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Kar, Krishnendu. Advanced computer vision with TensorFlow 2.x: build advanced computer vision applications using machine learning and deep learning techniques / Krishnendu Kar. — 1 online resource — <URL:http://elib.fa.ru/ebsco/2478487.pdf>.

Record create date: 3/10/2020

Subject: Computer vision.; Machine learning.; Computer vision; Machine learning

Collections: EBSCO

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Table of Contents

  • Cover
  • Title Page
  • Copyright and Credits
  • About Packt
  • Contributors
  • Table of Contents
  • Preface
  • Section 1: Introduction to Computer Vision and Neural Networks
  • Chapter 1: Computer Vision and TensorFlow Fundamentals
    • Technical requirements
    • Detecting edges using image hashing and filtering
      • Using a Bayer filter for color pattern formation
      • Creating an image vector
      • Transforming an image
      • Linear filtering—convolution with kernels
        • Image smoothing
          • The mean filter
          • The median filter
          • The Gaussian filter
          • Image filtering with OpenCV
        • Image gradient
        • Image sharpening
      • Mixing the Gaussian and Laplacian operations
      • Detecting edges in an image
        • The Sobel edge detector
        • The Canny edge detector
    • Extracting features from an image
      • Image matching using OpenCV
    • Object detection using Contours and the HOG detector
      • Contour detection
      • Detecting a bounding box
      • The HOG detector
      • Limitations of the contour detection method
    • An overview of TensorFlow, its ecosystem, and installation
      • TensorFlow versus PyTorch
        • TensorFlow Installation
    • Summary
  • Chapter 2: Content Recognition Using Local Binary Patterns
    • Processing images using LBP
      • Generating an LBP pattern
      • Understanding the LBP histogram
        • Histogram comparison methods
      • The computational cost of LBP
    • Applying LBP to texture recognition
    • Matching face color with foundation color – LBP and its limitations
    • Matching face color with foundation color – color matching technique
    • Summary
  • Chapter 3: Facial Detection Using OpenCV and CNN
    • Applying Viola-Jones AdaBoost learning and the Haar cascade classifier for face recognition
      • Selecting Haar-like features 
      • Creating an integral image
      • Running AdaBoost training
      • Attentional cascade classifiers
      • Training the cascade detector
    • Predicting facial key points using a deep neural network
      • Preparing the dataset for key-point detection
      • Processing key-point data
        • Preprocessing before being input into the Keras–Python code 
        • Preprocessing within the Keras–Python code 
      • Defining the model architecture
      • Training the model to make key point predictions
    • Predicting facial expressions using a CNN
    • Overview of 3D face detection
      • Overview of hardware design for 3D reconstruction
      • Overview of 3D reconstruction and tracking
      • Overview of parametric tracking
    • Summary
  • Chapter 4: Deep Learning on Images
    • Understanding CNNs and their parameters
      • Convolution
      • Convolution over volume – 3 x 3 filter
      • Convolution over volume – 1 x 1 filter
      • Pooling
      • Padding 
      • Stride
      • Activation
        • Fully connected layers
      • Regularization
      • Dropout
      • Internal covariance shift and batch normalization 
      • Softmax
    • Optimizing CNN parameters
      • Baseline case
      • Iteration 1 – CNN parameter adjustment
      • Iteration 2 – CNN parameter adjustment
      • Iteration 3 – CNN parameter adjustment
      • Iteration 4 – CNN parameter adjustment
    • Visualizing the layers of a neural network
      • Building a custom image classifier model and visualizing its layers
        • Neural network input and parameters
        • Input image 
        • Defining the train and validation generators
        • Developing the model 
        • Compiling and training the model
        • Inputting a test image and converting it into a tensor
        • Visualizing the first layer of activation
        • Visualizing multiple layers of activation
      • Training an existing advanced image classifier model and visualizing its layers
    • Summary
  • Section 2: Advanced Concepts of Computer Vision with TensorFlow
  • Chapter 5: Neural Network Architecture and Models
    • Overview of AlexNet
    • Overview of VGG16
    • Overview of Inception
      • GoogLeNet detection
