Zobrazeno 1 - 10
of 247
pro vyhledávání: '"multimodal feature fusion"'
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 590-602 (2024)
Kirsten rat sarcoma viral oncogene homolog (namely KRAS) is a key biomarker for prognostic analysis and targeted therapy of colorectal cancer. Recently, the advancement of machine learning, especially deep learning, has greatly promoted the developme
Externí odkaz:
https://doaj.org/article/e5318c9013be456dab7a3a654e626975
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-14 (2024)
Abstract Matching abbreviated names with their full names (full-abbr matching) plays a key role in data integration, address matching, information retrieval, and other fields. Traditional full-abbr matching technology often encounters issues related
Externí odkaz:
https://doaj.org/article/a70d8c0c22244ad48746bf68ef4e5336
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3104-3115 (2024)
Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper,
Externí odkaz:
https://doaj.org/article/8c4726a7e557492384477e76a0de8206
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 8581-8593 (2024)
Smart satellites and unmanned aerial vehicles (UAVs) are typically equipped with visible light and infrared (IR) spectrum sensors. However, achieving real-time object detection utilizing these multimodal data on such resource-limited devices is a cha
Externí odkaz:
https://doaj.org/article/24320c0fba4e40f7a3c636be8871dd83
Publikováno v:
IET Image Processing, Vol 17, Iss 11, Pp 3079-3094 (2023)
Abstract Current dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the
Externí odkaz:
https://doaj.org/article/18bf0ea96ed14dcaadc47ee117a917c6
Publikováno v:
Sensors, Vol 24, Iss 14, p 4557 (2024)
The unsafe action of miners is one of the main causes of mine accidents. Research on underground miner unsafe action recognition based on computer vision enables relatively accurate real-time recognition of unsafe action among underground miners. A d
Externí odkaz:
https://doaj.org/article/e2654ee2cba749b294950cec3762187d
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2459 (2024)
Optical and Synthetic Aperture Radar (SAR) imagery offers a wealth of complementary information on a given target, attributable to the distinct imaging modalities of each component image type. Thus, multimodal remote sensing data have been widely use
Externí odkaz:
https://doaj.org/article/4a2117c6df55446f91302fdc53108d5c
Autor:
Qianqian Liu, Xili Wang
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2289 (2024)
Image–text multimodal deep semantic segmentation leverages the fusion and alignment of image and text information and provides more prior knowledge for segmentation tasks. It is worth exploring image–text multimodal semantic segmentation for remo
Externí odkaz:
https://doaj.org/article/1130c44096b64aef9ef007e79f087f22
Publikováno v:
Remote Sensing, Vol 16, Iss 11, p 1993 (2024)
Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a large number of repetitive planar structures, the point
Externí odkaz:
https://doaj.org/article/718b6a6b1c2d4c7fa202decdb9208817
Publikováno v:
Sensors, Vol 24, Iss 8, p 2558 (2024)
The identification of multi-source signals with time-frequency aliasing is a complex problem in wideband signal reception. The traditional method of first separation and identification especially fails due to the significant separation error under un
Externí odkaz:
https://doaj.org/article/f4be9854493544dc804d88e58935f11f