The article proposes an approach to the determination of small-form objects against a complex background. The proposed approach uses a parallel data processing algorithm that includes the following main modules: a multi-criteria image filtering block built on an objective function that minimizes the weighted average sum of the average square of the first order finite difference, as well as the average square of the distance difference between the input implementation and the generated data; parallel separation of objects by analyzing local features, statistical analysis of histogram changes, building a mask of object detailing and frequency analysis; the formation of a feature mask and the search for similarity elements by analyzing the generated features. On the test data set, an example of determining small-sized objects on a complex background with their subsequent classification into class objects is presented. The data were obtained by a machine vision system installed on a robotic complex. Data on the required parameters of the formed machine vision systems are given, recommendations on the required parameters of the algorithms are presented.
The article proposes a fusion technique and an algorithm for combining images recorded in the IR and visible spectrum in relation to the problem of processing products by robotic complexes in dust and fog. Primary data processing is based on the use of a multi-criteria processing with complex data analysis and cross-change of the filtration coefficient for different types of data. The search for base points is based on the application of the technique of reducing the range of clusters (image simplification) and searching for transition boundaries using the approach of determining the slope of the function in local areas. As test data used to evaluate the effectiveness, pairs of test images obtained by sensors with a resolution of 1024x768 (8 bit, color image, visible range) and 640x480 (8 bit, color, IR image) are used. Images of simple shapes are used as analyzed objects.
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