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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

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Showing 181–190 of 339 papers

TitleStatusHype
ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from Large Language Models—0
Certifying Robustness of Learning-Based Keypoint Detection and Pose Estimation Methods—0
2D Human Pose Estimation: A Survey—0
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder—0
3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data—0
3D Pose Estimation of Tomato Peduncle Nodes using Deep Keypoint Detection and Point Cloud—0
A Comparison of CNN and Classic Features for Image Retrieval—0
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection—0
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training—0
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples—0
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