Results 201 to 210 of about 169,017 (241)
Learning Compact Identity Representations for Weakly Textured Hanwoo Cattle Re-Identification. [PDF]
Animals (Basel)Liu J +5 more
europepmc +1 more source
QGFace-LLaVA: Quality-Aware Controlled Fusion of Structured Side Information for Face Analysis Under Imperfect Metadata. [PDF]
Biomimetics (Basel)Feng J +5 more
europepmc +1 more source
Automated anatomically guided quality assessment for OCTA via multi-region analysis and statistical calibration. [PDF]
Biomed Opt ExpressZhang E, Yuan H, Zhang L, Huo L.
europepmc +1 more source
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A no-reference underwater image quality evaluator via quality-aware features
Journal of Visual Communication and Image Representation, 2023Guojia Hou, Lu Tan
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Quality-Aware Feature Aggregation Network for Robust RGBT Tracking
IEEE Transactions on Intelligent Vehicles, 2021RGBT tracking becomes a popular computer vision task, and has a variety of applications in visual surveillance systems, self-driving cars and intelligent transportation system. This paper investigates how to perform robust visual tracking in adverse and challenging conditions using complementary visual and thermal infrared data (RGBT tracking).
Bin Luo, Jin Tang, Yabin Zhu
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Face Detection on Distorted Images Augmented by Perceptual Quality-Aware Features
IEEE Transactions on Information Forensics and Security, 2014Motivated by the proliferation of low-cost digital cameras in mobile devices being deployed in automated surveillance networks, we study the interaction between perceptual image quality and a classic computer vision task of face detection. We quantify the degradation in performance of a popular and effective face detector when human-perceived image ...
Alan Bovik, Suriya Gunasekar
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No-Reference Video Quality Assessment Using Local Structural and Quality-Aware Deep Features
IEEE Transactions on Instrumentation and Measurement, 2023Kishor Bhurchandi +1 more
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Enhancing blind video quality assessment with rich quality-aware features
Expert Systems With ApplicationsBlind video quality assessment (BVQA) is a highly challenging task due to the intrinsic complexity of video content and visual distortions, especially given the high popularity of social media videos, which originate from a wide range of sources, and are often processed by various compression and enhancement algorithms.
Xiongkuo Min, Guangtao Zhai
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