Showing papers on "Contextual image classification published in 1980"

Context Distribution Estimation for Contextual Classification of Multispectral Image Data

Abstract: A classification algorithm incorporating contextual information in a general, statistical manner is presented. Methods are investigated for obtaining adequate estimates of the context distribution (a statistical characterization of context) upon which the classification algorithm depends. Finally, a method of estimating optimal algorithm parameters prior to performing preliminary classifications is explored.

Terrain Classification Using Texture Analysis

Abstract: This paper briefly summarizes some past work on texture analysis, including comparisonsbetween different classes of features based on spatial statistics and Fourier analysis;recently developed refinements of the spatial statistics approach; methods based on the ex-traction and description of texture primitives; texture models based on random geometricprocesses; methods of improving texture classification by feature value smoothing; andmethods of segmenting images into textured regions. References are given to reports andpapers in which further details can be found.IntroductionTexture analysis has been used extensively in many image Processing and recognition ap-plications over the past 25 years. A general review of the subject can be found in '.This paper briefly summarizes work on texture analysis, primarily at the University ofMaryland, during the past five years, and gives references to technical reports and papersin which further details can be found.Feature comparisons

Computational Efficiency Of Multiple Image Registration Algorithms

Abstract: The problem of multiple image registration is that of finding n subimages in a larger image (Search area S) which best match the n smaller images (windows or references) obtained from different sensors, assuming that all smaller images are completely located within the larger image. Although correlation and sequential similarity detection algorithms are commonly used, the method of moments which has been successfully used for automatic classification of an unknown pattern as one of several known patterns can also be used for digital image registration. This paper compares the computational efficiency of the three methods stated above for software as well as hardware implementations. For single image registration problem, the moments method requires more computation time than correlation and sequential similarity detection methods. However, for the multiple image registration problem the moments method becomes computationally more efficient as the number of windows increases. This paper shows that the moments method requires less computation time if implemented in software and less hardware for real time implementation when the number of windows is large.© (1980) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Contextual classification of multispectral image data: Approximate algorithm

Abstract: An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive Classifications that are nearly as accurate are produced

Target Recognition For Missile Guidance Using Adaptive Learning Networks

Abstract: The Adaptive Learning Network Synthesis methodology has been used to implement an image classification algorithm for infrared images. Using features extracted from transforms of the original image, the algorithm achieves range and aspect angle independent separation of images that contain a specific target (a tank) from images that do not contain the target. A ROC analysis of the algorithm, using 385 sample images, shows >95% detection rate, <5% false alarm rate, and a small (<1%) false dismissal rate.