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GFA-Net: Geometry-Focused Attention Network for Six Degrees of Freedom Object Pose Estimation.
Sensors (Basel)
December 2024
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China.
Six degrees of freedom (6-DoF) object pose estimation is essential for robotic grasping and autonomous driving. While estimating pose from a single RGB image is highly desirable for real-world applications, it presents significant challenges. Many approaches incorporate supplementary information, such as depth data, to derive valuable geometric characteristics.
View Article and Find Full Text PDFMulti-Biometric Feature Extraction from Multiple Pose Estimation Algorithms for Cross-View Gait Recognition.
Sensors (Basel)
November 2024
Department of Computer Science and Digital Technologies, University of East London, London E16 2RD, UK.
Gait recognition is a behavioral biometric technique that identifies individuals based on their unique walking patterns, enabling long-distance identification. Traditional gait recognition methods rely on appearance-based approaches that utilize background-subtracted silhouette sequences to extract gait features. While effective and easy to compute, these methods are susceptible to variations in clothing, carried objects, and illumination changes, compromising the extraction of discriminative features in real-world applications.
View Article and Find Full Text PDFDense projection fusion for 3D object detection.
Sci Rep
October 2024
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510640, China.
Fusing information from LiDAR and cameras can effectively enhance the overall perceptivity of autonomous vehicles in various scenarios. Despite the relatively good results achieved by point-wise fusion and Bird's-Eye-View (BEV) fusion, they still cannot fully leverage the image information and lack of effective depth information. For any fusion methods, the multi-modal features first need to be concatenated along the channel, and then the fused features are extracted using convolutional layers.
View Article and Find Full Text PDFMarkerless motion analysis to assess reaching-sideways in individuals with dyskinetic cerebral palsy: A validity study.
J Biomech
August 2024
Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium; Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium; Department of Rehabilitation Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Rehabilitation & Development, Amsterdam Movement Sciences, Amsterdam, the Netherlands. Electronic address:
This study aimed to evaluate clinical utility of 2D-markerless motion analysis (2DMMA) from a single camera during a reaching-sideways-task in individuals with dyskinetic cerebral palsy (DCP) by determining (1) concurrent validity by correlating 2DMMA against marker-based 3D-motion analysis (3DMA) and (2) construct validity by assessing differences in 2DMMA features between DCP and typically developing (TD) peers. 2DMMA key points were tracked from frontal videos of a single camera by DeepLabCut and accuracy was assessed against human labelling. Shoulder, elbow and wrist angles were calculated from 2DMMA and 3DMA (as gold standard) and correlated to assess concurrent validity.
View Article and Find Full Text PDFExperimental measurement of local noise power spectrum (NPS) in photon counting detector-CT (PCD-CT) using a single data acquisition.
Med Phys
June 2024
Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
Background: Accurate noise power spectra (NPS) measurement in clinical X-ray CT exams is challenging due to the need for repeated scans, which expose patients to high radiation risks. A reliable method for single CT acquisition NPS estimation is thus highly desirable.
Purpose: To develop a method for estimating local NPS from a single photon counting detector-CT (PCD-CT) acquisition.
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