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Quality-aware features-based noise level estimator for block matching and three-dimensional filtering algorithm

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
openaire   +2 more sources

Improved Quality Aware Network Using Quality and Max Pooling Based Late Feature Fusion for Video-Based Person Re-Identification

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
openaire   +1 more source

Quality-Aware Bag of Modulation Spectrum Features for Robust Speech Emotion Recognition

IEEE Transactions on Affective Computing, 2022
Shruti Rajendra Kshirsagar   +1 more
openaire   +1 more source

No-Reference Underwater Image Quality Assessment Based on Quality-Aware Features

SSRN Electronic Journal, 2022
Yuxuan Li   +3 more
openaire   +1 more source

Quality-Aware Multimodal Fusion Reveals Implicit Identity in Valence-Arousal Features

Conventional face recognition relies on static appearance cues and degrades in unconstrained settings with expression variation, occlusion, and poor lighting. We hypothesize that audiovisual expression dynamics carry identity-discriminative information complementary to static appearance, and that extracting this signal requires multimodal ...
Kim, Jisu, Riggan, Benjamin S.
openaire   +1 more source

SCOPE-FE: Structured Control of Operator and Pairwise Exploration for Feature Engineering via Quality-Aware Candidate-Space Reduction

Substantially revised version. FeatureClustering was changed from fuzzy soft clustering to deterministic agglomerative hard clustering. The methodology and experiments were extensively updated, including exhaustive candidate audits, budget-matched randomization controls, component ablations, probe-seed sensitivity, and intra-vs-inter cluster ...
Park, Minhee   +3 more
openaire   +1 more source

Learning time-aware features for action quality assessment

Pattern Recognition Letters, 2022
Yu Zhang, Siya Mi, Wei Xiong
exaly  

DACNN: Blind Image Quality Assessment via a Distortion-Aware Convolutional Neural Network

IEEE Transactions on Circuits and Systems for Video Technology, 2022
Sam Kwong, Nam Ling, Yuming Fang
exaly  

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