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Keypoint Detection

Keypoint Detection is essential for analyzing and interpreting images in computer vision. It involves simultaneously detecting and localizing interesting points in an image. Keypoints, also known as interest points, are spatial locations or points in the image that define what is interesting or what stands out. They are invariant to image rotation, shrinkage, translation, distortion, etc. Keypoints examples are body joints, facial landmarks, or any other salient points in objects. Keypoints have uses in problems such as pose estimation, object detection and tracking, facial analysis, and augmented reality.

( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )

Papers

Showing 7180 of 339 papers

TitleStatusHype
3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data0
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detectionCode0
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion ApproachCode1
SD2Event:Self-supervised Learning of Dynamic Detectors and Contextual Descriptors for Event Cameras0
FC-GNN: Recovering Reliable and Accurate Correspondences from InterferencesCode0
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions0
An Effective Image Copy-Move Forgery Detection Using Entropy InformationCode0
VoxelKP: A Voxel-based Network Architecture for Human Keypoint Estimation in LiDAR DataCode1
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph ApproachCode0
Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection (extended version)Code0
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