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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

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Showing 211–220 of 339 papers

TitleStatusHype
Bottom-Up Human Pose Estimation Via Disentangled Keypoint RegressionCode1
Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD—0
End-to-end learning of keypoint detection and matching for relative pose estimation—0
LatentKeypointGAN: Controlling Images via Latent KeypointsCode0
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARsCode1
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
End-to-End Trainable Multi-Instance Pose Estimation with TransformersCode1
Deep Dual Consecutive Network for Human Pose EstimationCode1
Regressive Domain Adaptation for Unsupervised Keypoint DetectionCode0
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal AssociationCode2
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