Новосибирский Государственный Университет
Опубликован: 20.08.2013 | Доступ: свободный | Студентов: 865 / 38 | Длительность: 14:11:00
  • 1.
    Otsu N.
    A threshold selection method from gray-level histogram
  • 2.
    Дж. Стокман, Л. Шапиро
    Компьютерное зрение
  • 3.
    K., S. and Abe, Suzuki
    Topological Structural Analysis of Digitized Binary Images by Border Following.
  • 4.
    Canny.
    A Computational Approach to Edge Detection
  • 5.
    Shi and C. Tomasi.
    Good Features to Track
  • 6.
    Bishop C. M.
    Pattern Recognition and Machine Learning
  • 7.
    Breiman L.
    Random Forests
  • 8.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classi
    Classification and Regression Trees
  • 9.
    Cortes C., Vapnik V. N.
    Support-Vector Networks
  • 10.
    Freund Y., Schapire R. A
    Decision-Theoretic Generalization of Online Learning and an Application to Boosting
  • 11.
    Friedman J. H.
    Greedy Function Approximation: a Gradient Boosting Machine. Technical report
  • 12.
    Friedman J. H.
    Stochastic Gradient Boosting. Technical report.
  • 13.
    Friedman J., Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • 14.
    Mitchell T.
    Machine Learning
  • 15.
    Samuel A.
    Some Studies in Machine Learning Using the Game of Checkers
  • 16.
    Agrawal R., Srikant R. (19
    Fast algorithms for mining association rules in large databases
  • 17.
    Alkhalid A, Chikalov I, Hussain S, Moshkov M
    Extensions of dynamic programming as a new tool for decision tree optimization
  • 18.
    Alkhalid A, Chikalov I, Moshkov M
    On algorithm for building of optimal ?-decision trees.
  • 19.
    Alonso D., Nieto M., Saldaro L.
    Robust Vehicle Detection through Multidimensional Classification for On Broad Video Based Systems
  • 20.
    Amit Y.
    2D Object Detection and Recognition: models, algorithms and networks
  • 21.
    Andrews S., Hofmann T., Tsochantaridis I.
    Support vector machines for multiple-instance learning
  • 22.
    Alpaslan F., Apolloni B., Ghosh A., Jain L.C., Patnaik S.
    Machine Learning and Robot Perception.
  • 23.
    Arrospide J., Jaureguizar F., Nieto M., Salgado L.
    Robust vehicle detection through multidimensional classification for on board video based systems
  • 24.
    Arndt R., Lhlein O., Paulus D., Schweiger R. Ritter W.
    Detection and tracking of multiple pedestrians in automotive applications
  • 25.
    Arth C., Bischof H., Limberger F.
    Real-Time License Plate Recognition on an Embedded DSP-Platform
  • 26.
    Aldrich C., Aureta L.
    Empirical comparison of tree ensemble variable importance measures
  • 27.
    Ballard D.H., Brown C.M.
    Computer Vision
  • 28.
    Bauer E., Kohavi R.
    An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
  • 29.
    Bay H., Ess A., Gool L.V., Tuytelaars T.
    SURF: speed up robust features
  • 30.
    Bertozzi M., Broggi A., Chapuis R., Chausse F., Fascioli A., Tibaldi A.
    Pedestrin localization and tracking system with Kalman filtering
  • 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
  • 32.
    Boryczka U, Kozak J
    New algorithms for generation decision trees - Ant-Miner and its modifications
  • 33.
    Bosch A., Munoz X., Zisserman A.
    Image classification using random forests and ferns
  • 34.
    Bradski G., Kaehler A.
    Learning OpenCV Computer Vision with OpenCV Library
  • 35.
    Breiman L.
    Random Forests
  • 36.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J.
    Classification and Regression Trees
  • 37.
    Calonder M., Fua P., Lepetit V., Strecha C.
    BRIEF: Binary Robust Independent Elementary Features
  • 38.
    Chikalov I
    Algorithm for constructing of decision trees with minimal number of nodes
  • 39.
    Chikalov I, Moshkov M, Zielosko B
    Online learning algorithm for ensemble of decision rules
  • 40.
    Comaniciu D., Meer P., Ramesh V.
