SOTAVerified

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 2130 of 339 papers

TitleStatusHype
Multiscale Feature Importance-based Bit Allocation for End-to-End Feature Coding for Machines0
Keypoint Detection and Description for Raw Bayer Images0
DaD: Distilled Reinforcement Learning for Diverse Keypoint DetectionCode2
REF-VLM: Triplet-Based Referring Paradigm for Unified Visual DecodingCode1
Spatial regularisation for improved accuracy and interpretability in keypoint-based registrationCode0
Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing0
A Novel Streamline-based diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection0
CNSv2: Probabilistic Correspondence Encoded Neural Image Servo0
Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets0
Rewards-based image analysis in microscopy0
Show:102550
← PrevPage 3 of 34Next →

No leaderboard results yet.