Aerial Object Recognition Algorithm Based on Contour Descriptor

Strotov, Valery V.; Babyan, P. V.; Smirnov, Sergey

doi:10.5194/isprs-archives-xlii-2-w4-91-2017

Cited by 2 publications

(1 citation statement)

References 7 publications

(4 reference statements)

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“…Classical image processing methods meet these requirements well [10]. In [11] the use of these methods to recognize types of air objects is investigated in detail.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

Development of a Video Processing Module for the Task of Air Object Recognition Based on Their Contours

Yesilevskyi

Koliadin²,

Sereda³

2022

ITSSI

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The subject of research in the article is the module of automatic segmentation and subtraction of the background, which is created, based on the sequential application of methods of image preprocessing and modified method of interactive segmentation of images and implemented in the system of optical monitoring of the air situation. The aim of the work is to develop an image segmentation module to increase the efficiency of recognition of an air object type on a video image in the system of visual monitoring of the air environment by means of qualitative automatic segmentation. To solve this problem, a modified interactive algorithm in the mode of automatic selection of an object in the image, which allows more accurately, without the participation of the operator, to determine the foreground pixels of the image for further recognition of the type of airborne object. The following tasks are solved in the article: the analysis of existing methods of binarization of color images for semantic segmentation of images, which are used in image recognition systems; the development of a pipeline of methods for automatic segmentation of images in the system of optical monitoring of the air environment. In the work, the following methods are used: methods of digital image processing, methods of filtering and semantic segmentation of images, methods of graph analysis. The following results are obtained: the results of image processing with the proposed module of segmentation and background subtraction confirm the performance of the module procedures. The developed pipeline of methods included in the module demonstrates correct segmentation in 93% of test images in automatic mode without operator participation, which allows us to conclude about the effectiveness of the proposed module. Conclusions: The implementation of the developed module of segmentation and background subtraction for the system of optical monitoring of the air environment allowed to solve the problem of segmentation of video images for further recognition of aerial objects in the system of optical monitoring of the air environment in automatic mode with a high degree of reliability, thus increasing the operational efficiency of this system.

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“…Classical image processing methods meet these requirements well [10]. In [11] the use of these methods to recognize types of air objects is investigated in detail.…”

Section: Analysis Of the Problem And Existing Methodsmentioning

confidence: 99%

Development of a Video Processing Module for the Task of Air Object Recognition Based on Their Contours

Yesilevskyi

Koliadin²,

Sereda³

2022

ITSSI

View full text Add to dashboard Cite

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A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network

Yesilevskyi

Tevyashev

Колядин

2020

EEJET

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This paper reports a study into the methods for recog nizing the type of an air object on a digital image acquired from an air situation video monitoring system. A method has been proposed that is based on the application of a specific neural network, which solves the problem of ca te gorizing multidimensional complex vectors of objects' fea tures based on complex calculations. In this case, a feature vector for recognizing the type of an air object is built on the basis of a Fourier transform for the sequence of coordi nates of its twodimensional contour. A technique has been proposed to train a neural network to recognize the type of an air object based on three image classes correspond ing to three projections. This makes it easier to solve the classification problem owing to a more compact arrange ment of the multidimensional feature vectors. The architec ture of an air situation video monitoring system has been suggested, which includes an image preprocessing module and a module of a complexvalued neural network. Pre processing makes it possible to identify an object's contour and build a sequence of normalized descriptors, which are partially independent of the spatial position of the object and the contour processing technique. Existing methods of air object recognition require significant computational resources and do not take into consideration the specificity of recognizing objects with three degrees of freedom or do not account for the complex nature of the numerical rep resentation of a contour. This study has shown that the reported results make it easier to train a neural network and reduce the hardware requirements in order to solve the task of air situation video monitoring. The proposed solu tion leads to increased mobility and extends the scope of application of such systems, including individual devices

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Aerial Object Recognition Algorithm Based on Contour Descriptor

Cited by 2 publications

References 7 publications

Development of a Video Processing Module for the Task of Air Object Recognition Based on Their Contours

Development of a Video Processing Module for the Task of Air Object Recognition Based on Their Contours

A method of air object recognition based on the normalized contour descriptors and a complex-valued neural network

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