This paper proposes an adaptive method of detecting objects on the image of an optoelectronic device. The method is based on reconstructing a reference signal-image and forming a statistic in the form of the maximum eigenvalue of the selective correlation matrix for making a decision concerning the detection of an object, using the Neyman-Pearson criterion. The information contained in the images recorded is used when there are no a priori data concerning the background-target situation. A block diagram of the algorithm is given, along with the results of estimating the efficiency index for detecting objects under various conditions.
A criterion and an algorithm of detecting dynamic objects (DOs) in a complex background formed by an intense cumulus and high-altitude cumulus are proposed. The object image has a small size (point image) and low contrast. The principle of DO detection is fractal-correlation: it is based on the use of sampling as a relationship of likelihood functions of similar alternative conditions: either "only complex background within the sight of an optoelectron device (OED)" or "DO on the complex background within the sight of an OED." The DO detection algorithm is designed as a binary accumulator according to the most powerful local criterion. The critical limit of decision making is defined by the Neumann -Pearson lemma for the acceptable possibility of false detection of a DO. Simulation proves the algorithm to be highly effective.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.