    Real-time tracking of non-rigid objects using mean shift
  • 41.
    Dalal N., Triggs B.
    Histograms of oriented gradients for human detection
  • 42.
    Bray C., Csurka G., Dance C., Fan L., Willamowski J.
    Visual categorization with bags of keypoints
  • 43.
    Berg A., Deng J., Fei-Fei L.
    Hierarchical Semantic Indexing for Large Scale Image Retrieval
  • 44.
    Berg A., Deng J., Fei-Fei L., Li K.
    What does classifying more than 10,000 image categories tell us?
  • 45.
    Deng J., Dong W., Fei-Fei L., Li K., Li L., Socher R.
    ImageNet: A large-scale hierarchical image database
  • 46.
    Belongie S., Dollar P., Perona P.
    The fastest pedestrian detector in the west
  • 47.
    Dollar P., Perona P., Schiele B., Wojek C.
    Pedestrian Detection: An Evaluation of the State of the Art
  • 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
  • 49.
    Duda R.O., Hart P.E., Stork D.G.
    Pattern classification (2nd edition).
  • 50.
    Enzweiler M., Gavrila D. M.
    Monocular Pedestrian Detection: Survey and Experiments
  • 51.
    Ewens W.J., Grant G.
    Grant Statistical Methods in Bioinformatics: An Introduction
  • 52.
    Bruns E., Exner D., Grundhofer A., Kurz D.
    Fast and robust CAMShift tracking
  • 53.
    Fellbaum C.
    WordNet: An Electronic Lexical Database
  • 54.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D.
    Object Detection with Discriminatively Trained Part Based Models
  • 55.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D.
    Cascade object detection with deformable path model
  • 56.
    Asuncion A, Frank A
  • 57.
    Franke U., Joss A.
    Real-time stereo vision for urban traffic scene understanding
  • 58.
    Adelson E., Freeman W.
    The design and use of steerable filters
  • 59.
    Freund Y., Schapire R.
    A decision-theoretic generalization of online learning and an application to boosting
  • 60.
    Friedman J.
    Greedy function approximation: the gradient boosting machine
  • 61.
    Friedman J. H.
    Greedy Function Approximation: a Gradient Boosting Machine. Technical report
  • 62.
    Friedman J. H.
    Stochastic Gradient Boosting. Technical report.
  • 63.
    Friedman J.H., Popescu B.E.
    Importance Sampled Learning Ensembles
  • 64.
    Gavrila D. M., Giebel J., Munder S.
    Vision-based pedestrian detection: the protector system
  • 65.
    Gavrila D.M.
    Pedestrian detection from a moving vehicle
  • 66.
    Geronimo D.
    A Global Approach to Vision Based Pedestrian Detection for Advanced Driver Assistance Systems, PhD Thesis
  • 67.
    Darrell T., Grauman K.
    Pyramid match kernels: Discriminative classification with sets of image features
  • 68.
    Grubb G., Nilsson L., Rilbe M., Zelinsky A.
    3D vision sensing for improved pedestrian safety
  • 69.
    Grunwald PD
    The Minimum Description Length Principle
  • 70.
    Friedman J., Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • 71.
    Armingol J.M., Collado J.M., Escalera A., Hilario C.
    Pyramidal Image Analysis for Vehicle Detection
  • 72.
    Hama H., Hirose K., Torio T.
    Robust Extraction of Wheel Region for Vehicle Position Estimation using a Circular Fisheye Camera
  • 73.
    Horn B., Schunk B.
    Determing Optical Flow
  • 74.
    Jensen R, Shen Q
    Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
  • 75.
    Favaro P., Jin H., Soatto S.
    Real-time tracking and outlier rejection with changes in illumination
  • 76.
    Jurafsky D., Martin J.H.
    Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Second Edition
  • 77.
    Kalal Z., Matas J., Mikolajczyk K.
    Forward-backward error: automatic detection of tracking failures
  • 78.
    Birchfield S. T., Kanhere N. K., Pundlik S. J.
    Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
  • 79.
    Ke Y., Sukthankar R.
    PCA-SIFT: A more distinctive representation for local image descriptors
  • 80.
    Kilian Q. Weinberger, Lawrence K.
    Saul Distance Metric Learning for Large Margin Nearest Neighbor Classification
  • 81.
