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

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

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