MASTERING COMPUTER VISION WITH TENSORFLOW AND OPENCV: A Beginner's Guide to Building Image and Video Processing Applications with Deep Learning for Real-World Projects (GAVIN KENT TECH)

By GAVIN H KENT

MASTERING COMPUTER VISION WITH TENSORFLOW AND OPENCV is your complete guide to building intelligent, real-time visual systems that see and understand the world. Whether you're a developer, AI enthusiast, or aspiring machine learning engineer, this book takes you from the fundamentals to advanced techniques using two of the most powerful tools in the field: TensorFlow and OpenCV.You'll begin with the core concepts of image processing and visual recognition, using OpenCV to manipulate images, detect features, and analyze scenes. Then, you'll dive into TensorFlow to build and train deep learning models for tasks like image classification, object detection, face recognition, and image segmentation. Each chapter combines clear explanations with practical code examples and hands-on projects. You'll learn how to integrate traditional computer vision techniques with modern AI models, optimize your pipelines, and deploy solutions that work in real-world environments--from smart cameras to mobile devices. You'll explore topics such as: Image preprocessing and augmentation Building and training Convolutional Neural Networks (CNNs) Real-time object detection with YOLO and SSD Face and emotion recognition systems Using pre-trained models and transfer learning Video analysis and gesture tracking Whether you're creating smart security systems, medical imaging tools, or the next big AR app, this book equips you with the skills to bring your vision to life--literally. Don't just process pixels--build intelligent vision systems. Grab your copy of Mastering Computer Vision with TensorFlow and OpenCV today and start creating the future of visual AI.

• Format: Paperback
• Language: English
• ISBN: B0F5X1HMKH
• ISBN13: 9798280927124
• Release Date: April 2025
• Publisher: Independently published
• Length: 336 Pages
• Dimensions: 9.0" x 0.8" x 6.0"