    Kim H.J., Kim J.B., Lee C.W., Lee K.M., Yun T.S.
    Wavelet-based Vehicle Tracking for Automatic Traffic Surveillance
  • 82.
    Lazebnik S., Ponce J., Schmid C.
    Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
  • 83.
    Chiu T.H., Hung Y.P., Lee P.H., Lin Y.L.
    Real-time pedestrian and vehicle detection in video using 3D cues
  • 84.
    Cornelis K., Cornelis N., Leibe B., Van Gool L.
    Dynamic 3D scene analysis from a moving vehicle
  • 85.
    Leibe B., Leonardis A., Schiele B.
    Robust Object Detection with Interleaved Object Categoization and Segmentation
  • 86.
    Leong C.W., Mihalcea R.
    Measuring the semantic relatedness between words and images
  • 87.
    Abbass HA, Liu B, McKay B
    Classification rule discovery with ant colony optimization
  • 88.
    Lowe D.
    Distinctive image features from scale-invariant keypoints
  • 89.
    Kanade T., Lucas B.D.
    An iterative image registration technique with an application to stereo vision
  • 90.
    Chum O., Matas J., PajdlaT., Urban M.
    Robust wide baseline stereo from maximally stable extremal regions
  • 91.
    Michalski SR, Pietrzykowski J
    iAQ: A program that discovers rules, AAAI-07 AI Video Competition
  • 92.
    Mikolajczyk K., Schmid C.
    A Performance Evaluation of Local Descriptors
  • 93.
    Mikolajczyk K., Schmid C.
    Scale and affine invariant interest point detectors
  • 94.
    Mitchell T.
    Machine Learning
  • 95.
    Chikalov I, Moshkov M
    On algorithm for constructing of decision trees with minimal depth
  • 96.
    Moshkov M, Piliszczuk M, Zielosko B
    Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications.
  • 97.
    Moshkov M, Zielosko B
    Combinatorial Machine Learning: A Rough Set Approach
  • 98.
    Gavrila D.M., Munder S.
    An experimental study on pedestrian classification
  • 99.
    A., A.S., J.J., Lim, Myung Jin Choi, Torralba, Willsky
    Exploiting Hierarchical Contex on a large database of object categories
  • 100.
    Neubeck A., Van Gool L. Ef
    Efficient Non-Maximum Supression
  • 101.
    Nguyen HS
    Approximate Boolean reasoning: foundations and applications in data mining
  • 102.
    Basu S., Bayardo R. J, Herbach J. S., Panda B.
    PLANET: Massively parallel learning of tree ensembles with MapReduce
  • 103.
    Huang Q., Jiang S, Pang J.
    Multiple instance boost using graph embedding based decision stump for pedestrian detection
  • 104.
    Papageorgiou C., Poggio T
    A trainable system for object detection
  • 105.
    Pawlak Z
    Rough Sets – Theoretical Aspects of Reasoning about Data
  • 106.
    Pawlak Z, Skowron A
    Rough sets and Boolean reasoning.
  • 107.
    Choudhury T, Pentland A.
    Face Recognition for Smart Environments
  • 108.
    Quinlan J.R
    Induction of decision trees
  • 109.
    Quinlan JR
    C4.5: Programs for Machine Learning
  • 110.
    Rissanen J
    Modeling by shortest data description.
  • 111.
    Drummond T, Rosten E.
    Machine Learning for high-speed corner detection
  • 112.
    Mori G, Sabzmeydani P.
    Detecting pedestrians by learning shapelet features
  • 113.
    Schapire R
    The boosting approach to machine learning. An overview
  • 114.
    Shi J., Tomasi C
    Good features to track
  • 115.
    Blake A., Cipolla R, Shotton J.
    Contour-based Learning for Object Detection
  • 116.
    Skowron A
    Rough sets in KDD
  • 117.
    Rauszer C, Skowron A
    The discernibility matrices and functions in information systems
  • 118.
    Slezak D, Wroblewski J
    rder-based genetic algorithms for the search of approximate entropy reducts
  • 119.
    Boyle R, Hlavac V., Sonka M.
    Image Processing, Analysis and Machine Vision
  • 120.
    Szeliski R
    Computer Vision: Algorithms and Applications
  • 121.
