Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Feiniu Yuan"'
Publikováno v:
IET Image Processing, Vol 18, Iss 12, Pp 3206-3217 (2024)
Abstract Visual smoke semantic segmentation is a challenging task due to semi‐transparency, variable shapes, and complex textures of smoke. To improve segmentation performance, a convolutional neural network and transformer hybrid network are propo
Externí odkaz:
https://doaj.org/article/724ce7f11e664e098f689b52c684f5cf
Publikováno v:
IET Computer Vision, Vol 18, Iss 2, Pp 236-246 (2024)
Abstract It is very challenging to accurately segment smoke images because smoke has some adverse vision characteristics, such as anomalous shapes, blurred edges, and translucency. Existing methods cannot fully focus on the texture details of anomalo
Externí odkaz:
https://doaj.org/article/cfe0325b7b554052b5ae8fdd2c5fbb45
Publikováno v:
IET Image Processing, Vol 17, Iss 10, Pp 3028-3039 (2023)
Abstract Segmentations provide important clues for diagnosing diseases. U‐shaped neural networks with skip connections have become one of popular frameworks for medical image segmentation. Skip connections really reduce loss of spatial details caus
Externí odkaz:
https://doaj.org/article/2c82659b84454d729526130479947a8c
Publikováno v:
Frontiers in Physics, Vol 11 (2023)
Image segmentation methods usually fuse shallow and deep features to locate object boundaries, but it is difficult to improve the accuracy of smoke segmentation by conventional fusion methods. It is a very difficult vision task to perform semantic se
Externí odkaz:
https://doaj.org/article/b9882b036fc44e25b999b8ed336bdb30
Publikováno v:
IEEE Access, Vol 10, Pp 31058-31069 (2022)
An important prerequisite for brain disease diagnosis is to segment brain tissues of Magnetic Resonance Imaging (MRI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). To improve performance, we propose a Multi-modality Reconst
Externí odkaz:
https://doaj.org/article/ae983fed01134197b0d71b0e553b6a64
Publikováno v:
IET Computer Vision, Vol 13, Iss 2, Pp 178-187 (2019)
It is challenging to recognize smoke from visual scenes due to large variations of smoke colors, textures and shapes. To improve robustness, we propose a novel feature extraction method based on similarity and dissimilarity matching measures of Local
Externí odkaz:
https://doaj.org/article/ffb94ea5f9874629adb30d3f6c6d915b
Publikováno v:
IEEE Access, Vol 5, Pp 6833-6841 (2017)
To improve smoke detection accuracy, we combine local binary pattern (LBP) like features, kernel principal component analysis (KPCA), and Gaussian process regression (GPR) to propose a novel data processing pipeline for smoke detection. The data proc
Externí odkaz:
https://doaj.org/article/e0c2ff11e12640fb8c340e3b44a2bfe4
Publikováno v:
IEEE Access, Vol 5, Pp 18429-18438 (2017)
It is a challenging task to recognize smoke from images due to large variance of smoke color, texture, and shapes. There are smoke detection methods that have been proposed, but most of them are based on hand-crafted features. To improve the performa
Externí odkaz:
https://doaj.org/article/9354f6aeb206415499643b51e98a9493
Publikováno v:
IEEE Access, Vol 4, Pp 4573-4582 (2016)
The purpose of remote sensing image fusion is to sharpen a low spatial resolution multispectral (MS) image by injecting the detail map extracted from a panchromatic (PAN) image. In this paper, a novel remote sensing image fusion method based on adapt
Externí odkaz:
https://doaj.org/article/6584e169446644da8400aa89bdcf5f5a
Publikováno v:
Multimedia Tools and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3acb5f4cf89c59ec3e0170401ce54fba
https://doi.org/10.1007/s11042-023-14879-z
https://doi.org/10.1007/s11042-023-14879-z