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Practical Computer Vision
ThisbookisformachinelearningpractitionersanddeeplearningenthusiastswhowanttounderstandandimplementvarioustasksassociatedwithComputerVisionandimageprocessinginthemostpracticalmannerpossible.SomeprogrammingexperiencewouldbebeneficialwhileknowingPythonwouldbeanaddedbonus.
目錄(201章)
倒序
- coverpage
- Title Page
- Dedication
- Packt Upsell
- Why subscribe?
- PacktPub.com
- Contributors
- About the author
- About the reviewer
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- A Fast Introduction to Computer Vision
- What constitutes computer vision?
- Computer vision is everywhere
- Getting started
- Reading an image
- Image color conversions
- Computer vision research conferences
- Summary
- Libraries Development Platform and Datasets
- Libraries and installation
- Installing Anaconda
- NumPy
- Matplotlib
- SciPy
- Jupyter notebook
- Installing OpenCV
- OpenCV Anaconda installation
- OpenCV build from source
- Opencv FAQs
- TensorFlow for deep learning
- Keras for deep learning
- Datasets
- ImageNet
- MNIST
- CIFAR-10
- Pascal VOC
- MSCOCO
- TUM RGB-D dataset
- Summary
- References
- Image Filtering and Transformations in OpenCV
- Datasets and libraries required
- Image manipulation
- Introduction to filters
- Linear filters
- 2D linear filters
- Box filters
- Properties of linear filters
- Non-linear filters
- Smoothing a photo
- Histogram equalization
- Median filter
- Image gradients
- Transformation of an image
- Translation
- Rotation
- Affine transform
- Image pyramids
- Summary
- What is a Feature?
- Features use cases
- Datasets and libraries
- Why are features important?
- Harris Corner Detection
- FAST features
- ORB features
- FAST feature limitations
- BRIEF Descriptors and their limitations
- ORB features using OpenCV
- The black box feature
- Application – find your object in an image
- Applications – is it similar?
- Summary
- References
- Convolutional Neural Networks
- Datasets and libraries used
- Introduction to neural networks
- A simple neural network
- Revisiting the convolution operation
- Convolutional Neural Networks
- The convolution layer
- The activation layer
- The pooling layer
- The fully connected layer
- Batch Normalization
- Dropout
- CNN in practice
- Fashion-MNIST classifier training code
- Analysis of CNNs
- Popular CNN architectures
- VGGNet
- Inception models
- ResNet model
- Transfer learning
- Summary
- Feature-Based Object Detection
- Introduction to object detection
- Challenges in object detection
- Dataset and libraries used
- Methods for object detection
- Deep learning-based object detection
- Two-stage detectors
- Demo – Faster R-CNN with ResNet-101
- One-stage detectors
- Demo
- Summary
- References
- Segmentation and Tracking
- Datasets and libraries
- Segmentation
- Challenges in segmentation
- CNNs for segmentation
- Implementation of FCN
- Tracking
- Challenges in tracking
- Methods for object tracking
- MOSSE tracker
- Deep SORT
- Summary
- References
- 3D Computer Vision
- Dataset and libraries
- Applications
- Image formation
- Aligning images
- Visual odometry
- Visual SLAM
- Summary
- References
- Mathematics for Computer Vision
- Datasets and libraries
- Linear algebra
- Vectors
- Addition
- Subtraction
- Vector multiplication
- Vector norm
- Orthogonality
- Matrices
- Operations on matrices
- Addition
- Subtraction
- Matrix multiplication
- Matrix properties
- Transpose
- Identity matrix
- Diagonal matrix
- Symmetric matrix
- Trace of a matrix
- Determinant
- Norm of a matrix
- Getting the inverse of a matrix
- Orthogonality
- Computing eigen values and eigen vectors
- Hessian matrix
- Singular Value Decomposition
- Introduction to probability theory
- What are random variables?
- Expectation
- Variance
- Probability distributions
- Bernoulli distribution
- Binomial distribution
- Poisson distribution
- Uniform distribution
- Gaussian distribution
- Joint distribution
- Marginal distribution
- Conditional distribution
- Bayes theorem
- Summary
- Machine Learning for Computer Vision
- What is machine learning?
- Kinds of machine learning techniques
- Supervised learning
- Classification
- Regression
- Unsupervised learning
- Dimensionality's curse
- A rolling-ball view of learning
- Useful tools
- Preprocessing
- Normalization
- Noise
- Postprocessing
- Evaluation
- Precision
- Recall
- F-measure
- Summary
- Other Books You May Enjoy
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