    Aggarwal J.K, Tamersoy B.
    Robust Vehicle Detection for Tracking in Highway Surveillance Videos using unsupervised Learning
  • 122.
    Fua P, Lepetit V., Tola E.
    A Fast Local Descriptor for Dense Matching
  • 123.
    Freeman W.T., Murphy K.P., Rubin M.A, Torralba A.
    Contex-based Vision System for Place and Object Recognition
  • 124.
    Mikolajczyk K, Tuytelaars T.
    Local Invariant Feature Detectors: A Survey
  • 125.
    Agrawal K., Paykin J, Tyree S., Weinberger K. Q.
    Parallel boosted regression trees for web search ranking
  • 126.
    Chandler D.M, Vasu L.
    Vehicle Tracking Using a Human-Vision-Based Model of Visual Similarity
  • 127.
    Jones M., Snow D, Viola P.
    Detecting pedestrians using patterns of motion and appearance
  • 128.
    Jones M.J, Viola P.
    Rapid object detection using a boosted cascade of simple features
  • 129.
    Jones M.J, Viola P.
    Robust Real-Time Face Detection
  • 130.
    Jones M.J., Snow D, Viola P.
    Detecting pedestrians using patterns of motion and appearance
  • 131.
    Majer N., Schiele B, Schindler K., Walk S.
    New features and insights for pedestrian detection
  • 132.
    Wallace CS
    Statistical and Inductive Inference by Minimum Message Length.
  • 133.
    Schiele B, Wojek C.
    A performance evaluation of single and multi-feature people detection
  • 134.
    Schiele B, Walk S., Wojek C.
    Multi-cue onboard pedestrian detection
  • 135.
    Wroblewski J
    Finding minimal reducts using genetic algorithm.
  • 136.
    Nevatia R, Wu B.
    Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors
  • 137.
    Frank E, Xu X.
    Logistic regression and boosting for labeled bags of instances
  • 138.
    Grossman R, Yike G.
    High Performance Data Mining: Scaling Algorithms, Applications and Systems
  • 139.
    Nevatia R, Wu B., Zhang L.
    Pedestrian detection in infrared images based on local shape features
  • 140.
    Bebis G., Miller R, Zehang Sun
    On-road vehicle detection using Gabor filters and support vector machines
  • 141.
    Avidan S., Cheng K, Yeh M., Zhu Q.
    Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
  • 142.
    Chikalov I, Moshkov M, Zielosko B
    Optimization of decision rules based on methods of dynamic programming
  • 143.
    Вапник В.Н., Червоненкис А.Я
    Теория распознавания образов. Статистические проблемы обучения.
  • 144.
    Дружков П. Н., Золотых Н. Ю., Половинкин А. Н
    Параллельная реализация алгоритма предсказания с помощью модели градиентного бустинга деревьев решений
  • 145.
    Дружков П.Н., Золотых Н.Ю., Половинкин А.Н
    Программная реализация алгоритма градиентного бустинга деревьев решений
  • 146.
    Котов Ю.Б
    Новые математические подходы к задачам медицинской диагностики
  • 147.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход.
  • 148.
    Чубукова И. А
    Data Mining: учебное пособие
  • 149.
    A, and Zisserman, Fergus, P., Perona, R.
    A sparse object category model for efficient learning and exhaustive recognition.
  • 150.
    A, Fergus, P. and Zisserman, R. and Perona
    Object Class Recognition by Unsupervised Scale-Invariant Learning
  • 151.
    and Huttenlocher, Crandall, D, D., Felzenszwalb, P.
    Spatial priors for part-based recognition using statistical models
  • 152.
    D. P, Felzenszwalb, P. F. and Huttenlocher
    Pictorial structures for object recognition
  • 153.
    and Bray, C, C. R., Csurka, Dance, Fan, G., J., L., Willamowski
    Visual categorization with bags of keypoints
  • 154.
    B, Bouchard, G. and Triggs
    Hierarchical part-based visual object categorization
  • 155.
    Carneiro, D, G. and Lowe
    Sparse flexible models of local features
  • 156.
    Bishop, C. M
    Pattern Recognition and Machine Learning.
  • 157.
