Book Description: Building Computer Vision Applications Using Artificial Neural Networks With Examples in OpenCV and TensorFlow with Python 2nd Edition is a comprehensive guide to creating computer vision applications using artificial neural networks (ANNs) with OpenCV and TensorFlow with Python. This book covers the fundamentals of ANNs, computer vision, and deep learning, as well as practical examples and projects that demonstrate how to apply these concepts in real-world scenarios. The second edition includes new chapters on object detection, segmentation, tracking, and facial recognition, as well as updated code examples and exercises. The book begins by introducing the basics of ANNs and their role in computer vision, followed by an overview of OpenCV and TensorFlow, two popular libraries used in computer vision development. It then delves into the details of ANN architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and feedforward neural networks (FNNs), as well as their applications in image processing, object recognition, and feature extraction. The author provides step-by-step instructions for implementing ANNs in computer vision tasks such as edge detection, image compression, and image denoising, as well as more advanced topics such as facial recognition, object tracking, and scene understanding. Each chapter includes exercises and projects to help readers reinforce their understanding of the concepts presented.

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