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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 31–40 of 339 papers

TitleStatusHype
2.5D U-Net with Depth Reduction for 3D CryoET Object IdentificationCode1
Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images—0
Video-based Surgical Tool-tip and Keypoint Tracking using Multi-frame Context-driven Deep Learning Models—0
Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction—0
MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching—0
Refinement Module based on Parse Graph of Feature Map for Human Pose Estimation—0
Corn Ear Detection and Orientation Estimation Using Deep Learning—0
Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential InformationCode1
ZeroKey: Point-Level Reasoning and Zero-Shot 3D Keypoint Detection from Large Language Models—0
Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment AnythingCode1
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