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 to control of algorithm for the process of forming a coating with an increased content of an oxide layer resulting from the application of plasma formation of surface films. An implementation of an algorithm for adaptive determination of the contours of plasma discharge boundaries during the formation of films of memristor structures is proposed. The construction of the algorithm is based on the use of a multicriteria data processing method in the function of the boundary detector. An implementation of an adaptive change in the contact mask of the plasma discharge with the surface is proposed. Analysis of the contact size and density influences the shape and rate of formation of the oxide layer. The appearance of such a coating has the ability, when exposed to current, to form a complex curve of a function of a given shape. With the subsequent application of voltage, it can be used as an activation function. Recommendations on control and changes influences are presented. A hardware model implementation of an artificial neuron based on blocks of digital elements is presented. Examples of solving the problem of predicting the movement of an actuating element in the control of robotic complexes based on the formed neurons are given.
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 article proposes an approach that allows merging a series of images obtained using an electron microscope and fusion data with a camera that records data in the far-infrared spectrum (thermal images). The proposed approach is implemented based on algorithms for stitching images obtained in the visible spectrum. Based on the data on the calibration parameters of the pair cameras, the images obtained by the thermal imaging camera are stitched. The frame overlap is between 30% and 50%. The data received by the thermal imaging camera is noisy and requires primary data processing. To filter thermal imaging images, a multi-criteria method is used in work. The parameters of the method are analyzed in parallel on both pairs of the image. They are used for the subsequent identification of the search boundaries of objects and interframe communication points. For data obtained in the visible spectrum, a simplification algorithm is applied to increase data analysis speed. As test data, we used a combination of images obtained by an electron microscope (a maximum approximation is 300x, the color depth is 8bit, the resolution is 1024x768) and a thermal imaging camera (image resolution is 320x240 pixels).
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