    Fischler, M. A. and Elschlager, R. A
    The representation and matching of pictorial structures.
  • 158.
    J. Winn and A. Criminisi, S. Savarese
    Discriminative Object Class Models of Appearance and Shape by Correlatons.
  • 159.
    Fei-Fei, J.C., L, Niebles
    A hierarchical model of shape and appearance for human action classification
  • 182.
    Bradski G., Kaehler A
    Learning OpenCV
  • 199.
    Jones M.J, Viola P.
    Robust Real-Time Face Detection
  • 200.
    Felzenszwalb P. F., Girshick R. B., McAllester D., Ramanan D
    Object Detection with Discriminatively Trained Part Based Models
  • 201.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
  • 202.
    Abe K, Suzuki S.
    Topological Structural Analysis of Digitized Binary Images by Border Following
  • 203.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение.
  • 204.
    Chin, Cho-Huak, Roland T, Teh
    On the detection of dominant points on digital curves
  • 205.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV Library
  • 206.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение.
  • 207.
    Понс Ж, Форсайт Д.
    Компьютерное зрение. Современный подход
  • 208.
    Canny J
    A computational approach to edge detection
  • 217.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • 218.
    Breiman L., Friedman J.H., Olshen R.A., Stone C.J
    Classification and Regression Trees
  • 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
  • 220.
    Breiman L
    Random Forests
  • 221.
    Arthur D., Vassilvitskii S
    k-means++: the advantages of careful seeding
  • 224.
    Agrawal M., Blas M, Konolige K.
    Censure: Center surround extremas for realtime feature detection and matching
  • 225.
    Bay H., Ess A., Gool L.V, Tuytelaars T.
    SURF: speed up robust features
  • 226.
    Bradski G., Kaehler A
    Learning OpenCV Computer Vision with OpenCV Library
  • 227.
    Calonder M., Fua P, Lepetit V., Strecha C.
    BRIEF: Binary Robust Independent Elementary Features
  • 228.
    Friedman J, Hastie T., Tibshirani R.
    The Elements of Statistical Learning.
  • 229.
    Ke Y., Sukthankar R
    PCA-SIFT: A more distinctive representation for local image descriptors
  • 230.
    Lindeberg T
    Feature detection with automatic scale selection
  • 231.
    Lowe D. Di
    Distinctive image features from scale-invariant keypoints
  • 232.
    Chum O., Matas J., PajdlaT, Urban M.
    Robust wide baseline stereo from maximally stable extremal regions
  • 233.
    Mikolajczyk K., Schmid C
    Scale and affine invariant interest point detectors
  • 234.
    Drummond T, Rosten E.
    Machine Learning for high-speed corner detection
  • 235.
    Bradski G, Konolige K., Rabaud V., Rublee E.
    ORB: an efficient alternative to SIFT or SURF
  • 236.
    Szeliski R
    Computer Vision: Algorithms and Applications.
  • 244.
    Стокман Дж, Шапиро Л.
    Компьютерное зрение
  • 245.
    Boyle R, Hlavac V., Sonka M.
    Image Processing, Analysis and Machine Vision
  • 246.
    Intel®
  • 254.
    Кормен Т., Лейзерсон Ч., Ривест Р
    Алгоритмы. Построение и анализ.
  • 255.
    Coppersmith D., Winograd S, Касперски К
    Техника оптимизации программ. Эффективное использование памяти.
  • 256.
    Bik A.J.C., Gerber R., Smith K.B., Tian X
  • 258.
    Вудхалл А, Таненбаум Э.
  • 259.
    Hennessy J., Patterson D
  • 260.
    Понс Ж, Форсайт Д.
  • 261.
    Szeliski R, Мееров И.Б, Сиднев А.А., Сысоев А.В.
  • 262.
    Szeliski R, Мееров И.Б, Сиднев А.А., Сысоев А.В.
  • 263.
    Понс Ж, Форсайт Д.
  • 264.
    Hennessy J., Patterson D
  • 265.
    Вудхалл А, Таненбаум Э.
  • 267.
    Bik A.J.C., Gerber R., Smith K.B., Tian X
  • 268.
    Coppersmith D., Winograd S, Касперски К
    Matrix Multiplication via Arithmetic Progressions
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?
 

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?