Новосибирский Государственный Университет
Опубликован: 20.08.2013 | Доступ: свободный | Студентов: 871 / 40 | Длительность: 14:11:00
  • 1.
    A threshold selection method from gray-level histogram
  • 2.
    Компьютерное зрение
  • 3.
    Topological Structural Analysis of Digitized Binary Images by Border Following.
  • 4.
    A Computational Approach to Edge Detection
  • 5.
    Good Features to Track
  • 6.
    Pattern Recognition and Machine Learning
  • 7.
    Random Forests
  • 8.
    Classification and Regression Trees
  • 9.
    Support-Vector Networks
  • 10.
    Decision-Theoretic Generalization of Online Learning and an Application to Boosting
  • 11.
    Greedy Function Approximation: a Gradient Boosting Machine. Technical report
  • 12.
    Stochastic Gradient Boosting. Technical report.
  • 13.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • 14.
    Machine Learning
  • 15.
    Some Studies in Machine Learning Using the Game of Checkers
  • 16.
    Fast algorithms for mining association rules in large databases
  • 17.
    Extensions of dynamic programming as a new tool for decision tree optimization
  • 18.
    On algorithm for building of optimal ?-decision trees.
  • 19.
    Robust Vehicle Detection through Multidimensional Classification for On Broad Video Based Systems
  • 20.
    2D Object Detection and Recognition: models, algorithms and networks
  • 21.
    Support vector machines for multiple-instance learning
  • 22.
    Machine Learning and Robot Perception.
  • 23.
    Robust vehicle detection through multidimensional classification for on board video based systems
  • 24.
    Detection and tracking of multiple pedestrians in automotive applications
  • 25.
    Real-Time License Plate Recognition on an Embedded DSP-Platform
  • 26.
    Empirical comparison of tree ensemble variable importance measures
  • 27.
    Computer Vision
  • 28.
    An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
  • 29.
    SURF: speed up robust features
  • 30.
    Pedestrin localization and tracking system with Kalman filtering
  • 31.
    A modular tracking system for far infrared pedestrian recognition
  • 32.
    New algorithms for generation decision trees - Ant-Miner and its modifications
  • 33.
    Image classification using random forests and ferns
  • 34.
    Learning OpenCV Computer Vision with OpenCV Library
  • 35.
    Random Forests
  • 36.
    Classification and Regression Trees
  • 37.
    BRIEF: Binary Robust Independent Elementary Features
  • 38.
    Algorithm for constructing of decision trees with minimal number of nodes
  • 39.
    Online learning algorithm for ensemble of decision rules
  • 40.
    Real-time tracking of non-rigid objects using mean shift
  • 41.
    Histograms of oriented gradients for human detection
  • 42.
    Visual categorization with bags of keypoints
  • 43.
    Hierarchical Semantic Indexing for Large Scale Image Retrieval
  • 44.
    What does classifying more than 10,000 image categories tell us?
  • 45.
    ImageNet: A large-scale hierarchical image database
  • 46.
    The fastest pedestrian detector in the west
  • 47.
    Pedestrian Detection: An Evaluation of the State of the Art
  • 48.
    On some new object detection features in OpenCV Library
  • 49.
    Pattern classification (2nd edition).
  • 50.
    Monocular Pedestrian Detection: Survey and Experiments
  • 51.
    Grant Statistical Methods in Bioinformatics: An Introduction
  • 52.
    Fast and robust CAMShift tracking
  • 53.
    WordNet: An Electronic Lexical Database
  • 54.
    Object Detection with Discriminatively Trained Part Based Models
  • 55.
    Cascade object detection with deformable path model
  • 56.
  • 57.
    Real-time stereo vision for urban traffic scene understanding
  • 58.
    The design and use of steerable filters
  • 59.
    A decision-theoretic generalization of online learning and an application to boosting
  • 60.
    Greedy function approximation: the gradient boosting machine
  • 61.
    Greedy Function Approximation: a Gradient Boosting Machine. Technical report
  • 62.
    Stochastic Gradient Boosting. Technical report.
  • 63.
    Importance Sampled Learning Ensembles
  • 64.
    Vision-based pedestrian detection: the protector system
  • 65.
