O SELECȚIE URIAȘĂ
Peste 4 milioane de cărți în engleză la prețuri avantajoase.
ISBN | 9783030048303 |
---|---|
Autor | Zhang Jianming |
Editura | Springer Nature |
Limba | english |
Tip copertă | Paperback |
Anul publicării | 2019 |
Număr de pagini | 138 |
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
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
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.