Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
SHREC '11: Robust Feature Detection and Description Benchmark
208
Zitationen
18
Autoren
2011
Jahr
Abstract
Feature-based approaches have recently become very popular in computer vision and image analysis applications, and are becoming a promising direction in shape retrieval. SHREC'11 robust feature detection and description benchmark simulates the feature detection and description stages of feature-based shape retrieval algorithms. The benchmark tests the performance of shape feature detectors and descriptors under a wide variety of transformations. The benchmark allows evaluating how algorithms cope with certain classes of transformations and strength of the transformations that can be dealt with. The present paper is a report of the SHREC'11 robust feature detection and description benchmark results
Ähnliche Arbeiten
Deep Residual Learning for Image Recognition
2016 · 216.145 Zit.
ImageNet: A large-scale hierarchical image database
2009 · 60.460 Zit.
Distinctive Image Features from Scale-Invariant Keypoints
2004 · 54.693 Zit.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
2016 · 52.689 Zit.
Going deeper with convolutions
2015 · 46.263 Zit.