Jianming Zhang – författare

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 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.

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 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.

This book explores the rapidly advancing field of cellulose-based hydrogels and aerogels, highlighting their immense potential for sustainable applications in materials science, nanotechnology, biomedicine, tissue engineering, energy, and environmental science. With their highly porous structure, eco-friendly nature, excellent biocompatibility, and biodegradability, these materials offer remarkable versatility for next-generation sustainable technologies.This book presents cutting-edge research on the design, synthesis, and functionalization of cellulose-based hydrogels and aerogels. It demonstrates how their properties can be fine-tuned through integration with organic and inorganic materials and examines their wide-ranging applications in sustainable technologies. Despite their promise, challenges remain in achieving large-scale production with enhanced mechanical properties, controllable modifications, and functionalization for diverse applications. To address these issues, researchers have developed innovative strategies to refine synthesis methods, improve functionality, and expand potential uses.A valuable resource for researchers, postdoctoral scholars, PhD students, and engineers, this book serves as a comprehensive guide to the latest advancements and future directions in this growing field.