{"id":164375,"date":"2013-02-01T00:00:00","date_gmt":"2013-02-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/decision-forests-for-computer-vision-and-medical-image-analysis\/"},"modified":"2018-10-16T20:48:12","modified_gmt":"2018-10-17T03:48:12","slug":"decision-forests-for-computer-vision-and-medical-image-analysis","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/decision-forests-for-computer-vision-and-medical-image-analysis\/","title":{"rendered":"Decision Forests for Computer Vision and Medical Image Analysis"},"content":{"rendered":"
\n

Decision forests (also known as random forests) are an indispensable tool for automatic image analysis.<\/p>\n

This practical and easy-to-follow text explores the theoretical underpinnings of decision forests, organizing the vast existing literature on the field within a new, general-purpose forest model. A number of exercises encourage the reader to practice their skills with the aid of the provided free software library. An international selection of leading researchers from both academia and industry then contribute their own perspectives on the use of decision forests in real-world applications such as pedestrian tracking, human body pose estimation, pixel-wise semantic segmentation of images and videos, automatic parsing of medical 3D scans, and detection of tumors. The book concludes with a detailed discussion on the efficient implementation of decision forests.<\/p>\n

Topics and features:<\/p>\n