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.