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The appearance of "blurred" digital images is a consequence of the violation of the immobility of the camera during the shooting of the objects under study. To this end, a procedure was devised for matched filtering of the blurred digital image of the object using its typical image form in a series of frames. This procedure is based on the automated formation of a typical form of a digital image, as well as on the choice of special parameters for the transfer function of the matched filter. Adapting the procedure specifically to the typical form makes it possible to perform a more accurate assessment of the required parameters of the blurred digital image compared to the analytically set profile. The formation of a typical form makes it possible to take into account the features of the very formation of the blurred image on each frame of the original series. Based on this, a more accurate assessment of the initial approximation of the parameters of all Gaussians of the object image is performed. In practice, matched filtering makes it possible to highlight blurred images of objects against the background of substrate noise. Also, using the matched filtering procedure makes it possible to improve the segmentation of images of reference objects and reduce the number of false detections. The devised procedure for the matched filtering of a blurred digital image using its typical form has been tested in practice as part of the research in the framework of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software for the automated detection of new and tracking of known objects. Owing to the use of Lemur software and the proposed computational procedure introduced into it, more than 700,000 measurements of various objects under study were successfully processed and identified. [ABSTRACT FROM AUTHOR]

A computational method for the automated formation of a typical form of a digital image of the investigated objects on a series of digital frames has been developed. Due to the imperfection of the mounting of digital cameras, as well as their automated mounts, their immobility at shooting during exposure time can be disturbed, which leads to the formation of "blurred" images of objects of various forms. Due to such inaccuracies in the tracking of objects on digital frames, even in one series, the typical form of the image of objects can vary from frame to frame. This fact of the difference in the standard form significantly complicates the execution of various image processing tasks. In order to simplify the evaluation of the image parameters of objects in a series of digital frames, it has been proposed to use a typical image on a digital frame corresponding to the average image of objects as a model of object images. In this case, the appearance of the image of the object, its form, the distribution of brightness in the image will be determined only by the typical image. This paper proposes a computational method for the automated formation and evaluation of the typical form of the image of an object in a digital frame based on the initial data- the actual given digital frame. This computational method is based on the selection of single images of objects and the formation of their rectangular area. Next, the offset is evaluated, and the selected single images of objects are normalized to calculate the typical form of the object image. Using the method makes it possible to highlight objects against the background of noise and reduce the number of false detections. It is recommended to apply the method only in the case when the frames have defects and "blurs" during the shooting, otherwise there will be unreasonable additional computational costs. The developed computational method was successfully tested in practice within the framework of the CoLiTec project and implemented in the intraframe processing unit of the Lemur software. [ABSTRACT FROM AUTHOR]

A computational method for matched filtration with analytical profile of the blurred digital image of the investigated objects on digital frames has been developed. Such «blurred» objects can be the result of an involuntary shift of a fixed camera, an incorrect choice of the mode of guiding the telescope (diurnal or object tracking) or a failure of the diurnal tracking. This computational method is based on the analytical selection of the typical form of the object’s image, as well as on the choice of special parameters for the transfer function of the matched filter for the blurred digital image, which makes it possible to evaluate the required parameters of the blurred digital image. In addition, determining the number of Gaussians of the object’s image makes it possible to perform the most accurate assessment of the initial approximation of the parameters of their shape. Thus, matched filtration makes it possible to highlight the investigated objects with a blurred image of a typical shape against the background of substrate noise. Using the computational method of matched filtration makes it possible to improve the segmentation of images of reference objects on the frame and reduce the number of false detections. The developed computational method for matched filtration with analytical profile of the blurred digital image of the investigated objects on the frames was tested in practice as part of the research of the CoLiTec project. It was implemented in the intraframe processing unit of the Lemur software for the operational automated detection of new and observation of known objects with a weak brightness. Owing to the Lemur software using and the proposed computational method introduced into it, more than 500,000 measurements of the various investigated objects were successfully processed and identified [ABSTRACT FROM AUTHOR]