Guest Editors
Prof. Yizhang Jiang, Jiangnan University, China
Prof. Xin Ning, Chinese Academy of Sciences, China
Prof. Weiwei Cai, Northern Arizona University, USA
Dr. Jing Wu, Cardiff University, UK
Summary
At present, computer vision (CV) has opened up a new application field. The characteristics of this field are human-centric, that is, human is the main target and service object of the CV system, and it involves the detection, recognition and understanding of static and dynamic features. Specifically, it includes the detection and recognition of various parts of the human body, such as the body, face, and limbs; human actions include gestures, gait, expressions, movements, behaviors, and emotions. human-centric computational intelligence solutions based on CV have the potential to enter a wide range of business, security, education, engineering, entertainment, consumption and daily life.
However, due to a series of complex factors such as illumination, occlusion, forgery attacks, and posture changes in real application scenarios, there are still many challenges and problems in human-centric image understanding that restrict its further application. In recent years, the research progress of deep learning-based CV has shown its potential in practical applications.
Therefore, the purpose of this special issue is to promote practical applications in this field and provide computational intelligence solutions, focusing on the study of computational intelligence methods in human-centric visual understanding and application. This topic will provide new ideas for deep learning-based CV researchers and help solve computational intelligence problems in human-centric visual understanding. Original research and review articles are welcome.
Potential topics include but are not limited to the following:
· Computational Intelligence approaches for human body posture recognition
· Computational Intelligence approaches for behavior recognition
· Computational Intelligence approaches for person re-identification
· CV-enabled and human-centric computational intelligence solutions for industrial applications
· CV-enabled and human-centric computational intelligence solutions for image and video processing
· Human-centric system modeling and design for daily life applications
· Human-centric computational intelligence tools in educational applications
Published Papers
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Open Access
ARTICLE
An Analysis Model of Learners’ Online Learning Status Based on Deep Neural Network and Multi-Dimensional Information Fusion
Mingyong Li, Lirong Tang, Longfei Ma, Honggang Zhao, Jinyu Hu, Yan Wei
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2349-2371, 2023, DOI:10.32604/cmes.2023.022604
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract The learning status of learners directly affects the quality of learning. Compared with offline teachers, it is difficult for online teachers to capture the learning status of students in the whole class, and it is even more difficult to continue to pay attention to students while teaching. Therefore, this paper proposes an online learning state analysis model based on a convolutional neural network and multi-dimensional information fusion. Specifically, a facial expression recognition model and an eye state recognition model are constructed to detect students’ emotions and fatigue, respectively. By integrating the detected data with the homework test score data after…
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Open Access
ARTICLE
Novel Apodized Fiber Bragg Grating Applied for Medical Sensors: Performance Investigation
Ramya Arumugam, Ramamoorthy Kumar, Samiappan Dhanalakshmi, Khin Wee Lai, Lei Jiao, Xiang Wu
CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 301-323, 2023, DOI:10.32604/cmes.2022.022144
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract Sensors play an important role in shaping and monitoring human health. Exploration of methods to use Fiber Bragg Grating (FBG) with enhanced sensitivity has attracted great interest in the field of medical research. In this paper, a novel apodization function is proposed and performance evaluation and optimization of the same have been made. A comparison was conducted between various existing apodization functions and the proposed one based on optical characteristics and sensor parameters. The results evince the implementation of the proposed apodization function for vital sign measurement. The optical characteristics considered for evaluation are Peak Resonance Reflectivity level, Side Lobes…
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Graphic Abstract
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Open Access
REVIEW
Broad Learning System for Tackling Emerging Challenges in Face Recognition
Wenjun Zhang, Wenfeng Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1597-1619, 2023, DOI:10.32604/cmes.2022.020517
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the
computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition
are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS).
We integrated two models, IR
3C with BLS and IR
3C with a triplet loss, to control the learning process. In our
experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness,
sensitivity, and practicability of the proposed two models. In the model of IR3C with BLS, the recognition rates
for…
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Open Access
REVIEW
Overview of 3D Human Pose Estimation
Jianchu Lin, Shuang Li, Hong Qin, Hongchang Wang, Ning Cui, Qian Jiang, Haifang Jian, Gongming Wang
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1621-1651, 2023, DOI:10.32604/cmes.2022.020857
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract 3D human pose estimation is a major focus area in the field of computer vision, which plays an important role
in practical applications. This article summarizes the framework and research progress related to the estimation of
monocular RGB images and videos. An overall perspective of methods integrated with deep learning is introduced.
