The solution of the problem of simplifying the images for the subsequent minimization of the image bit depth

In this paper, the approach of changing bit depth of images is considered. This type of operation is required when performing primary processing operations, identifying parameters and stitching images. The process of changing bits depth of images is performed in three stages. At each stage, the error minimization criterion is tested Result of applying the approach allows obtaining numerical region characteristics including the number of clusters, the number of minimum and maximum cluster sizes. To perform the process of minimizing some of the criteria, it is necessary to divide the image into areas. The paper presents a mathematical description of the approach, as well as flowcharts for performing operations of data processing steps. The article gives recommendations for choosing coefficients to obtain optimal minimizing parameters. The test images give an example of performing bit changes on image areas

Two approaches to solving the problem of smoothing digital signals based on a combined criterion

The paper presents a method for smoothing signals represented by a single realization of a finite-length random process, under conditions of a limited amount of a priori information about the signal function and statistical characteristics the noise component. The recommendations on the use of parameters affecting the processing speed and the efficiency of smoothing are given. Two solutions are presented to obtain the result of smoothing the signals. The efficiency results are shown for the processing of digital signals. Examples of comparison of simple methods and suggested ones are given

Solution of the Problem of Smoothing of the Signals at the Preprocessing of Thermal Images

Smoothing two-dimensional digital signals is important for a number of applications. The paper presents a mathematical method and an algorithm for smoothing two-dimensional digital signals. The method is based on minimizing the objective function using criteria of the first-order finite difference between the rows and columns of the image as a measure of distance. To estimate the parameters of the developed method, a non-iterative algorithm is used. The present study shows results of changing the smoothing filter core depending on variations in the method parameters

Solution of the Problem of Smoothing of the Signals at the Preprocessing of Thermal Images

Smoothing two-dimensional digital signals is important for a number of applications. The paper presents a mathematical method and an algorithm for smoothing two-dimensional digital signals. The method is based on minimizing the objective function using criteria of the first-order finite difference between the rows and columns of the image as a measure of distance. To estimate the parameters of the developed method, a non-iterative algorithm is used. The present study shows results of changing the smoothing filter core depending on variations in the method parameters

Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes

The development of machine vision systems is based on the analysis of visual information recorded by sensitive matrices. This information is most often distorted by the presence of interfering factors represented by a noise component. The common causes of the noise include imperfect sensors, dust and aerosols, used ADCs, electromagnetic interference, and others. The presence of these noise components reduces the quality of the subsequent analysis. To implement systems that allow operating in the presence of a noise, a new approach, which allows parallel processing of data obtained in various electromagnetic ranges, has been proposed. The primary area of application of the approach are machine vision systems used in complex robotic cells. The use of additional data obtained by a group of sensors allows the formation of arrays of usefull information that provide successfull optimization of operations. The set of test data shows the applicability of the proposed approach to combined images in machine vision systems

Medical image segmentation using modified active contour method

Image data is of major practical importance in medical informatics. Accurate segmentation of medical images largely determines the final result of image analysis, which provides significant information for 3D visualization, surgical planning and early detection of diseases. In this paper, a modified segmentation approach based on the active contour method is proposed to extract parts of bones from MRI data sets. The efficiency of the method is verified on real MRI slices. Good results are shown in comparison with existing approaches of segmentation of medical data

Fusing Data Processing in the Construction of Machine Vision Systems in Robotic Complexes

The development of machine vision systems is based on the analysis of visual information recorded by sensitive matrices. This information is most often distorted by the presence of interfering factors represented by a noise component. The common causes of the noise include imperfect sensors, dust and aerosols, used ADCs, electromagnetic interference, and others. The presence of these noise components reduces the quality of the subsequent analysis. To implement systems that allow operating in the presence of a noise, a new approach, which allows parallel processing of data obtained in various electromagnetic ranges, has been proposed. The primary area of application of the approach are machine vision systems used in complex robotic cells. The use of additional data obtained by a group of sensors allows the formation of arrays of usefull information that provide successfull optimization of operations. The set of test data shows the applicability of the proposed approach to combined images in machine vision systems

Restoration of the lost volume of bone tissue with use data of the computed tomography

The paper deals with the issue of bone tissue restoration. To assist the specialist, we developed the SmartAssistantConstructor software package. The recovery process is based on finding a mirror copy of a piece of bone tissue. The article describes the algorithm for finding the missing part of the bone. The paper shows examples of this operation

Automated visual inspection of fabric image using deep learning approach for defect detection

As a popular topic in automation, fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. The main challenge for automatically detecting fabric damage, in most cases, is the complex structure of the textile. This article presents a two-stage approach, combining novel and traditional algorithms to enhance image enhancement and defect detection. The first stage is a new combined local and global transform domain-based image enhancement algorithm using block-based alpha-rooting. In the second stage, we construct a neural network based on the modern architecture to detect fabric damage accurately. This solution allows localizing defects with higher accuracy than traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset

Metal artifact reduction on MRI images

This article examines the method of image reconstruction, which aims to restore the exposed areas on MRI images. The algorithm is based on a geometric model for patch synthesis. The lost pixels are recovered by copying pixel values from the source using a similarity criterion. We used a trained neural network to choose the “best similar” patch. Experimental results show that the proposed method outperforms widely used state-of-the-art methods