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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 326339 of 339 papers

TitleStatusHype
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
On the Visibility of Point Clouds0
Learning a Descriptor-Specific 3D Keypoint Detector0
An analysis of the factors affecting keypoint stability in scale-space0
Unsupervised learning of object semantic parts from internal states of CNNs by population encodingCode0
Keypoint Encoding for Improved Feature Extraction from Compressed Video at Low Bitrates0
Fast keypoint detection in video sequences0
Viewpoints and Keypoints0
Do Convnets Learn Correspondence?0
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition0
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