MATE: Machine Learning for Adaptive Calibration Template Detection | VitaVision
PaperSensors · 2016

MATE: Machine Learning for Adaptive Calibration Template Detection

Simon DonnéJonas De VylderBart GoossensWilfried Philips
DOIView in graph
1page built on it
3more pages cite it
1narrative
3registry papers cite it

In the Atlas

ModelPrimary source of
MATE

First learned per-pixel checkerboard X-corner detector: a three-convolutional-layer CNN with 2,939 parameters trained with mean-squared-error loss against a binary corner mask and post-processed with a fixed 0.5 threshold.

Cited as a reference by 3 pages, grouped by domain

Calibration 2
CCDNCCS
Features 1
Chessboard X-Corner Detection

Appears in 1 narrative

NarrativeFinding a Chessboard

Lineage

Papers in the Atlas registry only · one dot per paper
Built upon by · 3
’03’04’05’06’07’08’09’10’11’12’13’14’15’16’17’18’19’20’21’22’23A highly accurate and robust deep checkerboard corner detector (2021)Learning-Based Distortion Correction and Feature Detection for High Precision and Robust Camera Calibration (2022)CCDN: Checkerboard Corner Detection Network for Robust Camera Calibration (2023)Using saddle points for subpixel feature detection in camera calibration targets (2003)Machine Learning for High-Speed Corner Detection (2006)Automatic detection of checkerboards on blurred and distorted images (2008)ChESS — Quick and Robust Detection of Chess-board Features (2013)ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration (2014)