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: Summary of theoretic
findings and analysis of Boolean map distance Theoretic algorithmic analysis Applications in
salient object detection and eye fixation prediction. 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.
