Description
Section 11. Chapter 1: Prerequisite and Software Installation 1.1. Python and PIP 1.1.1. Installing Python and PIP on Ubuntu 1.1.2. Installing Python and PIP on Mac OS 1.1.3. Installing Python and PIP on CentOS 7 1.1.4. Installing Python and PIP on Windows 1.2. Virtualenv 1.2.1. Setup and activate virtualenv 1.3. Tensorflow 1.3.1. Installing Tensorflow 1.4. PyCharm IDE 1.4.1. Installing PyCharm 1.4.2. Configuring PyCharm to use virtualenv 1.5. OpenCV 1.5.1. Installing OpenCV 1.5.2. Installing OpenCV4 with Python bindings 1.6. Additional libraries 1.6.1. SciPy 1.6.2. Matplotlib
Chapter 2: Core Concepts of Image and Video Processing 1.7. Image processing 1.7.1. Image basics 1.7.2. Pixel 1.7.3. Pixel color 1.7.3.1. Grayscale 1.7.3.2. Color 1.7.4. Coordinate system 1.7.5. Python and OpenCV code to manipulate images 1.7.6. Program: loading, exploring and showing image 1.7.7. Program: OpenCV code to access and manipulate pixels 1.8. Drawing 1.8.1. Drawing a line on an image 1.8.2. Drawing a rectangle on an image 1.8.3. Drawing a circle on an image 1.9. Chapter summary 1.10. 2. Chapter 3: Techniques of Image Processing 2.1. Transformation 2.1.1. Resizing 2.1.2. Translation 2.1.3. Rotation 2.1.4. Flipping 2.1.5. Cropping 2.2. Image arithmetic and bitwise operations 2.2.1. Addition 2.2.2. Subtraction 2.2.3. Bitwise operations 2.2.3.1. OR 2.2.3.2. AND 2.2.3.3. NOT 2.2.3.4. XOR 2.3. Masking 2.4. Splitting and merging channels 2.5. Smoothing and blurring 2.6. Thresholding 2.7. Gradient and edge detection 2.8. Contours2.9. Chapter summary
Section 23. Chapter 4: Building Artificial Intelligence System For Computer Vision 3.1. Image processing pipeline 3.2. Feature extraction 3.2.1. Color histogram 3.2.2. GLCM 3.2.3. HOG 3.2.4. LBP 3.3. Feature selection 3.3.1. Filter 3.3.2. Wrapper 3.3.3. Embedded 3.3.4. Regularization 3.4. Chapter summary
4. Chapter 5: Artificial Neural Network for Computer Vision 4.1. Introduction to ANN 4.1.1. ANN topology 4.1.2. Hyperparameters 4.1.3. ANN model training using TensorFlow 4.1.4. Model evaluation 4.1.5. Model deployment 4.1.6. Use of trained model 4.2. Introduction to Convolution Neural Network (CNN)4.2.1. Core concepts of CNN4.2.2. Creating training set for CNN4.2.3. Training CNN model using TensorFlow 4.2.4. Inspecting CNN model and evaluating model fitness4.2.5. Using and deployment of trained model4.3. Introduction to Recurrent Neural Network (RNN) and long short-term Memory (LSTM)4.3.1. Core concepts of RNN and LSTM4.3.2. Creating training set for LSTM4.3.3. LSTM model training using TensorFlow4.3.4. Inspecting LSTM model and assessing fitness4.3.5. Deploying LSTM models in practice
Section 35. Chapter 6: Practical Example 1- Object Detection in Images 6. Chapter 7: Practical Example 2- Object Tracking in Videos 7. Chapter 8: Practical Example 3- Facial Detection 8. Chapter 9: Industrial Application - Realtime Defect Detection in Industrial Manufacturing
Author: Shamshad Ansari
Publisher: Apress
Published: 07/16/2020
Pages: 451
Binding Type: Paperback
Weight: 1.80lbs
Size: 10.00h x 7.00w x 0.96d
ISBN13: 9781484258866
ISBN10: 148425886X
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Python
- Computers | Programming | Open Source
