Showing papers on "Change detection published in 1984"

New likelihood test methods for change detection in image sequences

Abstract: Modeling the image as a piecewise linear gray-value function of the pixel coordinates considerably improved a change detection test based previously on a piecewise constant gray-value function. These results encouraged investigations into modeling the picture as a mosaic of patches where the gray-value function within each patch is described as a second-order bivariate polynomial of the pixel coordinates. Such a more appropriate model allowed the assumption to be made that the remaining gray-value variation within each patch can be attributed to noise related to the sensing and digitizing devices, independent of the individual image frames in a sequence. This assumption made it possible to relate the likelihood test for change detection to well-known statistical tests ( t test, F test), facilitating the determination of threshold values related to a priori confidence limits.

Matching Images Using Linear Features

Abstract: We describe techniques for matching two images or an image and a map. This operation is basic for machine vision and is needed for the tasks of object recognition, change detection, map up-dating, passive navigation, and other tasks. Our system uses line-based descriptions, and matching is accomplished by a relaxation operation which computes most similar geometrical structures. A more efficient variation, called the ``kernel'' method, is also described. We give results on complex aerial images which contain many image differences, caused by varying sun position, different seasons, and imaging environments, and also structural changes caused by man-made alterations such as new construction.

Digital Methods for Change Detection in Medical Images

Abstract: The detection and visualisation of the changes between two images is the basis or the goal of several imaging techniques. Thus, digitized subtraction angiography consists in visualizing the differences between two images obtained without and with iodine contrast, intravenously injected. In Nuclear Medicine, the comparison of two scintigraphic images of the same organ explored under varying conditions (images acquired at different times, with different tracers, after various physiological or pharmacological interventions) is a routine problem. The visual comparison of the images is often a difficult task for the following reasons: The differences can be too low to be visually identified. In certain types of images, there are normal statistical fluctuations which can mask or simulate a difference. The gray level intensities can be different in the images which must be first normalized. Therefore, it seems reasonable to use the capacities of a computer and the knowledge about the count fluctuations to perform an automated comparison of the images. Better performances than those given by the visual inspection can be expected from such a procedure. To form this comparison, it is necessary to first register the images (alignment, normalization, magnification,…) (1,2,3,4,5) and second detect the changes by analyzing the images point by point (6,7).

Image Processing For Target Detection Using Data From A Staring Mosaic Ir Sensor In Geosynchronous Orbit

Abstract: A set of multistage image processing algorithms have been developed to do change detection in image sequence analysis. These algorithms are tailored for the purpose of moving target identification (MTI). The image processing algorithms have been coupled with a simple tracking-detection algorithm. The resulting combination of processing shows very good performance in detecting targets in simulated mosaic IR images with projected probability of false alarm less than one per hundred billion frames and the probability of detection for targets within the model approaching unity.© (1984) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.