Book Image

Python Image Processing Cookbook

By : Sandipan Dey
Book Image

Python Image Processing Cookbook

By: Sandipan Dey

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.
Table of Contents (11 chapters)

Object detection with Faster R-CNN

As discussed in a previous Chapter 7, Image Segmentation, in the Deep instance segmentation recipe, region-based object detection methods (for example, R-CNN and Fast R-CNN) rely on region proposal algorithms (selective search) to guess object locations. Faster R-CNN is yet another region-based object detection model that was proposed as an improvement on R-CNN (2013) and Fast R-CNN (2015), by Girshick et al. again. Fast R-CNN decreases the execution time of detection (for example, for the slower R-CNN model) by introducing ROI Pooling, but still, region proposal computation becomes a bottleneck. Faster R-CNN introduces a Region Proposal Network (RPN). It achieves almost cost-free region proposals by sharing convolutional features with the detection network.

A Region Proposal Network (RPN) is an FCN that predicts regions that potentially contain...