Torrent details for "Shih F. AI Deep Learning in Image Processing 2026" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
43.1 MB
Info Hash:
6256B1899840F529DB866215D3FB76785E1F3C51
Added By:
Added:
Oct. 8, 2025, 10:03 a.m.
Stats:
|
(Last updated: Oct. 8, 2025, 10:04 a.m.)
| File | Size |
|---|---|
| Shih F. AI Deep Learning in Image Processing 2026.pdf | 43.1 MB |
Name
DL
Uploader
Size
S/L
Added
-
222.5 MB
[12
/
38]
2023-09-21
| Uploaded by indexFroggy | Size 222.5 MB | Health [ 12 /38 ] | Added 2023-09-21 |
NOTE
SOURCE: Shih F. AI Deep Learning in Image Processing 2026
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format Image processing plays a crucial role in various fields, including digital multimedia, automated vision detection and inspection, and pattern recognition. AI Deep Learning in Image Processing aims to provide a comprehensive overview of the mechanisms and techniques involved, with a particular focus on the application of advanced AI Deep Learning technologies in image processing. The field of image processing has experienced unprecedented growth in recent years. Image processing presents the latest state-of-the-art developments alongside clear explanations of fundamental concepts and contemporary applications. By emphasizing essential principles, this book enables readers to not only implement algorithms and techniques with ease but also identify new challenges and explore innovative applications in the field. The training procedure is implemented in Python, Numpy, and Pytorch. New and advanced AI deep-learning techniques for image processing as comparing against traditional image-processing methods Numerous practical examples and AI image-processing-related applications A more intuitive development and clear explanation to the complex technology Updated image-processing technology in medical, chemical, and ecological fields Extensive discussions of performance comparisons of various AI deep-learning image-processing methods This book is designed for students, researchers, and professionals seeking to enhance their knowledge, gain practical insights, and explore the evolving role of image processing in modern technology. Preface Fundamentals of Image Processing Introduction Image Enhancement Mathematical Morphology Image Segmentation Image Representation and Description Feature Extraction Fundamentals of AI Deep Learning Pattern Recognition Deep Learning Image Processing by Deep Learning Development of Deep-Learning Framework for Mathematical Morphology Deep Morphological Neural Networks Practical Applications A Robust and Blind Image Watermarking System Based on Deep Neural Networks Deep Learning Classification on Optical Coherence Tomography Retina Images Classification of Ecological Data by Deep Learning Joint Learning for Pneumonia Classification and Segmentation on Medical Images Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural Networks Land-Cover Image Segmentation Based on Individual Class Binary Masks FPA-Net: Frequency-Guided Position-Based Attention Network for Land-Cover Image Segmentation Defense against Adversarial Attacks Based on Stochastic Descent Sign Activation Networks on Medical Images Adaptive Image Reconstruction for Defense against Adversarial Attacks A Novel Multi-Data-Augmentation and Multi-Deep-Learning Framework for Counting Small Vehicles and Crowds Drug Toxicity Prediction by Machine-Learning Approaches An Efficient Detection and Recognition System for Multiple Motorcycle License Plates Based on Decision Tree The Deep Hybrid Neural Network and an Application on Polyp Detection BFC-Cap: Background and Frequency-Guided Contextual Image Captioning A Novel Adaptive Data Transformation for Contrastive Learning
×