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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 311320 of 339 papers

TitleStatusHype
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localizationCode0
KP2Dtiny: Quantized Neural Keypoint Detection and Description on the EdgeCode0
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph ApproachCode0
Regressive Domain Adaptation for Unsupervised Keypoint DetectionCode0
Spatial regularisation for improved accuracy and interpretability in keypoint-based registrationCode0
Interspecies Knowledge Transfer for Facial Keypoint DetectionCode0
Improving the matching of deformable objects by learning to detect keypointsCode0
Human Keypoint Detection by Progressive Context RefinementCode0
StarMap for Category-Agnostic Keypoint and Viewpoint EstimationCode0
Attend to Who You Are: Supervising Self-Attention for Keypoint Detection and Instance-Aware AssociationCode0
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