Author name cluster

Feng Qiu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

JBHI Journal 2026 Journal Article

Contactless Hemodynamic Monitoring Based on Multi-Scale Gaussian Filtering via Imaging PPG

Yonggang Tong
Zhipei Huang
Feng Qiu
Chenhao Wu
Xiaoyong Tao
Wei Huang
Fei Qin

Imaging photoplethysmography (iPPG) is an emerging optical technique that allows for the contactless acquisition of arterial Blood Volume Pulse (BVP) signals from video recordings of the human skin. While iPPG offers a non-contact and convenient means for physiological monitoring, the accuracy of the extracted BVP signals remains limited. This limitation hinders its potential for advanced cardiovascular assessments, such as evaluations of arterial stiffness and cardiac function. To address this issue, we propose a novel physiologically informed Gaussian filtering method, based on the prior knowledge that the BVP waveform can be modeled as a mixture of multiple Gaussian components. Specifically, a set of physiological Gaussian kernels is employed to convolve the noisy iPPG signal, generating a Gaussian representation that emphasizes waveform components with physiological relevance. This representation is further refined by a Transformer-based neural network to reconstruct accurate BVP signals. Experimental results demonstrate a notable improvement in BVP accuracy, with the mean absolute error reducing from 0. 25 to 0. 08. This enhancement in iPPG precision highlights the potential of our approach for advanced medical applications.

Details DOI

JBHI Journal 2024 Journal Article

An Accurate Non-Contact Photoplethysmography via Active Cancellation of Reflective Interference

Yonggang Tong
Zhipei Huang
Feng Qiu
Tao Wang
Yiquan Wang
Fei Qin
Ming Yin

Imaging Photoplethysmography (IPPG) is an emerging and efficient optical method for non-contact measurement of pulse waves using an image sensor. While the contactless way brings convenience, the inevitable distance between the sensor and the subject results in massive specular reflection interference on the skin surface, which leads to a low Signal to Interference plus Noise Ratio (SINR) of IPPG. To ease this challenge, this work proposes a novel modulation illumination approach to measure the accurate arterial pulse wave via surface reflection interference isolation from IPPG. Based on the proposed skin reflection model, a specific modulation illumination is designed to separate the surface reflections and obtain the subcutaneous diffuse reflections containing the pulse wave information. Compared with the results under ambient illumination and constant supplemental illumination, the SINR of the proposed method is improved by 4. 56 and 3. 74 dB, respectively.

Details DOI

EAAI Journal 2023 Journal Article

A novel seminar learning framework for weakly supervised salient object detection

Yan Liu
Yunzhou Zhang
Zhenyu Wang
Fei Yang
Feng Qiu
Sonya Coleman
Dermot Kerr

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much attention from several research perspectives, it has revealed two problems while driving the rapid development of saliency detection. (1) Large divergence in the characteristics of saliency regions in terms of location, shape and size makes them difficult to recognize. (2) The properties of convolutional neural networks dictate that it is insensitive to various transformations, which will lead to hardly balance the application of various disturbances. To tackle these limitations, this paper proposes a novel seminar learning framework with consistent transformation ensembling (SLF-CT) for scribble supervised SOD. The framework consists of the teacher–student model and the student–student model for segmenting the salient objects. Specifically, we first design a cross attention guided network (CAGNet) as a baseline model for saliency prediction. Then we assign CAGNet to the teacher–student model, where the teacher network is based on the exponential moving average and guides the training of the student network. Moreover, we adopt multiple pseudo labels to transfer the information among students from different conditions. To further enhance the regularization of the network, a consistency transformation mechanism is also incorporated, which encourages the saliency prediction and input image of the network to be consistent. The experimental results demonstrate that the proposed approach performs favorably comparable with the state-of-the-art weakly supervised methods. As far as we know, the proposed approach is the first application of seminar learning in the SOD area.

Details DOI