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

Offers a comprehensive guide to computer vision (CV), primarily focusing on its implementation using artificial neural networks (ANNs) and deep learning (DL). The text covers foundational image processing techniques such as resizing, translation, rotation, flipping, cropping, and arithmetic/bitwise operations. It progresses to explain machine learning (ML) principles, including feature extraction (histograms, GLCM, HOGs, LBP), feature selection (filter, wrapper, embedded methods), and model training (supervised and unsupervised learning), with an emphasis on TensorFlow for coding examples. Furthermore, the source provides practical examples of object detection using SSD and YOLOv3, object tracking in videos, and face recognition with FaceNet, alongside detailed instructions for distributed model training on cloud platforms like Google Cloud and Azure.

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