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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 171180 of 339 papers

TitleStatusHype
From Web Data to Real Fields: Low-Cost Unsupervised Domain Adaptation for Agricultural Robots0
GeoLayout: Geometry Driven Room Layout Estimation Based on Depth Maps of Planes0
GKNet: grasp keypoint network for grasp candidates detection0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations0
High Accuracy Face Geometry Capture using a Smartphone Video0
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition0
Human keypoint detection for close proximity human-robot interaction0
Human Pose Estimation in Monocular Omnidirectional Top-View Images0
Improved 2D Keypoint Detection in Out-of-Balance and Fall Situations -- combining input rotations and a kinematic model0
IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose0
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