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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 131–140 of 339 papers

TitleStatusHype
Learning a Descriptor-Specific 3D Keypoint Detector—0
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images—0
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training—0
Few-Shot Keypoint Detection as Task Adaptation via Latent Embeddings—0
CNSv2: Probabilistic Correspondence Encoded Neural Image Servo—0
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection—0
3D Keypoint Detection Based on Deep Neural Network with Sparse Autoencoder—0
Fast keypoint detection in video sequences—0
Cluster-Based Point Set Saliency—0
Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types—0
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