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Optimized efficient attention-based network for facial expressions analysis in neurological health care

Computers in Biology and Medicine 2024年 179卷 108822-108822页

作者： Munsif, Muhammad Sajjad, Muhammad Ullah, Mohib Tarekegn, Adane Nega Cheikh, Faouzi Alaya Tsakanikas, Panagiotis Muhammad, Khan Sejong University Seoul143-747 Korea Republic of Digital Image Processing Lab Department of Computer Science Islamia College Peshawar25000 Pakistan Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Institute of Communication and Computer Systems National Technical University of Athens Athens15773 Greece Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and *** code and data used in this work are available at: https://***/munsif200/Neurological-Health-Care. © 2024 Elsevier Ltd

关键词： Feature extraction

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Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

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arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

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MPASNET: Motion prior-aware siamese network for unsupervised deep crowd segmentation in video scenes

arXiv

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arXiv 2021年

作者： Yang, Jinhai Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Crowd segmentation is a fundamental task serving as the basis of crowded scene analysis, and it is highly desirable to obtain refined pixel-level segmentation maps. However, it remains a challenging problem, as existing approaches either require dense pixel-level annotations to train deep learning models or merely produce rough segmentation maps from optical or particle flows with physical models. In this paper, we propose the Motion Prior-Aware Siamese Network (MPASNET) for unsupervised crowd semantic segmentation. This model not only eliminates the need for annotation but also yields high-quality segmentation maps. Specially, we first analyze the coherent motion patterns across the frames and then apply a circular region merging strategy on the collective particles to generate pseudo-labels. Moreover, we equip MPASNET with siamese branches for augmentation-invariant regularization and siamese feature aggregation. Experiments over benchmark datasets indicate that our model outperforms the state-of-the-arts by more than 12% in terms of mIoU. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantic Segmentation

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An Adaptive Feature-based Quantization Algorithm for Point Cloud Compression

An Adaptive Feature-based Quantization Algorithm for Point C...

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Picture Coding Symposium, PCS

作者： Da Ai Hongying Lu Yurong Yang Ying Liu National Key Lab. of Electronic Information Processing for Applications in Crime Scene Investigation Ministry of Public Security Xi‘an China Center for Image and Information Processing Xi'an University of Posts and Telecommunications Xi'an China The International Joint-Research Center for Wireless Communication and Information Processing Xi‘an University of Posts and Telecommunications Xi‘an China

To reduce over-rasterization distortion caused by global uniform quantization for static surface point cloud, an adaptive quantization coding method based on feature mining is proposed. Combining spatial position and texture feature of point clouds with level of details, the quantization increment is dynamically set according to feature priority, which can reserve the number of effective points to the maximum extent, and reduce the rasterization distortion. Experimental results show that the proposed method can effectively enhance the subjective reconstruction quality of compressed point cloud, gaining better results of rate-distortion optimization.

关键词： Graphics Geometry Three-dimensional displays Quantization (signal) Transform coding Rate-distortion Distortion

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Adaptive Person-Specific Appearance-Based Gaze Estimation 16th

Adaptive Person-Specific Appearance-Based Gaze Estimation

引用

16th International Forum on Digital TV and Wireless Multimedia communication, IFTC 2019

作者： Zheng, Chuanyang Zhou, Jun Sun, Jun Zhao, Lihua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China Children’s Hospital of Shanghai Shanghai200062 China

ISBN: (纸本)9789811533402

Non-invasive gaze estimation from only eye images captured by camera is a challenging problem due to various eye shapes, eye structures and image qualities. Recently, CNN network has been applied to directly regress eye image to gaze direction and obtains good performance. However, generic approaches are susceptible to bias and variance highly relating to different individuals. In this paper, we study the person-specific bias when applying generic methods on new person. And we introduce a novel appearance-based deep neural network integrating meta-learning to reduce the person-specific bias. Given only a few person-specific calibration images collected in normal calibration process, our model adapts quickly to test person and predicts more accurate gaze directions. Experiments on public MPIIGaze dataset and Eyediap dataset show our approach has achieved competitive accuracy to current state-of-the-art methods and are able to alleviate person-specific bias problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Improving HEVC Coding Perceptual Quality Using Saliency Guided CTU Layer QP Adjustment

Improving HEVC Coding Perceptual Quality Using Saliency Guid...

引用

Chinese Automation Congress (CAC)

作者： Ziguan Cui Mengnan Zhang Xiaopeng Jiang Zongliang Gan Guijin Tang Feng Liu Image Processing and Image Communication Lab Nanjing University of Posts and Telecommunications Nanjing China

Recently, improving the perceptual quality of video coding under bit rate constraint has attracted researchers' attention in video coding field. In this paper, we propose to improve the coding perceptual quality for the latest High Efficiency Video Coding (HEVC) standard using saliency guided coding tree unit (CTU) level quantization parameter (QP) adjustment scheme. Our work mainly includes two steps: first, a CTU layer saliency detection scheme with low complexity is proposed to measure the visual significance for each CTU in a frame. Then, a CTU layer QP adjustment scheme based on relative saliency is proposed to increase the bit rate resource for more salient regions and decrease the bit rate cost for less salient regions. Therefore, the proposed QP adjustment scheme obtains better bit rate allocation among CTUs and generates better coding perceptual quality. Experimental results show that the proposed method achieves better perceptual quality and rate distortion performance for HEVC video coding.