    Pedestrian detection from a moving vehicle
  • 66.
    A Global Approach to Vision Based Pedestrian Detection for Advanced Driver Assistance Systems, PhD Thesis
  • 67.
    Pyramid match kernels: Discriminative classification with sets of image features
  • 68.
    3D vision sensing for improved pedestrian safety
  • 69.
    The Minimum Description Length Principle
  • 70.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • 71.
    Pyramidal Image Analysis for Vehicle Detection
  • 72.
    Robust Extraction of Wheel Region for Vehicle Position Estimation using a Circular Fisheye Camera
  • 73.
    Determing Optical Flow
  • 74.
    Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches
  • 75.
    Real-time tracking and outlier rejection with changes in illumination
  • 76.
    Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Second Edition
  • 77.
    Forward-backward error: automatic detection of tracking failures
  • 78.
    Vehicle Segmentation and Tracking from a Low-Angle Off-Axis Camera
  • 79.
    PCA-SIFT: A more distinctive representation for local image descriptors
  • 80.
    Saul Distance Metric Learning for Large Margin Nearest Neighbor Classification
  • 81.
    Wavelet-based Vehicle Tracking for Automatic Traffic Surveillance
  • 82.
    Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
  • 83.
    Real-time pedestrian and vehicle detection in video using 3D cues
  • 84.
    Dynamic 3D scene analysis from a moving vehicle
  • 85.
    Robust Object Detection with Interleaved Object Categoization and Segmentation
  • 86.
    Measuring the semantic relatedness between words and images
  • 87.
    Classification rule discovery with ant colony optimization
  • 88.
    Distinctive image features from scale-invariant keypoints
  • 89.
    An iterative image registration technique with an application to stereo vision
  • 90.
    Robust wide baseline stereo from maximally stable extremal regions
  • 91.
    iAQ: A program that discovers rules, AAAI-07 AI Video Competition
  • 92.
    A Performance Evaluation of Local Descriptors
  • 93.
    Scale and affine invariant interest point detectors
  • 94.
    Machine Learning
  • 95.
    On algorithm for constructing of decision trees with minimal depth
  • 96.
    Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications.
  • 97.
    Combinatorial Machine Learning: A Rough Set Approach
  • 98.
    An experimental study on pedestrian classification
  • 99.
    Exploiting Hierarchical Contex on a large database of object categories
  • 100.
    Efficient Non-Maximum Supression
  • 101.
    Approximate Boolean reasoning: foundations and applications in data mining
  • 102.
    PLANET: Massively parallel learning of tree ensembles with MapReduce
  • 103.
    Multiple instance boost using graph embedding based decision stump for pedestrian detection
  • 104.
    A trainable system for object detection
  • 105.
    Rough Sets – Theoretical Aspects of Reasoning about Data
  • 106.
    Rough sets and Boolean reasoning.
  • 107.
    Face Recognition for Smart Environments
  • 108.
    Induction of decision trees
  • 109.
    C4.5: Programs for Machine Learning
  • 110.
    Modeling by shortest data description.
  • 111.
    Machine Learning for high-speed corner detection
  • 112.
    Detecting pedestrians by learning shapelet features
  • 113.
    The boosting approach to machine learning. An overview
  • 114.
    Good features to track
  • 115.
    Contour-based Learning for Object Detection
  • 116.
    Rough sets in KDD
  • 117.
    The discernibility matrices and functions in information systems
  • 118.
    rder-based genetic algorithms for the search of approximate entropy reducts
  • 119.
    Image Processing, Analysis and Machine Vision
  • 120.
    Computer Vision: Algorithms and Applications
  • 121.
    Robust Vehicle Detection for Tracking in Highway Surveillance Videos using unsupervised Learning
  • 122.
    A Fast Local Descriptor for Dense Matching
  • 123.
    Contex-based Vision System for Place and Object Recognition
  • 124.
    Local Invariant Feature Detectors: A Survey
  • 125.
    Parallel boosted regression trees for web search ranking
  • 126.
    Vehicle Tracking Using a Human-Vision-Based Model of Visual Similarity
  • 127.
    Detecting pedestrians using patterns of motion and appearance
  • 128.
