Питер, 2007.
  • 266.
    Intel® Integrated Performance Primitives Reference Manual - Volume 2: Image and Video Processing
    IntelPress, 2004.
  • 267.
    Bik A.J.C., Gerber R., Smith K.B., Tian X
    Intel® Integrated Performance Primitives Reference Manual - Volume 1: Signal Processing
    Intel Press, 2006.
  • 268.
    Coppersmith D., Winograd S, Касперски К
    Matrix Multiplication via Arithmetic Progressions
    J. Symbolic Computation, 9(3). – P. 251–280, 1990.
  • 269.
    Intel® Integrated Performance Primitives for Linux* OS User's Guide
    IntelPress, 2004.

Вопросы и ответы

вопросов: 2
Александра Максимова
Александра Максимова

При прохождении теста 1 в нем оказались вопросы, который во-первых в 1 лекции не рассматривались, во-вторых, оказалось, что вопрос был рассмаотрен в самостоятельно работе №2. Это значит, что их нужно выполнить перед прохождением теста? или это ошибка?
 

ответить
Алена Борисова
Алена Борисова

В лекции по обработке полутоновых изображений (http://www.intuit.ru/studies/courses/10621/1105/lecture/17979?page=2) увидела следующий фильтр:

\begin{array}{|c|c|c|} \hline \\ 0 & 0 & 0 \\ \hline \\ 0 & 2 & 0 \\ \hline \\ 0 & 0 & 0 \\ \hline \end{array} - \frac{1}{9} \begin{array}{|c|c|c|} \hline \\ 0 & 0 & 0 \\ \hline \\ 0 & 1 & 0 \\ \hline \\ 0 & 0 & 0 \\ \hline \end{array}

В описании говорится, что он "делает изображение более чётким, потому что, как видно из конструкции фильтра, в однородных частях изображение не изменяется, а в местах изменения яркости это изменение усиливается".

Что вижу я в конструкции фильтра (скорее всего ошибочно): F(x, y) = 2 * I(x, y) - 1/9 I(x, y) = 17/9 * I(x, y), где F(x, y) - яркость отфильтрованного пикселя, а I(x, y) - яркость исходного пикселя с координатами (x, y). Что означает обычное повышение яркости изображения, при этом без учета соседних пикселей (так как их множители равны 0).

Объясните, пожалуйста, как данный фильтр может повышать четкость изображения?

ответить

Студенты

всего: 24
Сергей Кротов
Сергей Кротов
Россия
предложить дружбу
Дмитрий Донсков
Дмитрий Донсков
Россия, Москва, Московский Авиационный Институт
предложить дружбу