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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 201210 of 339 papers

TitleStatusHype
Task-Aware Sampling Layer for Point-Wise Analysis0
Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields0
Blur-Countering Keypoint Detection via Eigenvalue Asymmetry0
BonnBeetClouds3D: A Dataset Towards Point Cloud-based Organ-level Phenotyping of Sugar Beet Plants under Field Conditions0
ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition0
CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild0
ClothesNet: An Information-Rich 3D Garment Model Repository with Simulated Clothes Environment0
Cluster-Based Point Set Saliency0
CNSv2: Probabilistic Correspondence Encoded Neural Image Servo0
CoKe: Localized Contrastive Learning for Robust Keypoint Detection0
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