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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 241–250 of 339 papers

TitleStatusHype
A Low Rank Promoting Prior for Unsupervised Contrastive Learning—0
Real-time Keypoints Detection for Autonomous Recovery of the Unmanned Ground Vehicle—0
Exploring Set Similarity for Dense Self-supervised Representation Learning—0
Task-Aware Sampling Layer for Point-Wise Analysis—0
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?—0
Real-Time Human Pose Estimation on a Smart Walker using Convolutional Neural Networks—0
GKNet: grasp keypoint network for grasp candidates detection—0
Unsupervised Visual Attention and Invariance for Reinforcement Learning—0
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
End-to-end learning of keypoint detection and matching for relative pose estimation—0
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