舉報(bào) 
會員
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
- Leave a review - let other readers know what you think 更新時間：2021-06-30 18:55:38
推薦閱讀
- 并行數(shù)據(jù)挖掘及性能優(yōu)化：關(guān)聯(lián)規(guī)則與數(shù)據(jù)相關(guān)性分析
- 數(shù)據(jù)運(yùn)營之路：掘金數(shù)據(jù)化時代
- 條碼技術(shù)及應(yīng)用
- 機(jī)器學(xué)習(xí)流水線實(shí)戰(zhàn)
- Visual C++編程全能詞典
- 傳感器與新聞
- C++程序設(shè)計(jì)基礎(chǔ)（上）
- 案例解說Delphi典型控制應(yīng)用
- Oracle 11g Anti-hacker's Cookbook
- 渲染王3ds Max三維特效動畫技術(shù)
- 智能小車機(jī)器人制作大全（第2版）
- SolarWinds Server & Application Monitor：Deployment and Administration
- Windows Server 2012 Automation with PowerShell Cookbook
- Hadoop大數(shù)據(jù)開發(fā)基礎(chǔ)
- 局域網(wǎng)組建與使用完全自學(xué)手冊
- Mastering Windows Group Policy
- ARM? Cortex? M4 Cookbook
- 數(shù)字媒體交互設(shè)計(jì)原理與方法
- 從零開始學(xué)Visual C++
- 超限學(xué)習(xí)機(jī)：理論、技術(shù)與應(yīng)用
- NetBeans權(quán)威指南
- Excel 2003中文電子表格5日通
- 微機(jī)原理與接口技術(shù)
- 巧學(xué)活用Excel
- 數(shù)據(jù)庫原理、應(yīng)用與開發(fā)
- Mastering Azure Machine Learning
- UG NX 8.0中文版從入門到精通
- IBM SPSS Modeler Essentials
- Mastering PostgreSQL 12
- 深度學(xué)習(xí)實(shí)戰(zhàn)
