Handbook of Texture Analysis: Generalized Texture for AI-Based Industrial Applications [Paperback]

This book examines four major application domains related to texture analysis and their relationship to AI-based industrial applications: texture classification, texture segmentation, shape from texture, and texture synthesis.?

1 Texture Analysis in Neuroradiology  2 Information Theoretic Entropy Approaches and Their Applications to Texture Analysis  3 Texture Analysis in Chronic Liver Diseases  4 Role of Texture Analysis in the Clinical Management of Focal Liver Lesions  5 Texture Analysis in Abdominal Imaging 6 Texture Modeling in Optical Coherence Tomography Images  7 Texture Analysis in Thoracic Imaging  8 Applications of Texture Analysis in Prostate Cancer  9 Texture Analysis for Breast Ultrasound Using Conventional Method and Deep Learning  10 Texture Analysis and Machine Learning on MRI for the Quality Evaluation of Meat Products  11 Comparison of Image Processing Techniques with Supervised Machine Learning vs. Deep Learning Based on Texture Analysis to Detect Powdery Mildew on Strawberry Leaves  12 A Radiomic FeaturesBased Pipeline for Accurate Bladder Cancer Staging 

The major goals of texture research in computer vision are to understand, model, and process texture, and ultimately, to simulate the human visual learning process using computer technologies. In the last decade, artificial intelligence has been revolutionized by machine learning and big data approaches, outperforming human prediction on a wide range of problems. In particular, deep learning convolutional neural networks (CNNs) are particularly well suited to texture analysis.

This book examines foul£i