Building computer vision applications using artificial neural networks : with step-by-step Eeamples in OpenCV and TensorFlow with Python /: with step-by-step Eeamples in OpenCV and TensorFlow with Python. (2020)

• Record Type: Book
• Further Information: Note: Shamshad Ansari.
• Other Names: Ansari, Shamshad
• Contents: Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Prerequisites and Software Installation -- Python and PIP -- Installing Python and PIP on Ubuntu -- Installing Python and PIP on macOS -- Installing Python and PIP on CentOS 7 -- Installing Python and PIP on Windows -- virtualenv -- Installing and Activating virtualenv -- TensorFlow -- Installing TensorFlow -- PyCharm IDE -- Installing PyCharm -- Configuring PyCharm to Use virtualenv -- OpenCV -- Working with OpenCV -- Installing OpenCV4 with Python Bindings Additional Libraries -- Installing SciPy -- Installing Matplotlib -- Chapter 2: Core Concepts of Image and Video Processing -- Image Processing -- Image Basics -- Pixels -- Pixel Color -- Grayscale -- Color -- Coordinate Systems -- Python and OpenCV Code to Manipulate Images -- Program: Loading, Exploring, and Showing an Image -- Program: OpenCV Code to Access and Manipulate Pixels -- Drawing -- Drawing a Line on an Image -- Drawing a Rectangle on an Image -- Drawing a Circle on an Image -- Summary -- Chapter 3: Techniques of Image Processing -- Transformation -- Resizing -- Translation Rotation -- Flipping -- Cropping -- Image Arithmetic and Bitwise Operations -- Addition -- Subtraction -- Bitwise Operations -- AND -- OR -- NOT -- XOR -- Masking -- Splitting and Merging Channels -- Noise Reduction Using Smoothing and Blurring -- Mean Filtering or Averaging -- Gaussian Filtering -- Median Blurring -- Bilateral Blurring -- Binarization with Thresholding -- Simple Thresholding -- Adaptive Thresholding -- Otsu's Binarization -- Gradients and Edge Detection -- Sobel Derivatives (cv2.Sobel() Function) -- Laplacian Derivatives (cv2.Laplacian() Function) -- Canny Edge Detection Contours -- Drawing Contours -- Summary -- Chapter 4: Building a Machine Learning-Based Computer Vision System -- Image Processing Pipeline -- Feature Extraction -- How to Represent Features -- Color Histogram -- How to Calculate a Histogram -- Grayscale Histogram -- RGB Color Histogram -- Histogram Equalizer -- GLCM -- HOGs -- LBP -- Feature Selection -- Filter Method -- Wrapper Method -- Embedded Method -- Model Training -- How to Do Machine Learning -- Supervised Learning -- Unsupervised Learning -- Model Deployment -- Summary -- Chapter 5: Deep Learning and Artificial Neural Networks Introduction to Artificial Neural Networks -- Perceptron -- How a Perceptron Learns -- Multilayer Perceptron -- Why MLP? -- What Is Deep Learning? -- Deep Learning or Multilayer Perceptron Architecture -- Activation Functions -- Linear Activation Function -- Sigmoid or Logistic Activation Function -- TanH/Hyperbolic Tangent -- Rectified Linear Unit -- Leaky ReLU -- Scaled Exponential Linear Unit -- Softplus Activation Function -- Softmax -- Feedforward -- Error Function -- Regression Loss Function -- Binary Classification Loss Function -- Multiclass Classification Loss Function
• Publisher Details: Berkeley, CA : Apress
• Publication Date: 2020
• Extent: 1 online resource (467 p.)
• Subjects: 006.3/7; Computer vision; Neural networks (Computer science); Electronic books; Electronic books
• Languages: English
• ISBNs: 9781484258873; 1484258878
• Related ISBNs: 9781484258866
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.