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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 321330 of 339 papers

TitleStatusHype
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors0
Towards Accurate Multi-person Pose Estimation in the Wild0
A Fast Keypoint Based Hybrid Method for Copy Move Forgery Detection0
ArtTrack: Articulated Multi-person Tracking in the WildCode0
Associative Embedding: End-to-End Learning for Joint Detection and GroupingCode0
UMDFaces: An Annotated Face Dataset for Training Deep Networks0
Multi-Person Pose Estimation with Local Joint-to-Person AssociationsCode0
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation ModelCode0
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder0
Cluster-Based Point Set Saliency0
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