Results 251 to 260 of about 363,003 (273)
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Deeply fusing multi-model quality-aware features for sophisticated human activity understanding
Signal Processing: Image Communication, 2020Abstract Detecting and understanding human action under sophisticated lighting condition and backgrounds, also known as human action recognition in real-world context, is an indispensable component in modern intelligent systems and has becoming a hot research topic currently.
Xiaoli Liu, Zhibin Li
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Face detection on distorted images using perceptual quality-aware features
SPIE Proceedings, 2014We quantify the degradation in performance of a popular and effective face detector when human–perceived image quality is degraded by distortions due to additive white gaussian noise, gaussian blur or JPEG compression. It is observed that, within a certain range of perceived image quality, a modest increase in image quality can drastically improve ...
Suriya Gunasekar +2 more
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Compression-RQ-VQA: Leveraging Rich Quality-Aware Features for Compressed Video Quality Assessment
Lecture Notes in Computer ScienceZiheng Jia, Zicheng Zhang, Yuqin Cao
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Recaptured Image Forensics Based on Quality Aware and Histogram Feature
2017The recaptured images forensics has drawn much attention in forensics community. The technology can provide some evidences for copyright protection and protect the face spoofing system to a certain degree. In this paper, we propose an algorithm to detect the images recaptured from LCD screen.
Pengpeng Yang 0001 +3 more
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2021 6th International Conference for Convergence in Technology (I2CT), 2021
Assessment of the quality of the no-reference images in real-life applications is of immense importance. The no-reference image quality assessment (NR-IQA) algorithms are designed to measure quantitatively and accurately the perpetual quality of an image without any prior knowledge about its reference.
Chirag Kyal +2 more
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Assessment of the quality of the no-reference images in real-life applications is of immense importance. The no-reference image quality assessment (NR-IQA) algorithms are designed to measure quantitatively and accurately the perpetual quality of an image without any prior knowledge about its reference.
Chirag Kyal +2 more
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Stress and Anxiety Measurement "In-the-Wild" Using Quality-aware Multi-scale HRV Features
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Most studies, however, have been conducted in controlled laboratory environments with artificially-
Abhishek Tiwari 0003 +2 more
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Journal of Electronic Imaging, 2016
The performance of conventional denoising algorithms is usually controlled by one or several parameters whose optimal settings depend on the contents of the processed images and the characteristics of the noises. Among these parameters, noise level is a fundamental parameter that is always assumed to be known by most of the existing denoising ...
Shaoping Xu, Lingyan Hu, Xiaohui Yang
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The performance of conventional denoising algorithms is usually controlled by one or several parameters whose optimal settings depend on the contents of the processed images and the characteristics of the noises. Among these parameters, noise level is a fundamental parameter that is always assumed to be known by most of the existing denoising ...
Shaoping Xu, Lingyan Hu, Xiaohui Yang
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2019 IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT), 2019
Video-based person re-identification is an important research area in computer vision. Quality Aware Network (QAN) is a model that is robust to noise in the videos. It calculates the weighted average of all the frames' image features, with each frame's quality score as the weight, and generates a video feature.
Yude Shi, Xiao Ke
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Video-based person re-identification is an important research area in computer vision. Quality Aware Network (QAN) is a model that is robust to noise in the videos. It calculates the weighted average of all the frames' image features, with each frame's quality score as the weight, and generates a video feature.
Yude Shi, Xiao Ke
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Quality-Aware Bag of Modulation Spectrum Features for Robust Speech Emotion Recognition
IEEE Transactions on Affective Computing, 2022Shruti Rajendra Kshirsagar +1 more
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No-Reference Underwater Image Quality Assessment Based on Quality-Aware Features
SSRN Electronic Journal, 2022Yuxuan Li +3 more
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