Performance Evaluation of RANSAC Family

By Sunglok Choi, Taemin Kim, Wonpil Yu. British Machine Vision Conference, 2009

495 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4bf76d1575cbd20f599d7475d58be1e70e043ba5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Pascal Fua
2. Luc Van Gool
3. Eduard Trulls
4. Andrew Zisserman
5. Andrea Vedaldi
6. Mathieu Salzmann
7. Kwang Moo Yi
8. Dacheng Tao

In Electrical and Electronic Engineering

1. Mathieu Salzmann
2. Dan Feldman

In Statistics and Probability

1. Mathieu Salzmann
2. Vincent Lepetit
3. Pascal Fua
4. Dan Feldman
5. Neil D. Lawrence

Citations in the corpus

• Core‐sets: An updated survey (2019, methodology): “Unlike techniques such as RANSAC (Choi, Kim, & Yu, 1997), while the first iteration uses random sampling, the overall algorithm is adaptive: in each layer we remove the “ main stream/cluster ” of the data.”
• Learning to Find Good Correspondences (2017, background): “A comprehensive study on this topic can be found in [6, 22].”