    • Overview of ResNet
    • Overview of R-CNN
      • Image segmentation 
        • Clustering-based segmentation
        • Graph-based segmentation
      • Selective search
      • Region proposal
      • Feature extraction
      • Classification of the image
      • Bounding box regression
    • Overview of Fast R-CNN
    • Overview of Faster R-CNN
    • Overview of GANs
    • Overview of GNNs
      • Spectral GNN
    • Overview of Reinforcement Learning
    • Overview of Transfer Learning
    • Summary
  • Chapter 6: Visual Search Using Transfer Learning
    • Coding deep learning models using TensorFlow
      • Downloading weights
      • Decoding predictions
      • Importing other common features
      • Constructing a model
      • Inputting images from a directory
      • Loop function for importing multiple images and processing using TensorFlow Keras
    • Developing a transfer learning model using TensorFlow
      • Analyzing and storing data
      • Importing TensorFlow libraries
      • Setting up model parameters
      • Building an input data pipeline
        • Training data generator
        • Validation data generator
      • Constructing the final model using transfer learning
      • Saving a model with checkpoints
      • Plotting training history
    • Understanding the architecture and applications of visual search
      • The architecture of visual search
      • Visual search code and explanation
        • Predicting the class of an uploaded image
        • Predicting the class of all images
    • Working with a visual search input pipeline using tf.data
    • Summary
  • Chapter 7: Object Detection Using YOLO
    • An overview of YOLO
      • The concept of IOU
      • How does YOLO detect objects so fast?
      • The YOLO v3 neural network architecture
      • A comparison of YOLO and Faster R-CNN
    • An introduction to Darknet for object detection
      • Detecting objects using Darknet
      • Detecting objects using Tiny Darknet
    • Real-time prediction using Darknet
    • YOLO versus YOLO v2 versus YOLO v3 
    • When to train a model?
    • Training your own image set with YOLO v3 to develop a custom model
      • Preparing images
      • Generating annotation files
      • Converting .xml files to .txt files
      • Creating a combined train.txt and test.txt file
      • Creating a list of class name files
      • Creating a YOLO .data file
      • Adjusting the YOLO configuration file
      • Enabling the GPU for training
      • Start training
    • An overview of the Feature Pyramid Network and RetinaNet
    • Summary
  • Chapter 8: Semantic Segmentation and Neural Style Transfer
    • Overview of TensorFlow DeepLab for semantic segmentation
      • Spatial Pyramid Pooling
        • Atrous convolution
        • Encoder-decoder network
          • Encoder module
          • Decoder module
      • Semantic segmentation in DeepLab – example
        • Google Colab, Google Cloud TPU, and TensorFlow
    • Artificial image generation using DCGANs
      • Generator
      • Discriminator
      • Training
        • Image inpainting using DCGAN
      • TensorFlow DCGAN – example
    • Image inpainting using OpenCV
    • Understanding neural style transfer
    • Summary
  • Section 3: Advanced Implementation of Computer Vision with TensorFlow
  • Chapter 9: Action Recognition Using Multitask Deep Learning
    • Human pose estimation – OpenPose
      • Theory behind OpenPose 
      • Understanding the OpenPose code
    • Human pose estimation – stacked hourglass model
      • Understanding the hourglass model
      • Coding an hourglass model
        • argparse block
        • Training an hourglass network
        • Creating the hourglass network
          • Front module
          • Left half-block
          • Connect left to right
          • Right half-block
          • Head block
        • Hourglass training
    • Human pose estimation – PoseNet
      • Top-down approach
      • Bottom-up approach
      • PoseNet implementation
      • Applying human poses for gesture recognition
    • Action recognition using various methods
      • Recognizing actions based on an accelerometer
      • Combining video-based actions with pose estimation
      • Action recognition using the 4D method
    • Summary
  • Chapter 10: Object Detection Using R-CNN, SSD, and R-FCN
    • An overview of SSD
    • An overview of R-FCN
    • An overview of the TensorFlow object detection API
    • Detecting objects using TensorFlow on Google Cloud
    • Detecting objects using TensorFlow Hub
    • Training a custom object detector using TensorFlow and Google Colab
      • Collecting and formatting images as .jpg files
