This book provides an introduction to recent advances in theory, algorithms and application of Boolean map distance for image processing. Applications include modeling what humans find salient or prominent in an image, and then using this for guiding smart image cropping, selective image filtering, image segmentation, image matting, etc.
In this book, the authors present methods for both traditional and emerging saliency computation tasks, ranging from classical low-level tasks like pixel-level saliency detection to object-level tasks such as subitizing and salient object detection. For low-level tasks, the authors focus on pixel-level image processing approaches based on efficient distance transform. For object-level tasks, the authors propose data-driven methods using deep convolutional neural networks. The book includes both empirical and theoretical studies, together with implementation details of the proposed methods. Below are the key features for different types of readers.
For computer vision and image processing practitioners:
Efficient algorithms based on image distance transforms for two pixel-level saliency tasks;
Promising deep learning techniques for two novel object-level saliency tasks;
Deep neural network model pre-training with synthetic data;
Thorough deep model analysis including useful visualization techniques and generalization tests;
Fully reproducible with code, models and datasets available.
For researchers interested in the intersection between digital topological theories and computer vision problems:
Students majoring in image processing, machine learning and computer vision:
This book provides up-to-date supplementary reading material for course topics like connectivity based image processing, deep learning for image processing;
Some easy-to-implement algorithms for course projects with data provided (as links in the book);
Hands-on programming exercises in digital topology and deep learning.