Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.
Machine Learning in Computer Vision (Computational Imaging and Vision) The goal of computer vision research is to provide computers with humanlike
perception capabilities so that they can sense the environment, understand
the sensed data, take appropriate actions, and learn from this experience in
order to enhance future performance. The field has evolved from the application
of classical pattern...
Geometry for Computer Graphics: Formulae, Examples and Proofs Geometry is the cornerstone of computer graphics and computer animation, and provides the framework and tools for solving problems in two and three dimensions. This may be in the form of describing simple shapes such as a circle, ellipse, or parabola, or complex problems such as rotating 3D objects about an arbitrary axis. Geometry for Computer... Emerging Topics in Computer Vision (IMSC Press Multimedia Series) The topics in this book were handpicked to showcase what we consider to be exciting and promising in computer vision. They are a mix of more well-known and traditional topics (such as camera calibration, multi-view geometry, and face detection), and newer ones (such as vision for special effects and tensor voting framework). All have the common...
3D Computer Vision: Efficient Methods and Applications (X.media.publishing)
This book provides an introduction to the foundations of three-dimensional computer vision and describes recent contributions to the field. Geometric methods include linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and...
Modeling of Curves and Surfaces with MATLAB®
This text on geometry modeling is devoted to a number of central geometrical topics—
graphs of functions, transformations, (non-)Euclidean geometries, curves and surfaces—
and presents some elementary methods for analytical modeling and visualization
In 1872 F. Klein proposed his Erlangen Programme in...