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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 271280 of 339 papers

TitleStatusHype
Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image0
Weakly-supervised High-fidelity Ultrasound Video Synthesis with Feature Decoupling0
Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation0
Topology Repairing of Disconnected Pulmonary Airways and Vessels: Baselines and a DatasetCode0
Unsupervised learning of object semantic parts from internal states of CNNs by population encodingCode0
RF-Net: An End-to-End Image Matching Network based on Receptive FieldCode0
Dynamic Convolution: Attention over Convolution KernelsCode0
CoDeF: Content Deformation Fields for Temporally Consistent Video ProcessingCode0
Towards High Performance One-Stage Human Pose EstimationCode0
Tracking Object Positions in Reinforcement Learning: A Metric for Keypoint Detection (extended version)Code0
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