Novel image-based and video-based inputs are proposed as the analysis framework. From this viewpoint, common
problems are discussed. The diversity of human postures usually leads to problems such as occlusion and ambiguity,
and the lack of training datasets often results in poor generalization ability of the model. Regression…
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Open Access
ARTICLE
An Effective Machine-Learning Based Feature Extraction/Recognition Model for Fetal Heart Defect Detection from 2D Ultrasonic Imageries
Bingzheng Wu, Peizhong Liu, Huiling Wu, Shunlan Liu, Shaozheng He, Guorong Lv
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1069-1089, 2023, DOI:10.32604/cmes.2022.020870
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract Congenital heart defect, accounting for about 30% of congenital defects, is the most common one. Data shows that congenital heart defects have seriously affected the birth rate of healthy newborns. In Fetal and Neonatal Cardiology, medical imaging technology (2D ultrasonic, MRI) has been proved to be helpful to detect congenital defects of the fetal heart and assists sonographers in prenatal diagnosis. It is a highly complex task to recognize 2D fetal heart ultrasonic standard plane (FHUSP) manually. Compared with manual identification, automatic identification through artificial intelligence can save a lot of time, ensure the efficiency of diagnosis, and improve the…
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Open Access
ARTICLE
Slope Collapse Detection Method Based on Deep Learning Technology
Xindai An, Di Wu, Xiangwen Xie, Kefeng Song
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1091-1103, 2023, DOI:10.32604/cmes.2022.020670
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural…
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Open Access
ARTICLE
A Multi Moving Target Recognition Algorithm Based on Remote Sensing Video
Huanhuan Zheng, Yuxiu Bai, Yurun Tian
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 585-597, 2023, DOI:10.32604/cmes.2022.020995
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract The Earth observation remote sensing images can display ground activities and status intuitively, which plays an
important role in civil and military fields. However, the information obtained from the research only from the
perspective of images is limited, so in this paper we conduct research from the perspective of video. At present, the
main problems faced when using a computer to identify remote sensing images are: They are difficult to build a
fixed regular model of the target due to their weak moving regularity. Additionally, the number of pixels occupied
by the target is not enough for accurate detection. However,…
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Open Access
ARTICLE
Visual Object Tracking via Cascaded RPN Fusion and Coordinate Attention
Jianming Zhang, Kai Wang, Yaoqi He, Lidan Kuang
CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.3, pp. 909-927, 2022, DOI:10.32604/cmes.2022.020471
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract Recently, Siamese-based trackers have achieved excellent performance in object tracking. However, the high speed
and deformation of objects in the movement process make tracking difficult. Therefore, we have incorporated
cascaded region-proposal-network (RPN) fusion and coordinate attention into Siamese trackers. The proposed
network framework consists of three parts: a feature-extraction sub-network, coordinate attention block, and
cascaded RPN block.We exploit the coordinate attention block, which can embed location information into channel
attention, to establish long-term spatial location dependence while maintaining channel associations. Thus, the
features of different layers are enhanced by the coordinate attention block. We then send these features separately
into…
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Open Access
ARTICLE
Underwater Diver Image Enhancement via Dual-Guided Filtering
Jingchun Zhou, Taian Shi, Weishi Zhang, Weishen Chu
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 1063-1081, 2022, DOI:10.32604/cmes.2022.019447
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging. To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method. First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image. Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details. Four feature weight maps of the two images were then calculated, and…
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Open Access
ARTICLE
N-SVRG: Stochastic Variance Reduction Gradient with Noise Reduction Ability for Small Batch Samples
Haijie Pan, Lirong Zheng
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 493-512, 2022, DOI:10.32604/cmes.2022.019069
（This article belongs to this Special Issue:
Enabled and Human-centric Computational Intelligence Solutions for Visual Understanding and Application)
Abstract The machine learning model converges slowly and has unstable training since large variance by random using a sample estimate gradient in SGD. To this end, we propose a noise reduction method for Stochastic Variance Reduction gradient (SVRG), called N-SVRG, which uses small batches samples instead of all samples for the average gradient calculation, while performing an incremental update of the average gradient. In each round of iteration, a small batch of samples is randomly selected for the average gradient calculation, while the average gradient is updated by rounding of the past model gradients during internal iterations. By suitably reducing the…
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