Overview
- This book includes efficient algorithms based on image distance transforms for two pixel-level saliency tasks as well as applications in salient object detection and eye fixation prediction.
- Also included are hands-on programming exercises in digital topology and deep learning.
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (7 chapters)
-
Pixel-Level Saliency
-
Object-Level Saliency
Keywords
About this book
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 fordifferent 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.
Authors and Affiliations
Bibliographic Information
Book Title: Visual Saliency: From Pixel-Level to Object-Level Analysis
Authors: Jianming Zhang, Filip Malmberg, Stan Sclaroff
DOI: https://doi.org/10.1007/978-3-030-04831-0
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-04830-3Published: 02 February 2019
eBook ISBN: 978-3-030-04831-0Published: 21 January 2019
Edition Number: 1
Number of Pages: VII, 138
Number of Illustrations: 3 b/w illustrations, 44 illustrations in colour
Topics: Image Processing and Computer Vision, Signal, Image and Speech Processing, Mathematics of Computing