      • Annotating images to create a .xml file
      • Separating the file by train and test folders
      • Configuring parameters and installing the required packages
      • Creating TensorFlow records
      • Preparing the model and configuring the training pipeline
      • Monitoring training progress using TensorBoard
        • TensorBoard running on a local machine
        • TensorBoard running on Google Colab
      • Training the model
      • Running an inference test
      • Caution when using the neural network model
    • An overview of Mask R-CNN and a Google Colab demonstration
    • Developing an object tracker model to complement the object detector
      • Centroid-based tracking
      • SORT tracking
      • DeepSORT tracking
      • The OpenCV tracking method
      • Siamese network-based tracking
      • SiamMask-based tracking
    • Summary
  • Section 4: TensorFlow Implementation at the Edge and on the Cloud
  • Chapter 11: Deep Learning on Edge Devices with CPU/GPU Optimization
    • Overview of deep learning on edge devices
    • Techniques used for GPU/CPU optimization
    • Overview of MobileNet
    • Image processing with a Raspberry Pi
      • Raspberry Pi hardware setup
      • Raspberry Pi camera software setup
      • OpenCV installation in Raspberry Pi
      • OpenVINO installation in Raspberry Pi
      • Installing the OpenVINO toolkit components
        • Setting up the environmental variable
        • Adding a USB rule
        • Running inference using Python code
        • Advanced inference
          • Face detection, pedestrian detection, and vehicle detection
          • Landmark models
          • Models for action recognition
          • License plate, gaze, and person detection
    • Model conversion and inference using OpenVINO
      • Running inference in a Terminal using ncappzoo
      • Converting the pre-trained model for inference
        • Converting from a TensorFlow model developed using Keras
    • Converting a TensorFlow model developed using the TensorFlow Object Detection API
      • Summary of the OpenVINO Model inference process
    • Application of TensorFlow Lite
      • Converting a TensorFlow model into tflite format
        • Python API
        • TensorFlow Object Detection API – tflite_convert
        • TensorFlow Object Detection API – toco
      • Model optimization
    • Object detection on Android phones using TensorFlow Lite
    • Object detection on Raspberry Pi using TensorFlow Lite
      • Image classification
      • Object detection
    • Object detection on iPhone using TensorFlow Lite and Create ML
      • TensorFlow Lite conversion model for iPhone
      • Core ML
      • Converting a TensorFlow model into Core ML format
    • A summary of various annotation methods
      • Outsource labeling work to a third party
      • Automated or semi-automated labeling
    • Summary
  • Chapter 12: Cloud Computing Platform for Computer Vision
    • Training an object detector in GCP
      • Creating a project in GCP
      • The GCP setup
      • The Google Cloud Storage bucket setup
        • Setting up a bucket using the GCP API
        • Setting up a bucket using Ubuntu Terminal
      • Setting up the Google Cloud SDK
      • Linking your terminal to the Google Cloud project and bucket
      • Installing the TensorFlow object detection API
      • Preparing the dataset
        • TFRecord and labeling map data
          • Data preparation
          • Data upload
        • The model.ckpt files
        • The model config file
      • Training in the cloud
      • Viewing the model output in TensorBoard
      • The model output and conversion into a frozen graph
      • Executing export tflite graph.py from Google Colab
    • Training an object detector in the AWS SageMaker cloud platform
      • Setting up an AWS account, billing, and limits
      • Converting a .xml file to JSON format
      • Uploading data to the S3 bucket
      • Creating a notebook instance and beginning training
      • Fixing some common failures during training
    • Training an object detector in the Microsoft Azure cloud platform
      • Creating an Azure account and setting up Custom Vision
      • Uploading training images and tagging them
    • Training at scale and packaging
      • Application packaging
    • The general idea behind cloud-based visual search
    • Analyzing images and search mechanisms in various cloud platforms
      • Visual search using GCP
      • Visual search using AWS
      • Visual search using Azure
    • Summary
  • Other Books You May Enjoy
  • Index

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