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.
This paper presents a new method for video segmentation using deep learning neural networks in the quaternion space into sets of objects, background, static and dynamic textures. We introduce a novel quaternionic anisotropic gradient (QAG) which can combine the color channels and the orientations in the image plane. The local polynomial estimates and the ICI rule are used for QAG calculation. Since for segmentation tasks, the image is usually converted to grayscale, this leads to the loss of important information about color, saturation, and other important information associated color. To solve this problem, we use the quaternion framework to represent a color image to consider all three channels simultaneously when segmenting the RGB image. Using the QAGs, we extract the local orientation information in the color images. Second, to improve the segmentation result we applied neural network to this derived orientation information. The presented new approach allows obtaining clearer and more detailed boundaries of objects of interest. Experimental comparisons to state-of-the-art video segmentation methods demonstrate the effectiveness of the proposed approach.
The paper proposes a solution to the problem of interpolation of the step function of displacement, obtained from the movement's formation by simple systems of object analysis. The analysis of the motion curve is carried out, taking into account the transformation of data into Cartesian coordinate systems and the processing of 2D signals. A multicriteria objective function is used as an interpolation method. This approach is based on solving the problem of minimizing the functional simultaneously according to three criteria. The first criterion is the mean square of the measure of the discrepancy between the input values and those obtained due to minimization. This criterion is to set the degree of approximation to the input data. As the second criterion, the function of the root-mean-square spread of the neighboring elements of the obtained values is used. This criterion allows you to minimize the scatter of data and set the smoothness of the function. As the third criterion, the root-mean-square functional between adjacent elements of the second group is used. This criterion allows one to increase the degree of smoothness of the function and the rate of convergence. The weighting function is adjusted using weighting factors. The paper provides recommendations on choosing these values, and the diagrams show the rationale for this choice. The graphs showing the effect of the rate of convergence of the results on the degree of smoothness of the function and the selected parameters of the method are presented. The graphs of the tool exit speed to the working point and the calculation of the path lengths are given. Examples of plotting the curves of functions obtained by machine vision systems located on robotic portal complexes are presented on test data sets. Data obtained in the visible range, with a resolution of 1280x1024 pixels, are presented in grayscale.
To establish stable video operations and services while maintaining high quality of experience, perceptual video quality assessment becomes an essential research topic in video technology. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. The paper presents a novel visual quality metric for video quality assessment. To address this problem, we study the of neural networks through the robust optimization. High degree of correlation with subjective estimations of quality is due to using of a convolutional neural network trained on a large amount of pairs video sequence-subjective quality score. We demonstrate how our predicted no-reference quality metric correlates with qualitative opinion in a human observer study. Results are shown on the MCL-V dataset with comparison existing approaches.
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