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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

Showing 161170 of 339 papers

TitleStatusHype
Keypoint-based Stereophotoclinometry for Characterizing and Navigating Small Bodies: A Factor Graph ApproachCode0
Pose2Seg: Detection Free Human Instance SegmentationCode0
PifPaf: Composite Fields for Human Pose EstimationCode0
Hand Keypoint Detection in Single Images using Multiview BootstrappingCode0
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
Conditional Negative Sampling for Contrastive Learning of Visual RepresentationsCode0
Multi-Person Pose Estimation with Local Joint-to-Person AssociationsCode0
Simple and Lightweight Human Pose EstimationCode0
Deep Alignment Network: A convolutional neural network for robust face alignmentCode0
Video-based Sequential Bayesian Homography Estimation for Soccer Field RegistrationCode0
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