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Building computer vision applications using artificial neural networks : with step-by-step Eeamples in OpenCV and TensorFlow with Python /
Apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach. The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berkeley, CA :
Apress,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 037 | |a com.springer.onix.9781484258873 |b Springer Nature | ||
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| 082 | 0 | 4 | |a 006.3/7 |2 23 |
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| 100 | 1 | |a Ansari, Shamshad. | |
| 245 | 1 | 0 | |a Building computer vision applications using artificial neural networks : |b with step-by-step Eeamples in OpenCV and TensorFlow with Python / |c Shamshad Ansari. |
| 260 | |a Berkeley, CA : |b Apress, |c 2020. | ||
| 300 | |a 1 online resource (467 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
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| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Optimization Algorithms | ||
| 500 | |a Includes index. | ||
| 520 | |a Apply computer vision and machine learning concepts in developing business and industrial applications using a practical, step-by-step approach. The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section. Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing. The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning. You will: · Employ image processing, manipulation, and feature extraction techniques · Work with various deep learning algorithms for computer vision · Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO · Build neural network models using Keras and TensorFlow · Discover best practices when implementing comp uter vision applications in business and industry · Train distributed models on GPU-based cloud infrastructure. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Neural networks (Computer science) | |
| 650 | 2 | |a Neural Networks, Computer | |
| 650 | 6 | |a Vision par ordinateur. | |
| 650 | 6 | |a Réseaux neuronaux (Informatique) | |
| 650 | 7 | |a Neural networks (Computer science) |2 fast |0 (OCoLC)fst01036260 | |
| 650 | 7 | |a Computer vision. |2 fast |0 (OCoLC)fst00872687 | |
| 650 | 7 | |a Computer programming. |2 fast |0 (OCoLC)fst00872390 | |
| 650 | 7 | |a Machine learning. |2 fast |0 (OCoLC)fst01004795 | |
| 650 | 7 | |a Open source software. |2 fast |0 (OCoLC)fst01046097 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 776 | 0 | 8 | |i Print version: |a Ansari, Shamshad. |t Building Computer Vision Applications Using Artificial Neural Networks : With Step-By-Step Examples in OpenCV and TensorFlow with Python. |d Berkeley, CA : Apress L.P., ©2020 |z 9781484258866 |
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