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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 271–280 of 339 papers

TitleStatusHype
Motion-Aware Transformer For Occluded Person Re-identification—0
Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network—0
Multi-Person Pose Estimation with Enhanced Feature Aggregation and Selection—0
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines—0
Multi-Scale Supervised Network for Human Pose Estimation—0
My Emotion on your face: The use of Facial Keypoint Detection to preserve Emotions in Latent Space Editing—0
NeMo: Learning 3D Neural Motion Fields From Multiple Video Instances of the Same Action—0
OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models—0
On Mutual Information in Contrastive Learning for Visual Representations—0
On the Visibility of Point Clouds—0
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