Computer Vision Algorithms and Applications 2nd Edition by Richard Szeliski ISBN 3030343723 9783030343729

Computer Vision: Algorithms and Applications 2nd Edition by Richard Szeliski – Ebook PDF Instant Download/Delivery: 3030343723, 9783030343729

Full download Computer Vision: Algorithms and Applications 2nd Edition after payment

 

 

Product details:

ISBN 10: 3030343723

ISBN 13: 9783030343729

Author:  Richard Szeliski 

Computer Vision: Algorithms and Applications 2nd Edition: 

Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos.

More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles.

Topics and features:

• Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses
• Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality
• Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects
  
• Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade
  
• Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, estimation theory, datasets, and software
  

Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

 

Computer Vision: Algorithms and Applications 2nd Edition Table of contents:

Chapter 1 Introduction
1.1 What is computer vision?
1.2 A brief history
1.3 Book overview
1.4 Sample syllabus
1.5 A note on notation
1.6 Additional reading

Chapter 2 Image formation
2.1 Geometric primitives and transformations

• 2.1.1 2D transformations
• 2.1.2 3D transformations
• 2.1.3 3D rotations
• 2.1.4 3D to 2D projections
• 2.1.5 Lens distortions
  2.2 Photometric image formation
• 2.2.1 Lighting
• 2.2.2 Reflectance and shading
• 2.2.3 Optics
  2.3 The digital camera
• 2.3.1 Sampling and aliasing
• 2.3.2 Color
• 2.3.3 Compression
  2.4 Additional reading
  2.5 Exercises

Chapter 3 Image processing
3.1 Point operators

• 3.1.1 Pixel transforms
• 3.1.2 Color transforms
• 3.1.3 Compositing and matting
• 3.1.4 Histogram equalization
  3.2 Linear filtering
• 3.2.1 Separable filtering
• 3.2.2 Examples of linear filtering
• 3.2.3 Band-pass and steerable filters
  3.3 More neighborhood operators
• 3.3.1 Non-linear filtering
• 3.3.2 Bilateral filtering
• 3.3.3 Binary image processing
  3.4 Fourier transforms
• 3.4.1 Two-dimensional Fourier transforms
• 3.4.2 Application: Sharpening, blur, and noise removal
  3.5 Pyramids and wavelets
• 3.5.1 Interpolation
• 3.5.2 Decimation
• 3.5.3 Multi-resolution representations
• 3.5.4 Wavelets
• 3.5.5 Application: Image blending
  3.6 Geometric transformations
• 3.6.1 Parametric transformations
• 3.6.2 Mesh-based warping
• 3.6.3 Application: Feature-based morphing
  3.7 Additional reading
  3.8 Exercises

Chapter 4 Model fitting and optimization
4.1 Scattered data interpolation

• 4.1.1 Radial basis functions
• 4.1.2 Overfitting and underfitting
• 4.1.3 Robust data fitting
  4.2 Variational methods and regularization
• 4.2.1 Discrete energy minimization
• 4.2.2 Total variation
• 4.2.3 Bilateral solver
• 4.2.4 Application: Interactive colorization
  4.3 Markov random fields
• 4.3.1 Conditional random fields
• 4.3.2 Application: Interactive segmentation
  4.4 Additional reading
  4.5 Exercises

Chapter 5 Deep Learning
5.1 Supervised learning

• 5.1.1 Nearest neighbors
• 5.1.2 Bayesian classification
• 5.1.3 Logistic regression
• 5.1.4 Support vector machines
• 5.1.5 Decision trees and forests
  5.2 Unsupervised learning
• 5.2.1 Clustering
• 5.2.2 K-means and Gaussians mixture models
• 5.2.3 Principal component analysis
• 5.2.4 Manifold learning
• 5.2.5 Semi-supervised learning
  5.3 Deep neural networks
• 5.3.1 Weights and layers
• 5.3.2 Activation functions
• 5.3.3 Regularization and normalization
• 5.3.4 Loss functions
• 5.3.5 Backpropagation
• 5.3.6 Training and optimization
  5.4 Convolutional neural networks
• 5.4.1 Pooling and unpooling
• 5.4.2 Application: Digit classification
• 5.4.3 Network architectures
• 5.4.4 Model zoos
• 5.4.5 Visualizing weights and activations
• 5.4.6 Adversarial examples
• 5.4.7 Self-supervised learning
  5.5 More complex models
• 5.5.1 Three-dimensional CNNs
• 5.5.2 Recurrent neural networks
• 5.5.3 Transformers
• 5.5.4 Generative models
  5.6 Additional reading
  5.7 Exercises

Chapter 6 Recognition
6.1 Instance recognition
6.2 Image classification

• 6.2.1 Feature-based methods
• 6.2.2 Deep networks
• 6.2.3 Application: Visual similarity search
• 6.2.4 Face recognition
  6.3 Object detection
• 6.3.1 Face detection
• 6.3.2 Pedestrian detection
• 6.3.3 General object detection
  6.4 Semantic segmentation
• 6.4.1 Application: Medical image segmentation
• 6.4.2 Instance segmentation
• 6.4.3 Panoptic segmentation
• 6.4.4 Application: Intelligent photo editing
• 6.4.5 Pose estimation
  6.5 Video understanding
  6.6 Vision and language
  6.7 Additional reading
  6.8 Exercises

