| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Instrumentação. Para informações adicionais entre em contato com cnpdia.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Instrumentação. |
|
Data corrente: |
25/10/2021 |
|
Data da última atualização: |
09/06/2022 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
Autoria: |
BERTOLLA, A. B.; CRUVINEL, P. E. |
|
Afiliação: |
PAULO ESTEVAO CRUVINEL, CNPDIA. |
|
Título: |
Band-pass filtering for non-stationary noise in agricultural images to pest control based on adaptive semantic modeling. |
|
Ano de publicação: |
2021 |
|
Fonte/Imprenta: |
In: IEEE International Conference on Semantic Computing (ICSC), 15th, Laguna Hills, CA, USA, 2021. |
|
DOI: |
10.1109/ICSC50631.2021.00073 |
|
Idioma: |
Inglês |
|
Conteúdo: |
Image analysis has been used in a very large scale for different purposes. When an image is captured by a digital sensor, it is usually affected by some type of noise, even the smoothest ones. Therefore, image enhancement and denoising process are important tasks of digital image processing. This paper presents an algorithm to reduce non-stationary noise with the combination of a Low-Pass Filter (LPF) and a High-Pass Filter (HPF), in conjunction with an adaptive semantic model. To simulate the usefulness of such arrangement, a non-stationary Gaussian noise has been applied to an image, which has been splitted into the four quadrants, all of them having the same dimensions. In fact, such a noise with different intensities, has been added to the image in each of its quadrants. The Peak Signal-to-Noise Ratio (PSNR) has been used to measure the best cutoff frequencies for both filters, as well as rules based on semantic concepts have been structured for decision making. Furthermore, for the validation of the algorithm we have taken into account the evaluation of the Mean Squared Error (MSE) using a typical digital image obtained from a crop of maize with the presence of the earwornm (Helicoverpa Zea). Besides, the denoising process demonstrates the efficiency and the satisfactory performance for the non-stationary noise filtering in agricultural images |
|
Palavras-Chave: |
Low-Pass Filter; Non-Stationary Noise; Semantic Filtering. |
|
Categoria do assunto: |
-- |
|
Marc: |
LEADER 02001nam a2200169 a 4500
001 2135525
005 2022-06-09
008 2021 bl uuuu u00u1 u #d
024 7 $a10.1109/ICSC50631.2021.00073$2DOI
100 1 $aBERTOLLA, A. B.
245 $aBand-pass filtering for non-stationary noise in agricultural images to pest control based on adaptive semantic modeling.$h[electronic resource]
260 $aIn: IEEE International Conference on Semantic Computing (ICSC), 15th, Laguna Hills, CA, USA$c2021
520 $aImage analysis has been used in a very large scale for different purposes. When an image is captured by a digital sensor, it is usually affected by some type of noise, even the smoothest ones. Therefore, image enhancement and denoising process are important tasks of digital image processing. This paper presents an algorithm to reduce non-stationary noise with the combination of a Low-Pass Filter (LPF) and a High-Pass Filter (HPF), in conjunction with an adaptive semantic model. To simulate the usefulness of such arrangement, a non-stationary Gaussian noise has been applied to an image, which has been splitted into the four quadrants, all of them having the same dimensions. In fact, such a noise with different intensities, has been added to the image in each of its quadrants. The Peak Signal-to-Noise Ratio (PSNR) has been used to measure the best cutoff frequencies for both filters, as well as rules based on semantic concepts have been structured for decision making. Furthermore, for the validation of the algorithm we have taken into account the evaluation of the Mean Squared Error (MSE) using a typical digital image obtained from a crop of maize with the presence of the earwornm (Helicoverpa Zea). Besides, the denoising process demonstrates the efficiency and the satisfactory performance for the non-stationary noise filtering in agricultural images
653 $aLow-Pass Filter
653 $aNon-Stationary Noise
653 $aSemantic Filtering
700 1 $aCRUVINEL, P. E.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Instrumentação (CNPDIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Registros recuperados : 2 | |
| Registros recuperados : 2 | |
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
