УДК 004.932 М.В. ХАРИНОВ, И.Г. ХАНЫКОВ ОПТИМИЗАЦИЯ КУСОЧНО-ПОСТОЯННОГО ПРИБЛИЖЕНИЯ СЕГМЕНТИРОВАННОГО ИЗОБРАЖЕНИЯХаринов М.В., Ханыков И.Г. Оптимизация кусочно-постоянного приближения сегментированного изображения. Аннотация. В статье анализируется проблема сегментации цветового изображения, аппроксимируемого кусочно-постоянными приближениями. Качество сегментации оце-нивается по классическому среднеквадратичному отклонению (СКО) пикселей прибли-жения от пикселей изображения. Обсуждаются современные версии классических мето-дов кластеризации пикселей изображения посредством минимизации СКО или суммар-ной квадратичной ошибки. Описываются четыре основные операции с кластерами пик-селей и критерии их выполнения для построения оптимизированных приближений. Предлагаются варианты алгоритма преобразования приближения изображения, которые при неизменном числе сегментов обеспечивают оптимизацию приближения как по СКО, так и по зрительному восприятию. Ключевые слова: кластеры пикселей, сегменты изображения, кусочно-постоянное приближение, оценка качества, оптимизация, среднеквадратическое отклонение. Khariniv M.V., Khanykov I.G. Optimization of Piecewise Constant Approximation forSegmented Image. Abstract. In this paper а problem of segmentation of the color image, approached by piecewise constant approximations, is analyzed. The quality of the optimization is estimated by the classical standard deviation of image pixels from the pixels of approximations. The modern versions of the classical methods of image simulating by piecewise constant approximations characterized by minimal values of standard deviation or total squared error are detailed. Four main operations over pixel clusters and appropriate working criterions for the optimized approximation generating are discussed. The algorithmic versions of approximation transformation, providing the enhancement of approximation by standard deviation and also by visual perception for the given number of segments are proposed.
This paper considers the issues of image fusion in a spatially distributed small-size on-board location system for operational monitoring. The purpose of this research is to develop a new method for the formation of fused images of the land surface based on data obtained from optical and radar devices operated from two-position spatially distributed systems of small aircraft, including unmanned aerial vehicles. The advantages of the method for integrating information from radar and optical information-measuring systems are justified. The combined approach allows removing the limitations of each separate system. The practicality of choosing the integration of information from several widely used variants of heterogeneous sources is shown. An iterative approach is used in the method for combining multi-angle location images. This approach improves the quality of synthesis and increases the accuracy of integration, as well as improves the information content and reliability of the final fused image by using the pixel clustering algorithm, which produces many partitions into clusters. The search for reference points on isolated contours is carried out on a pair of left and right images of the docked image from the selected partition. For these reference points, a functional transformation is determined. Having applied it to the original multi-angle heterogeneous images, the degree of correlation of the fused image is assessed. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the quality assessment of the fusion becomes acceptable. The type of functional transformation is selected based on clustered images and then applied to the original multi-angle heterogeneous images. This process is repeated for clustered images with greater granularity in case if quality assessment of the fusion is considered to be poor. At each iteration, there is a search for pairs of points of the contour of the isolated areas. Areas are isolated with the use of two image segmentation methods. Experiments on the formation of fused images are presented. The result of the research is the proposed method for integrating information obtained from a two-position airborne small-sized radar system and an optical location system. The implemented method can improve the information content, quality, and reliability of the finally established fused image of the land surface.
The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1,2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Ward’s and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined “semantic” segmentation.
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