Chapter 7 Feature detection and matching
7.1 Points and patches

• 7.1.1 Feature detectors
• 7.1.2 Feature descriptors
• 7.1.3 Feature matching
• 7.1.4 Large-scale matching and retrieval
• 7.1.5 Feature tracking
• 7.1.6 Application: Performance-driven animation
  7.2 Edges and contours
• 7.2.1 Edge detection
• 7.2.2 Contour detection
• 7.2.3 Application: Edge editing and enhancement
  7.3 Contour tracking
• 7.3.1 Snakes and scissors
• 7.3.2 Level Sets
• 7.3.3 Application: Contour tracking and rotoscoping
  7.4 Lines and vanishing points
• 7.4.1 Successive approximation
• 7.4.2 Hough transforms
• 7.4.3 Vanishing points
  7.5 Segmentation
• 7.5.1 Graph-based segmentation
• 7.5.2 Mean shift
• 7.5.3 Normalized cuts
  7.6 Additional reading
  7.7 Exercises

Chapter 8 Image alignment and stitching
8.1 Pairwise alignment

• 8.1.1 2D alignment using least squares
• 8.1.2 Application: Panography
• 8.1.3 Iterative algorithms
• 8.1.4 Robust least squares and RANSAC
• 8.1.5 3D alignment
  8.2 Image stitching
• 8.2.1 Parametric motion models
• 8.2.2 Application: Whiteboard and document scanning
• 8.2.3 Rotational panoramas
• 8.2.4 Gap closing
• 8.2.5 Application: Video summarization and compression
• 8.2.6 Cylindrical and spherical coordinates
  8.3 Global alignment
• 8.3.1 Bundle adjustment
• 8.3.2 Parallax removal
• 8.3.3 Recognizing panoramas
  8.4 Compositing
• 8.4.1 Choosing a compositing surface
• 8.4.2 Pixel selection and weighting (deghosting)
• 8.4.3 Application: Photomontage
• 8.4.4 Blending
  8.5 Additional reading
  8.6 Exercises

Chapter 9 Motion estimation
9.1 Translational alignment

• 9.1.1 Hierarchical motion estimation
• 9.1.2 Fourier-based alignment
• 9.1.3 Incremental refinement
  9.2 Parametric motion
• 9.2.1 Application: Video stabilization
• 9.2.2 Spline-based motion
• 9.2.3 Application: Medical image registration
  9.3 Optical flow
• 9.3.1 Deep learning approaches
• 9.3.2 Application: Rolling shutter wobble removal
• 9.3.3 Multi-frame motion estimation
• 9.3.4 Feature-based optical flow
• 9.3.5 Local matching
  9.4 Additional reading
  9.5 Exercises

Chapter 10 3D vision
10.1 Stereo matching

• 10.1.1 Stereo algorithms
• 10.1.2 Correspondence problems
• 10.1.3 Semi-global matching
  10.2 Structure from motion
• 10.2.1 Epipolar geometry
• 10.2.2 Visual odometry
• 10.2.3 3D reconstruction
• 10.2.4 SLAM
• 10.2.5 Multi-view stereo
  10.3 Light field imaging
• 10.3.1 Application: Plenoptic cameras
• 10.3.2 Holography
• 10.3.3 Time-of-flight imaging
  10.4 Depth sensing
• 10.4.1 Depth from stereo
• 10.4.2 Structured light
• 10.4.3 Time-of-flight sensors
• 10.4.4 Lidar
  10.5 Additional reading
  10.6 Exercises

Chapter 11 Visual tracking
11.1 Tracking by detection
11.2 Tracking by matching

• 11.2.1 Optical flow-based tracking
• 11.2.2 Features-based tracking
  11.3 Deep learning for tracking
  11.4 Tracking algorithms
• 11.4.1 Kalman filtering
• 11.4.2 Particle filtering
• 11.4.3 Mean-shift tracking
  11.5 Applications of visual tracking
  11.6 Additional reading
  11.7 Exercises

Chapter 12 Advanced topics in computer vision
12.1 3D reconstruction
12.2 Computational photography
12.3 Deep learning for vision
12.4 Visual SLAM
12.5 Applications in robotics
12.6 Additional reading
12.7 Exercises

Index

 

People also search for Computer Vision: Algorithms and Applications 2nd Edition:

szeliski r 2010 computer vision algorithms and applications springer
computer vision algorithms and applications pdf
computer vision algorithms and applications 2nd ed
szeliski r 2010 computer vision algorithms and applications
computer vision algorithms and applications 2nd edition by richard szeliski

Tags:

Richard Szeliski,Computer Vision,Algorithms,Applications