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Your search keyword '"Chong, Nannan"' showing total 11 results
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11 results on '"Chong, Nannan"'

3. Virtual Reality Application for Laparoscope in Clinical Surgery Based on Siamese Network and Census Transformation

Author

Chong, Nannan, Si, Yazhong, Zhao, Wei, Zhang, Qiushi, Yin, Boran, Zhao, Yuehua, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Su, Ruidan, editor, Zhang, Yu-Dong, editor, and Liu, Han, editor

Published
2022
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4. CMMCAN: Lightweight Feature Extraction and Matching Network for Endoscopic Images Based on Adaptive Attention.

Author

Chong, Nannan and Yang, Fan

Subjects
MINIMALLY invasive procedures, OPTICAL images, GRAYSCALE model, HUMAN body, ENDOSCOPES, OPTICAL flow
Abstract

In minimally invasive surgery, endoscopes or laparoscopes equipped with miniature cameras and tools are used to enter the human body for therapeutic purposes through small incisions or natural cavities. However, in clinical operating environments, endoscopic images often suffer from challenges such as low texture, uneven illumination, and non-rigid structures, which affect feature observation and extraction. This can severely impact surgical navigation or clinical diagnosis due to missing feature points in endoscopic images, leading to treatment and postoperative recovery issues for patients. To address these challenges, this paper introduces, for the first time, a Cross-Channel Multi-Modal Adaptive Spatial Feature Fusion (ASFF) module based on the lightweight architecture of EfficientViT. Additionally, a novel lightweight feature extraction and matching network based on attention mechanism is proposed. This network dynamically adjusts attention weights for cross-modal information from grayscale images and optical flow images through a dual-branch Siamese network. It extracts static and dynamic information features ranging from low-level to high-level, and from local to global, ensuring robust feature extraction across different widths, noise levels, and blur scenarios. Global and local matching are performed through a multi-level cascaded attention mechanism, with cross-channel attention introduced to simultaneously extract low-level and high-level features. Extensive ablation experiments and comparative studies are conducted on the HyperKvasir, EAD, M2caiSeg, CVC-ClinicDB, and UCL synthetic datasets. Experimental results demonstrate that the proposed network improves upon the baseline EfficientViT-B3 model by 75.4% in accuracy (Acc), while also enhancing runtime performance and storage efficiency. When compared with the complex DenseDescriptor feature extraction network, the difference in Acc is less than 7.22%, and IoU calculation results on specific datasets outperform complex dense models. Furthermore, this method increases the F1 score by 33.2% and accelerates runtime by 70.2%. It is noteworthy that the speed of CMMCAN surpasses that of comparative lightweight models, with feature extraction and matching performance comparable to existing complex models but with faster speed and higher cost-effectiveness. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Design of Laboratory Monitoring Management System Based on Internet of Things

Author

Chong, Nannan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Liu, Xin, editor, Na, Zhenyu, editor, Wang, Wei, editor, Mu, Jiasong, editor, and Zhang, Baoju, editor

Published
2020
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10. Point Cloud Semantic Segmentation with Cross-Correction Features

Author

Yuehua Zhao, Ma Jie, Chong Nannan, and Wen Junjie

Abstract

Real time large scale point cloud segmentation is an important but challenging task for practical application like autonomous driving. Existing real time methods have achieved acceptance performance by aggregating local information. However, most of them only exploit local spatial information or local semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial-Semantic Incorporation Network (SSI-Net) for real time large scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High quality contextual features can be learned through SSC by correct and update semantic features using spatial cues, and vice verse. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder-decoder architecture. To ensure efficiency, it also adopts a random sample based hierarchical network structure. Extensive experiments on several prevalent datasets demonstrate that our method can achieve state-of-the-art performance.

Published
2022

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