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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 221–230 of 339 papers

TitleStatusHype
Learning Enhanced Resolution-wise features for Human Pose Estimation—0
Do Convnets Learn Correspondence?—0
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection—0
EdgePoint2: Compact Descriptors for Superior Efficiency and Accuracy—0
Efficient grouping for keypoint detection—0
Enabling Privacy-Aware AI-Based Ergonomic Analysis—0
End-to-end learning of keypoint detection and matching for relative pose estimation—0
End-to-end learning of keypoint detector and descriptor for pose invariant 3D matching—0
Exploring Set Similarity for Dense Self-supervised Representation Learning—0
Facial Chick Sexing: An Automated Chick Sexing System From Chick Facial Image—0
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