The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 논문은 원격 감지된 낮은 플랫폼에 장착된 가시 대역 카메라 이미지를 통해 수질 오염을 감지하는 새로운 접근 방식을 제시합니다. 매끄러운(수면에 유출된 기름) 영역 라벨링에 대한 감독되지 않은 분할의 타당성을 조사합니다. 적응형 및 비적응형 필터링은 획득된 텍스처 특징의 밀도 모델링과 결합됩니다. 필터 뱅크를 사용하여 원시 강도 이미지에서 텍스처 특징을 추출하고 얻은 출력 계수에서 적응형 특징을 추출하는 데 특별한 노력이 집중되었습니다. 추출된 특징 공간의 분할은 가우스 혼합 모델(GMM)을 사용하여 달성됩니다.
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부
Inna STAINVAS, David LOWE, "Towards Sea Surface Pollution Detection from Visible Band Images" in IEICE TRANSACTIONS on Electronics,
vol. E84-C, no. 12, pp. 1848-1856, December 2001, doi: .
Abstract: This paper presents a novel approach to water pollution detection from remotely sensed low-platform mounted visible band camera images. We examine the feasibility of unsupervised segmentation for slick (oily spills on the water surface) region labelling. Adaptive and non adaptive filtering is combined with density modeling of the obtained textural features. A particular effort is concentrated on the textural feature extraction from raw intensity images using filter banks and adaptive feature extraction from the obtained output coefficients. Segmentation in the extracted feature space is achieved using Gaussian mixture models (GMM).
URL: https://global.ieice.org/en_transactions/electronics/10.1587/e84-c_12_1848/_p
부
@ARTICLE{e84-c_12_1848,
author={Inna STAINVAS, David LOWE, },
journal={IEICE TRANSACTIONS on Electronics},
title={Towards Sea Surface Pollution Detection from Visible Band Images},
year={2001},
volume={E84-C},
number={12},
pages={1848-1856},
abstract={This paper presents a novel approach to water pollution detection from remotely sensed low-platform mounted visible band camera images. We examine the feasibility of unsupervised segmentation for slick (oily spills on the water surface) region labelling. Adaptive and non adaptive filtering is combined with density modeling of the obtained textural features. A particular effort is concentrated on the textural feature extraction from raw intensity images using filter banks and adaptive feature extraction from the obtained output coefficients. Segmentation in the extracted feature space is achieved using Gaussian mixture models (GMM).},
keywords={},
doi={},
ISSN={},
month={December},}
부
TY - JOUR
TI - Towards Sea Surface Pollution Detection from Visible Band Images
T2 - IEICE TRANSACTIONS on Electronics
SP - 1848
EP - 1856
AU - Inna STAINVAS
AU - David LOWE
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Electronics
SN -
VL - E84-C
IS - 12
JA - IEICE TRANSACTIONS on Electronics
Y1 - December 2001
AB - This paper presents a novel approach to water pollution detection from remotely sensed low-platform mounted visible band camera images. We examine the feasibility of unsupervised segmentation for slick (oily spills on the water surface) region labelling. Adaptive and non adaptive filtering is combined with density modeling of the obtained textural features. A particular effort is concentrated on the textural feature extraction from raw intensity images using filter banks and adaptive feature extraction from the obtained output coefficients. Segmentation in the extracted feature space is achieved using Gaussian mixture models (GMM).
ER -