Chapter 2: Core Concepts of Image and Video Processing 1.7. Image processing 1.7.1. Image basics 1.7.2. Pixel 1.7.3. Pixel color 1.7.3.1. Grayscale 1.7.3.2. Color 1.7.4. Coordinate system 1.7.5. Python and OpenCV code to manipulate images 1.7.6. Program: loading, exploring and showing image 1.7.7. Program: OpenCV code to access and manipulate pixels 1.8. Drawing 1.8.1. Drawing a line on an image 1.8.2. Drawing a rectangle on an image 1.8.3. Drawing a circle on an image 1.9. Chapter summary 1.10. 2. Chapter 3: Techniques of Image Processing 2.1. Transformation 2.1.1. Resizing 2.1.2. Translation 2.1.3. Rotation 2.1.4. Flipping 2.1.5. Cropping 2.2. Image arithmetic and bitwise operations 2.2.1. Addition 2.2.2. Subtraction 2.2.3. Bitwise operations 2.2.3.1. OR 2.2.3.2. AND 2.2.3.3. NOT 2.2.3.4. XOR 2.3. Masking 2.4. Splitting and merging channels 2.5. Smoothing and blurring 2.6. Thresholding 2.7. Gradient and edge detection 2.8. Contours2.9. Chapter summary
Section 23. Chapter 4: Building Artificial Intelligence System For Computer Vision 3.1. Image processing pipeline 3.2. Feature extraction 3.2.1. Color histogram 3.2.2. GLCM 3.2.3. HOG 3.2.4. LBP 3.3. Feature selection 3.3.1. Filter 3.3.2. Wrapper 3.3.3. Embedded 3.3.4. Regularization 3.4. Chapter summary
4. Chapter 5: Artificial Neural Network for Computer Vision 4.1. Introduction to ANN 4.1.1. ANN topology 4.1.2. Hyperparameters 4.1.3. ANN model training using TensorFlow 4.1.4. Model evaluation 4.1.5. Model deployment 4.1.6. Use of trained model 4.2. Introduction to Convolution Neural Network (CNN)4.2.1. Core concepts of CNN4.2.2. Creating training set for CNN4.2.3. Training CNN model using TensorFlow 4.2.4. Inspecting CNN model and evaluating model fitness4.2.5. Using and deployment of trained model4.3. Introduction to Recurrent Neural Network (RNN) and long short-term Memory (LSTM)4.3.1. Core concepts of RNN and LSTM4.3.2. Creating training set for LSTM4.3.3. LSTM model training using TensorFlow4.3.4. Inspecting LSTM model and assessing fitness4.3.5. Deploying LSTM models in practice
Section 35. Chapter 6: Practical Example 1- Object Detection in Images 6. Chapter 7: Practical Example 2- Object Tracking in Videos 7. Chapter 8: Practical Example 3- Facial Detection 8. Chapter 9: Industrial Application - Realtime Defect Detection in Industrial Manufacturing
Author: Shamshad Ansari
Publisher: Apress
Published: 07/16/2020
Pages: 451
Binding Type: Paperback
Weight: 1.80lbs
Size: 10.00h x 7.00w x 0.96d
ISBN13: 9781484258866
ISBN10: 148425886X
BISAC Categories:
- Computers | Artificial Intelligence | General
- Computers | Languages | Python
- Computers | Programming | Open Source
About the Author
Shamshad (Sam) Ansari works as President and CEO of Accure Inc, an artificial intelligence automation company that he founded. He has raised Accure from startup to a sustainable business by building a winning team and acquiring customers from across the globe. He has technical expertise in the area of computer vision, machine learning, AI, cognitive science, NLP, and big data. He architected, designed, and developed the Momentum platform that automates AI solution development. He is an inventor and has four US patents in the area of AI and cognitive computing.
Shamshad worked as a senior software engineer with IBM, VP of engineering with Orbit Solutions, and as principal architect and director of engineering with Apixio.