Cross-Modal Feature Integration Network for Human Eye-Fixation Prediction in RGB-D Images

Liu, Wenyu; Zhou, Wujie; Luo, Ting

doi:10.1109/access.2020.3036681

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…Then, the output of Leaky ReLU layer and the output of the network block before the current block will be added together in MSFF. The addition operation here is inspired by the work on human eye-fixation prediction, where the authors also face a dualmodal information fusion problem and fuse information of different scales by addition operation [41]. Like YOLOV3, the initial fused feature maps will be fed into multiple layers for thoroughly information fusion.…”

Section: ) Multi-scale Feature Fusion Module (Msff)mentioning

confidence: 99%

Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

Liu

Bagnaninchi

Yang

2022

IEEE Trans. Med. Imaging

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While Electrical Impedance Tomography (EIT) has found many biomedicine applications, a better resolution is needed to provide quantitative analysis for tissue engineering and regenerative medicine. This paper proposes an impedance-optical dual-modal imaging framework, which is mainly aimed at high-quality 3D cell culture imaging and can be extended to other tissue engineering applications. The framework comprises three components, i.e., an impedance-optical dual-modal sensor, the guidance image processing algorithm, and a deep learning model named multi-scale feature cross fusion network (MSFCF-Net) for information fusion. The MSFCF-Net has two inputs, i.e., the EIT measurement and a binary mask image generated by the guidance image processing algorithm, whose input is an RGB microscopic image. The network then effectively fuses the information from the two different imaging modalities and generates the final conductivity image. We assess the performance of the proposed dual-modal framework by numerical simulation and MCF-7 cell imaging experiments. The results show that the proposed method could significantly improve image quality, indicating that impedance-optical joint imaging has the potential to reveal the structural and functional information of tissue-level targets simultaneously.

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Section: ) Multi-scale Feature Fusion Module (Msff)mentioning

confidence: 99%

Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

Liu

Bagnaninchi

Yang

2022

IEEE Trans. Med. Imaging

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Mlanet: Multilevel Aggregation Network for Binocular Eye-Fixation Prediction

Zhou

Qian

et al. 2022

SSRN Journal

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Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm

Shi

2022

Applied Sciences

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The modern urban environment is becoming more and more complex. In helping us identify surrounding objects, vehicle vision sensors rely more on the semantic segmentation ability of deep learning networks. The performance of a semantic segmentation network is essential. This factor will directly affect the comprehensive level of driving assistance technology in road environment perception. However, the existing semantic segmentation network has a redundant structure, many parameters, and low operational efficiency. Therefore, to reduce the complexity of the network and reduce the number of parameters to improve the network efficiency, based on the deep learning (DL) theory, a method for efficient image semantic segmentation using Deep Convolutional Neural Network (DCNN) is deeply studied. First, the theoretical basis of the convolutional neural network (CNN) is briefly introduced, and the real-time semantic segmentation technology of urban scenes based on DCNN is recommended in detail. Second, the atrous convolution algorithm and the multi-scale parallel atrous spatial pyramid model are introduced. On the basis of this, an Efficient Symmetric Network (ESNet) of real-time semantic segmentation model for autonomous driving scenarios is proposed. The experimental results show that: (1) On the Cityscapes dataset, the ESNet structure achieves 70.7% segmentation accuracy for the 19 semantic categories set, and 87.4% for the seven large grouping categories. Compared with other algorithms, the accuracy has increased to varying degrees. (2) On the CamVid dataset, compared with segmentation networks of multiple lightweight real-time images, the parameters of the ESNet model are around 1.2 m, the highest FPS value is around 90 Hz, and the highest mIOU value is around 70%. In seven semantic categories, the segmentation accuracy of the ESNet model is the highest at around 98%. From this, we found that the ESNet significantly improves segmentation accuracy while maintaining faster forward inference speed. Overall, the research not only provides technical support for the development of real-time semantic understanding and segmentation of DCNN algorithms but also contributes to the development of artificial intelligence technology.

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Cross-Modal Feature Integration Network for Human Eye-Fixation Prediction in RGB-D Images

Cited by 3 publications

References 56 publications

Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion

Mlanet: Multilevel Aggregation Network for Binocular Eye-Fixation Prediction

Real-Time Semantic Understanding and Segmentation of Urban Scenes for Vehicle Visual Sensors by Optimized DCNN Algorithm

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