Neural Network Computer Vision with OpenCV 5: Build computer vision solutions using Python and DNN module

Description

Unlocking computer vision with Python and OpenCV. Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition. This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, Deep Learning and CNNs by using Deep Learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples. You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems. Whether you are building intelligent systems, automating processes, or working on image-related projects, this book equips you with the skills to revolutionize your approach to visual data. Deep Learning has revolutionized the field of Artificial Intelligence, enabling remarkable progress in areas such as computer vision, natural language processing, and machine translation. This chapter explores the multifaceted landscape of Deep Learning. Moreover, it investigates various architectural approaches, such as convolutional neural networks (CNNs), elucidating their mathematical foundations, strengths, and applications. Furthermore, the chapter introduces training and inference processes in Deep Learning, focusing on techniques for efficient and accurate predictions. It highlights the significance of optimization functions, activation functions, and model compression techniques in enhancing inference speed, reducing computational requirements, and ensuring robustness. What you will learn: - Acquire expertise in image manipulation techniques. - Apply knowledge to practical scenarios in computer vision. - Implement robust systems for face detection and recognition. - Enhance projects with accurate object localization capabilities. - Extract text information from images effectively. Who this book is for: This book is designed for those with basic Python skills, from beginners to intermediate-level readers. Whether you are building intelligent robots that perceive their surroundings or crafting advanced vision systems for object detection and image analysis, this book will equip you with the tools and skills to push the boundaries of AI perception.