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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 231–240 of 339 papers

TitleStatusHype
Rotation-Equivariant Keypoint Detection—0
SD2Event:Self-supervised Learning of Dynamic Detectors and Contextual Descriptors for Event Cameras—0
Self-supervised 3D Patient Modeling with Multi-modal Attentive Fusion—0
Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images—0
Self-Supervised Keypoint Detection with Distilled Depth Keypoint Representation—0
Semantic Image Attack for Visual Model Diagnosis—0
Semi-supervised Dense Keypoints Using Unlabeled Multiview Images—0
Semi-supervised Keypoint Localization—0
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving—0
ShaRPy: Shape Reconstruction and Hand Pose Estimation from RGB-D with Uncertainty—0
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