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Building Computer Vision Applications Using Artificial Neural Networks : With Step-by-Step Examples in OpenCV and TensorFlow with Python / by Shamshad Ansari

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer eBook CollectionPublisher: Berkeley, CA : Apress, 2020Publisher: Berkeley, CA : Imprint: Apress, 2020Edition: 1st ed. 2020Description: 1 Online-Ressource(XXII, 451 p. 247 illus., 201 illus. in color.)ISBN:
  • 9781484258873
Subject(s): Additional physical formats: 9781484258866 | 9781484258880 | Erscheint auch als: 9781484258866 Druck-Ausgabe | Erscheint auch als: 9781484258880 Druck-AusgabeDOI: DOI: 10.1007/978-1-4842-5887-3Online resources: Summary: 1. Chapter 1: Prerequisite and Software Installation -- Chapter 2: Core Concepts of Image and Video Processing -- Chapter 3: Techniques of Image Processing -- Chapter 4: Building Artificial Intelligence System for Computer Vision -- Chapter 5: Artificial Neural Network for Computer Vision -- Chapter 6: Practical Example 1- Object Detection in Images -- Chapter 7: Practical Example 2- Object Tracking in Videos -- Chapter 8: Practical Example 3- Facial Detection -- Chapter 9: Industrial Application - Realtime Defect Detection in Industrial Manufacturing -- Chapter 10: Training Machine Learning Model on the Cloud.Summary: 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 computer vision applications in business and industry · Train distributed models on GPU-based cloud infrastructure .PPN: PPN: 1726026620Package identifier: Produktsigel: ZDB-2-CWD | ZDB-2-SEB | ZDB-2-SXPC
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