关键词： Saliency detection Encoding Visualization image coding Bit rate High efficiency video coding

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Attentive semantic exploring for manipulated face detection

arXiv

引用

arXiv 2020年

作者： Chen, Zehao Yang, Hua Institution of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Degital Media Processing and Transmission Shanghai China

Face manipulation methods develop rapidly in recent years, whose potential risk to society accounts for the emerging of researches on detection methods. However, due to the diversity of manipulation methods and the high quality of fake images, detection methods suffer from a lack of generalization ability. To solve the problem, we find that segmenting images into semantic fragments could be effective, as discriminative defects and distortions are closely related to such fragments. Besides, to highlight discriminative regions in fragments and to measure contribution to the final prediction of each fragment is efficient for the improvement of generalization ability. Therefore, we propose a novel manipulated face detection method based on Multilevel Facial Semantic Segmentation and Cascade Attention Mechanism. To evaluate our method, we reconstruct two datasets: GGFI and FFMI, and also collect two open-source datasets. Experiments on four datasets verify the advantages of our approach against other state-of-the-arts, especially its generalization ability. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Blind Quality Assessment for in-the-Wild images via Hierarchical Feature Fusion and Iterative Mixed Database Training

arXiv

引用

arXiv 2021年

作者： Sun, Wei Min, Xiongkuo Tu, Danyang Zhai, Guangtao Ma, Siwei The Institute of Image Communication and Information Processing Shanghai Jiao Tong University China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China The Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China

image quality assessment (IQA) is very important for both end-users and service providers since a high-quality image can significantly improve the user’s quality of experience (QoE) and also benefit lots of computer vision algorithms. Most existing blind image quality assessment (BIQA) models were developed for synthetically distorted images, however, they perform poorly on in-the-wild images, which are widely existed in various practical applications. In this paper, we propose a novel BIQA model for in-the-wild images by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity. Considering that perceptual visual quality is affected by both low-level visual features (e.g. distortions) and high-level semantic information (e.g. content), we first propose a staircase structure to hierarchically integrate the features from intermediate layers into the final feature representation, which enables the model to make full use of visual information from low-level to high-level. Then an iterative mixed database training (IMDT) strategy is proposed to train the BIQA model on multiple databases simultaneously, so the model can benefit from the increase in both training samples and image content and distortion diversity and can learn a more general feature representation. Experimental results show that the proposed model outperforms other state-of-the-art BIQA models on six in-the-wild IQA databases by a large margin. Moreover, the proposed model shows an excellent performance in the cross-database evaluation experiments, which further demonstrates that the learned feature representation is robust to images with diverse distortions and content. The code is available at https://***/sunwei925/StairIQA. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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A Multiple Attributes image Quality Database for Smartphone Camera Photo Quality Assessment

A Multiple Attributes Image Quality Database for Smartphone ...

引用

IEEE International Conference on image processing

作者： Wenhan Zhu Guangtao Zhai Zongxi Han Xiongkuo Min Tao Wang Zicheng Zhang Xiaokang Yangand MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Institute of Image Communication and Information Processing Shanghai Jiao Tong University China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Smartphone is the superstar product in digital device market and the quality of smartphone camera photos (SCPs) is becoming one of the dominant considerations when consumers purchase smartphones. How to evaluate the quality of smartphone cameras and the taken photos is urgent issue to be solved. To bridge the gap between academic research accomplishment and industrial needs, in this paper, we establish a new Smartphone Camera Photo Quality Database (SCPQD2020) including 1800 images with 120 scenes taken by 15 smartphones. Exposure, color, noise and texture which are four dominant factors influencing the quality of SCP are evaluated in the subjective study, respectively. Ten popular no-reference (NR) image quality assessment (IQA) algorithms are tested and analyzed on our database. Experimental results demonstrate that the current objective models are not suitable for SCPs, and quality metrics having high correlation with human visual perception are highly needed.

关键词： Cameras image color analysis Databases Colored noise Measurement Quality assessment image quality

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A multiple attributes image quality database for smartphone camera photo quality assessment

arXiv

引用

arXiv 2020年

作者： Zhu, Wenhan Zhai, Guangtao Han, Zongxi Min, Xiongkuo Wang, Tao Zhang, Zicheng Yang, Xiaokang MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Institute of Image Communication and Information Processing Shanghai Jiao Tong University China

关键词： Database systems

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