    Rapid object detection using a boosted cascade of simple features
  • 129.
    Robust Real-Time Face Detection
  • 130.
    Detecting pedestrians using patterns of motion and appearance
  • 131.
    New features and insights for pedestrian detection
  • 132.
    Statistical and Inductive Inference by Minimum Message Length.
  • 133.
    A performance evaluation of single and multi-feature people detection
  • 134.
    Multi-cue onboard pedestrian detection
  • 135.
    Finding minimal reducts using genetic algorithm.
  • 136.
    Detection and tracking of multiple, partially occluded humans by bayesian combination of edgelet based part detectors
  • 137.
    Logistic regression and boosting for labeled bags of instances
  • 138.
    High Performance Data Mining: Scaling Algorithms, Applications and Systems
  • 139.
    Pedestrian detection in infrared images based on local shape features
  • 140.
    On-road vehicle detection using Gabor filters and support vector machines
  • 141.
    Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
  • 142.
    Optimization of decision rules based on methods of dynamic programming
  • 143.
    Теория распознавания образов. Статистические проблемы обучения.
  • 144.
    Параллельная реализация алгоритма предсказания с помощью модели градиентного бустинга деревьев решений
  • 145.
    Программная реализация алгоритма градиентного бустинга деревьев решений
  • 146.
    Новые математические подходы к задачам медицинской диагностики
  • 147.
    Компьютерное зрение. Современный подход.
  • 148.
    Data Mining: учебное пособие
  • 149.
    A sparse object category model for efficient learning and exhaustive recognition.
  • 150.
    Object Class Recognition by Unsupervised Scale-Invariant Learning
  • 151.
    Spatial priors for part-based recognition using statistical models
  • 152.
    Pictorial structures for object recognition
  • 153.
    Visual categorization with bags of keypoints
  • 154.
    Hierarchical part-based visual object categorization
  • 155.
    Sparse flexible models of local features
  • 156.
    Pattern Recognition and Machine Learning.
  • 157.
    The representation and matching of pictorial structures.
  • 158.
    Discriminative Object Class Models of Appearance and Shape by Correlatons.
  • 159.
    A hierarchical model of shape and appearance for human action classification
  • 182.
    Learning OpenCV
  • 199.
    Robust Real-Time Face Detection
  • 200.
    Object Detection with Discriminatively Trained Part Based Models
  • 201.
    Компьютерное зрение. Современный подход
  • 202.
    Topological Structural Analysis of Digitized Binary Images by Border Following
  • 203.
    Компьютерное зрение.
  • 204.
    On the detection of dominant points on digital curves
  • 205.
    Learning OpenCV Computer Vision with OpenCV Library
  • 206.
    Компьютерное зрение.
  • 207.
    Компьютерное зрение. Современный подход
  • 208.
    A computational approach to edge detection
  • 217.
    The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • 218.
    Classification and Regression Trees
  • 219.
    New object detection features in the OpenCV Library
  • 220.
    Random Forests
  • 221.
    k-means++: the advantages of careful seeding
  • 224.
    Censure: Center surround extremas for realtime feature detection and matching
  • 225.
    SURF: speed up robust features
  • 226.
    Learning OpenCV Computer Vision with OpenCV Library
  • 227.
    BRIEF: Binary Robust Independent Elementary Features
  • 228.
    The Elements of Statistical Learning.
  • 229.
    PCA-SIFT: A more distinctive representation for local image descriptors
  • 230.
    Feature detection with automatic scale selection
  • 231.
    Distinctive image features from scale-invariant keypoints
  • 232.
    Robust wide baseline stereo from maximally stable extremal regions
  • 233.
    Scale and affine invariant interest point detectors
  • 234.
    Machine Learning for high-speed corner detection
  • 235.
    ORB: an efficient alternative to SIFT or SURF
  • 236.
    Computer Vision: Algorithms and Applications.
  • 244.
    Компьютерное зрение
  • 245.
    Image Processing, Analysis and Machine Vision
  • 246.
  • 254.
    Алгоритмы. Построение и анализ.
  • 255.
    Техника оптимизации программ. Эффективное использование памяти.
  • 259.
  • 260.
  • 262.
  • 265.
  • 268.
    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?