IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY

Background. An analysis of the processes of image segmentation is being carried out. An original method of image segmentation using a convolutional neural network is proposed. Materials and methods. A comparative assessment of existing segmentation methods such as threshold segmentation methods: O...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	A.I. Godunov, S.T. Balanyan, P.S. Egorov
Formato:	article
Lenguaje:	EN RU
Publicado:	Penza State University Publishing House 2021
Materias:	adaptive methods threshold methods segmentation otsu's method niblack's method bernsen's method savol's method convolutional neural network Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/4724ca36bfbe461fbf58fbd8179bca2a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:4724ca36bfbe461fbf58fbd8179bca2a
record_format	dspace
spelling	oai:doaj.org-article:4724ca36bfbe461fbf58fbd8179bca2a2021-12-01T11:54:30ZIMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY10.21685/2307-4205-2021-3-82307-4205https://doaj.org/article/4724ca36bfbe461fbf58fbd8179bca2a2021-11-01T00:00:00Zhttps://doaj.org/toc/2307-4205Background. An analysis of the processes of image segmentation is being carried out. An original method of image segmentation using a convolutional neural network is proposed. Materials and methods. A comparative assessment of existing segmentation methods such as threshold segmentation methods: Otsu, Niblack, Bernsen, Savola, as well as the method of image segmentation using a convolutional neural network is carried out. Their advantages and disadvantages are evaluated. Examples of image segmentation by various methods are given. Algorithmic descriptions of segmentation methods are presented. Experiments were carried out to study the segmentation of frames (images) from a given video sequence. Results and conclusions. The results of the experiment, showing the operation of one or another segmentation method, are presented.A.I. GodunovS.T. BalanyanP.S. EgorovPenza State University Publishing Housearticleadaptive methodsthreshold methodssegmentationotsu's methodniblack's methodbernsen's methodsavol's methodconvolutional neural networkMotor vehicles. Aeronautics. AstronauticsTL1-4050ENRUНадежность и качество сложных систем, Iss 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU
topic	adaptive methods threshold methods segmentation otsu's method niblack's method bernsen's method savol's method convolutional neural network Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	adaptive methods threshold methods segmentation otsu's method niblack's method bernsen's method savol's method convolutional neural network Motor vehicles. Aeronautics. Astronautics TL1-4050 A.I. Godunov S.T. Balanyan P.S. Egorov IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY
description	Background. An analysis of the processes of image segmentation is being carried out. An original method of image segmentation using a convolutional neural network is proposed. Materials and methods. A comparative assessment of existing segmentation methods such as threshold segmentation methods: Otsu, Niblack, Bernsen, Savola, as well as the method of image segmentation using a convolutional neural network is carried out. Their advantages and disadvantages are evaluated. Examples of image segmentation by various methods are given. Algorithmic descriptions of segmentation methods are presented. Experiments were carried out to study the segmentation of frames (images) from a given video sequence. Results and conclusions. The results of the experiment, showing the operation of one or another segmentation method, are presented.
format	article
author	A.I. Godunov S.T. Balanyan P.S. Egorov
author_facet	A.I. Godunov S.T. Balanyan P.S. Egorov
author_sort	A.I. Godunov
title	IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY
title_short	IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY
title_full	IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY
title_fullStr	IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY
title_full_unstemmed	IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY
title_sort	image segmentation and object recognition based on convolutional neural network technology
publisher	Penza State University Publishing House
publishDate	2021
url	https://doaj.org/article/4724ca36bfbe461fbf58fbd8179bca2a
work_keys_str_mv	AT aigodunov imagesegmentationandobjectrecognitionbasedonconvolutionalneuralnetworktechnology AT stbalanyan imagesegmentationandobjectrecognitionbasedonconvolutionalneuralnetworktechnology AT psegorov imagesegmentationandobjectrecognitionbasedonconvolutionalneuralnetworktechnology
_version_	1718405230141374464

IMAGE SEGMENTATION AND OBJECT RECOGNITION BASED ON CONVOLUTIONAL NEURAL NETWORK TECHNOLOGY

Ejemplares similares