Decision Forests for Computer Vision and Medical Image Analysis

• Overview and Scope.- Notation and Terminology.- Part I: The Decision Forest Model.- Introduction.- Classification Forests.- Regression Forests.- Density Forests.- Manifold Forests.- Semi-Supervised Classification Forests.- Part II: Applications in Computer Vision and Medical Image Analysis.- Keypoint Recognition Using Random Forests and Random Ferns.- Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval.- Class-Specific Hough Forests for Object Detection.- Hough-Based Tracking of Deformable Objects.- Efficient Human Pose Estimation from Single Depth Images.- Anatomy Detection and Localization in 3D Medical Images.- Semantic Texton Forests for Image Categorization and Segmentation.- Semi-Supervised Video Segmentation Using Decision Forests.- Classification Forests for Semantic Segmentation of Brain Lesions in Multi-Channel MRI.- Manifold Forests for Multi-Modality Classification of Alzheimer’s Disease.- Entangled Forests and Differentiable Information Gain Maximization.- Decision Tree Fields.- Part III: Implementation and Conclusion.- Efficient Implementation of Decision Forests.- The Sherwood Software Library.- Conclusions.

From the reviews:“This book is a comprehensive presentation of the theory and use of decision forests in a wide range of applications, centered on computer vision and medical imaging. The book is strikingly well integrated. … This is an excellent volume on the concept, theory, and application of decision forests. … I highly recommend it to those currently working in the field, as well as researchers desiring an introduction to the application of random forests for imaging applications.” (Creed Jones, Computing Reviews, March, 2014)