This book presents a comprehensive treatise on Riemannian geometric computations and related
statistical inferences in several computer vision problems. This edited volume includes chapter
contributions from leading figures in the field of computer vision who are applying Riemannian
geometric approaches in problems such as face recognition activity recognition object
detection biomedical image analysis and structure-from-motion. Some of the mathematical
entities that necessitate a geometric analysis include rotation matrices (e.g. in modeling
camera motion) stick figures (e.g. for activity recognition) subspace comparisons (e.g. in
face recognition) symmetric positive-definite matrices (e.g. in diffusion tensor imaging) and
function-spaces (e.g. in studying shapes of closed contours).
