Neural Network Computer Vision with OpenCV 5.

Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentatio...

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Detaylı Bibliyografya
Yazar: Nuti, Gopi Krishna
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Delhi : BPB Publications, 2023.
Edisyon:1st ed.
Konular:
Electronic books.
Online Erişim:Full-text access
İçindekiler:
  • Intro
  • Cover Page
  • Title Page
  • Copyright Page
  • Dedication
  • About the Author
  • About the Reviewer
  • Acknowledgement
  • Preface
  • Table of Contents
  • 1. Introduction to Computer Vision
  • Introduction
  • Structure
  • Objectives
  • History of computer imaging
  • Retrieving information from images
  • Image processing
  • Representation
  • Manipulation
  • Flexibility
  • Reproducibility
  • Digital image processing
  • Conclusion
  • Exercises
  • 2. Basics of Imaging
  • Introduction
  • Structure
  • Objectives
  • Pixels and image representation
  • Pixels
  • Color spaces
  • Primary colors
  • Additive colors
  • Subtractive colors
  • Grayscale
  • Other color spaces
  • Pixels and color spaces
  • Examples
  • Image filetypes
  • Video files
  • Images and videos
  • Programming for image data
  • A brief history of computer image programming
  • OpenCV: History and overview
  • Image processing code samples
  • Opening, viewing and closing image files
  • CPP code
  • Python code
  • Videos and frames
  • Programming with color spaces
  • Grayscale
  • RGB image
  • Conclusion
  • Exercises
  • 3. Challenges in Computer Vision
  • Introduction
  • Structure
  • Objectives
  • Topics in computer vision
  • Complexity in image processing
  • Image classification
  • Object localization
  • Image segmentation
  • Character recognition
  • Conclusion
  • Exercises
  • Key terms
  • 4. Classical Solutions
  • Introduction
  • Structure
  • Objectives
  • Solutions for challenges in computer vision
  • Classical solutions
  • Modern solutions
  • Algorithm families
  • Morphological operations
  • Erosion and dilation of images
  • Closing and opening images
  • Thresholding
  • Detecting edges and corners
  • Image transformations
  • Region growing
  • Clustering
  • Template matching
  • Watershed algorithm
  • Foreground and background detection
  • Superpixels
  • Image pyramids
  • Convolution.
  • Conclusion
  • Exercises
  • Key terms
  • 5. Deep Learning and CNNs
  • Introduction
  • Structure
  • Objectives
  • History of deep learning
  • Perceptron
  • Shallow learning networks
  • Deep learning networks
  • Weights, biases, and activation functions
  • Weight
  • Bias
  • Activation function
  • Optimization function
  • Convolutional neural networks
  • CNNs versus fully connected networks
  • Deep learning process
  • Training
  • Techniques in training
  • Inference process
  • Techniques/tricks in inference
  • Conclusion
  • Key terms
  • Exercises
  • 6. OpenCV DNN Module
  • Introduction
  • Structure
  • Objectives
  • Deep learning frameworks
  • TensorFlow
  • PyTorch
  • Keras
  • Inference for computer vision
  • Local inferencing
  • Local CPUs
  • Local GPUs
  • Cloud
  • Edge computing
  • OpenCV DNN module
  • History
  • Features and limitations
  • Capabilities
  • Limitations
  • Considerations
  • Supported layers
  • Unsupported layers and operations
  • Important classes
  • Conclusion
  • Exercises
  • 7. Modern Solutions for Image Classification
  • Introduction
  • Structure
  • Objectives
  • CNNS for classification
  • Inception-v3
  • Keras
  • OpenCV DNN module
  • ResNet
  • Keras implementation
  • OpenCV DNN implementation
  • MobileNetV2
  • Keras implementation
  • OpenCV DNN implementation
  • Comparison of models
  • Parameters for blobFromImage()
  • Conclusion
  • Exercises
  • 8. Modern Solutions for Object Detection
  • Introduction
  • Structure
  • Convolutional neural networks architecture for object detection
  • Faster region convolutional neural network
  • Single shot multibox detector
  • You only look once
  • YOLOv3
  • Overview of NMSBoxes() API
  • YOLOv5
  • Differences between YOLOv3 and v5
  • Obtaining v5 model ONNX file
  • Working with v6, v7 and v8
  • Conclusion
  • Exercises
  • 9. Faces and Text
  • Introduction
  • Structure
  • Objectives
  • Face detection.
  • Haar cascades
  • Deep learning approaches: YuNet
  • Face recognition
  • Face detection versus recognition
  • Face recognition using landmarks
  • Face recognizer module
  • Labeled Faces in the Wild dataset
  • FaceRecognizerSF class
  • Comparing faces
  • Text recognition
  • Text detection
  • Text recognition
  • OpenCV Model Zoo
  • Conclusion
  • Exercises
  • Key terms
  • 10. Running the Code
  • Introduction
  • Structure
  • Objectives
  • Sequence of steps
  • Setting up Anaconda
  • Installing Anaconda on Windows
  • Installing Anaconda on Ubuntu Linux
  • Installing Git
  • Installing Git on Windows
  • Installing Git on Ubuntu
  • Setting up Python environment
  • Fetching the code
  • Downloading the code
  • Fetch the weights
  • Installing the libraries
  • Running the code
  • Conclusion
  • Exercises
  • 11. End-to-end Demo
  • Introduction
  • Structure
  • Objectives
  • Code
  • main_app.py
  • video_app_ui.py
  • image_processor.py
  • numberplate_recognizor.py
  • object_detector.py
  • Running the code
  • Application design
  • Notes about codes
  • Conclusion
  • Exercises
  • Index.

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