Document-analysis systems and techniques. Agraphics-recognition system for interpretation of line drawings. Automation recognition of engineering drawings and maps. Image-analysis techniques for geographic information systems. Digital image processing and tree-dimensional reconstruction in the basic neurociences. Applying digital processing methods in the analysis of retinal structure. Visual percepition using a Blackbord architeture. Analysis of high-resolution aerial images. Image formation and characterization for tree-dimensional vision. Enhancement of fingerprints using digital and optical techniques. The digital morphological sampling theorem.

Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phase based (or frequency domain) methods; correlation based (or area) methods; feature point (or sparse data) tracking methods; In this chapter we compute the optic flow as a dense optic flow field with a multi scale differential method. The method, originally proposed by Florack and Nielsen [Florack1998a] is known as the Multiscale Optic Flow Constrain Equation (MOFCE). This is a scale space version of the well known computer vision implementation of the optic flow constraint equation, as originally proposed by Horn and Schunck [Horn1981]. This scale space variation, as usual, consists of the introduction of the aperture of the observation in the process. The application to stereo has been described by Maas et al. [Maas 1995a, Maas 1996a]. Of course, difficulties arise when structure emerges or disappears, such as with occlusion, cloud formation etc. Then knowledge is needed about the processes and objects involved. In this chapter we focus on the scale space approach to the local measurement of optic flow, as we may expect the visual front end to do. 17. 2 Motion detection with pairs of receptive fields As a biologically motivated start, we begin with discussing some neurophysiological findings in the visual system with respect to motion detection.

Minimizing theoretical background and mathematical formalism, Image Analysis sets forth the basic principles of image acquisition, enhancement, measurements, and interpretation in a very simple form, focused on applications and the properties of available tools. The author lists different tasks and offers complete solutions to these tasks based on his extensive experience with the procedures described. The book provides a multitude of illustrations, drawings, and bitmaps and serves as an outstanding resource for specialists using image analysis equipment, including technicians and students.

This book is a comprehensive treatise on the theory and applications of moment functions in image analysis. Moment functions are widely used in various realms of computer vision and image processing. Numerous algorithms and techniques have been developed using image moments, in the areas of pattern recognition, object identification, three-dimensional object pose estimation, robot sensing, image coding and reconstruction. This book provides a compilation of the theoretical aspects related to different types of moment functions, and their applications in the above areas.The book is organized into two parts. The first part discusses the fundamental concepts behind important moments such as geometric moments, complex moments, Legendre moments, Zernike moments, and moment tensors. Most of the commonly used properties of moment functions and the mathematical framework for the derivation of basic theorems and results are discussed in detail. This includes the derivation of moment invariants, implementation aspects of moments, transform properties, and fast methods for computing the moment functions for both binary and gray-level images. The second part presents the key application areas of moments such as pattern recognition, object identification, image-based pose estimation, edge detection, clustering, segmentation, coding and reconstruction. Important algorithms in each of these areas are discussed. A comprehensive list of bibliographical references on image moments is also included.

The challenge behind the processing of digital images is the huge amounts of data that has to be processed in an extremely short period of time. This book is a broad-ranging technical survey of computational and analytical methods and tools for digital image analysis and interpretation. The ultimate goal is to create a rich set of computational methods for image analysis and interpretation that can achieve rapid response times. This book will serve as an excellent up-to-date resource for computer scientists and engineers in digital imaging and analysis.

Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.