In this chapter, we will discuss the application of a few advanced machine learning and deep learning techniques to solve a few advanced image processing problems. We will start with a face recognition problem that tries to match a set of faces detected in an image with a fixed set of known faces using deep face embedding representations. You will then learn how to use a few deep learning models to solve problems, such as the age or gender recognition of a human face and automatically colorizing a grayscale image. Another interesting problem we will look at is automatically captioning images with a deep learning model called im2txt. Finally, we will concentrate on a few techniques for image generation. In particular, we will focus on generative models in image processing (for example, a GAN, a VAE, and an RBM), which is a hot topic...
Python Image Processing Cookbook
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Python Image Processing Cookbook
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Overview of this book
With the advancements in wireless devices and mobile technology, there's increasing demand for people with digital image processing skills in order to extract useful information from the ever-growing volume of images. This book provides comprehensive coverage of the relevant tools and algorithms, and guides you through analysis and visualization for image processing.
With the help of over 60 cutting-edge recipes, you'll address common challenges in image processing and learn how to perform complex tasks such as object detection, image segmentation, and image reconstruction using large hybrid datasets. Dedicated sections will also take you through implementing various image enhancement and image restoration techniques, such as cartooning, gradient blending, and sparse dictionary learning. As you advance, you'll get to grips with face morphing and image segmentation techniques. With an emphasis on practical solutions, this book will help you apply deep learning techniques such as transfer learning and fine-tuning to solve real-world problems.
By the end of this book, you'll be proficient in utilizing the capabilities of the Python ecosystem to implement various image processing techniques effectively.
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Current Title:
Python Image Processing Cookbook
Table of Contents (11 chapters)
Preface
Image Manipulation and Transformation
Free Chapter
Image Enhancement
Image Restoration
Binary Image Processing
Image Registration
Image Segmentation
Image Classification
Object Detection in Images
Face Recognition, Image Captioning, and More
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