ALCN: Adaptive Local Contrast Normalization for Robust Object Detection and 3D Pose Estimation

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

To be robust to illumination changes when detecting objects in images, the current
trend is to train a Deep Network with training images captured under many different
lighting conditions. Unfortunately, creating such a training set is very cumbersome, or
sometimes even impossible, for some applications such as 3D pose estimation of specific
objects, which is the application we focus on in this paper. We therefore propose a
novel illumination normalization method that lets us learn to detect objects and estimate
their 3D pose under challenging illumination conditions from very few training samples.
Our key insight is that normalization parameters should adapt to the input image. In
particular, we realized this via a Convolutional Neural Network trained to predict the
parameters of a generalization of the Difference-of-Gaussians method. We show that our
method significantly outperforms standard normalization methods and demonstrate it on
two challenging 3D detection and pose estimation problems.
Originalspracheenglisch
TitelBritish Machine Vision Conference
PublikationsstatusVeröffentlicht - 2017
VeranstaltungBritish Machine Vision Conference - London, Großbritannien / Vereinigtes Königreich
Dauer: 4 Sep 20177 Apr 2018

Konferenz

KonferenzBritish Machine Vision Conference
LandGroßbritannien / Vereinigtes Königreich
OrtLondon
Zeitraum4/09/177/04/18

Fingerprint

Lighting
Neural networks
Object detection

Dies zitieren

ALCN: Adaptive Local Contrast Normalization for Robust Object Detection and 3D Pose Estimation. / Rad, Mahdi; Roth, Peter M.; Lepetit, Vincent.

British Machine Vision Conference. 2017.

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Rad, M, Roth, PM & Lepetit, V 2017, ALCN: Adaptive Local Contrast Normalization for Robust Object Detection and 3D Pose Estimation. in British Machine Vision Conference., London, Großbritannien / Vereinigtes Königreich, 4/09/17.
@inproceedings{5672dc9a6cab46369e51842c296b9551,
title = "ALCN: Adaptive Local Contrast Normalization for Robust Object Detection and 3D Pose Estimation",
abstract = "To be robust to illumination changes when detecting objects in images, the currenttrend is to train a Deep Network with training images captured under many differentlighting conditions. Unfortunately, creating such a training set is very cumbersome, orsometimes even impossible, for some applications such as 3D pose estimation of specificobjects, which is the application we focus on in this paper. We therefore propose anovel illumination normalization method that lets us learn to detect objects and estimatetheir 3D pose under challenging illumination conditions from very few training samples.Our key insight is that normalization parameters should adapt to the input image. Inparticular, we realized this via a Convolutional Neural Network trained to predict theparameters of a generalization of the Difference-of-Gaussians method. We show that ourmethod significantly outperforms standard normalization methods and demonstrate it ontwo challenging 3D detection and pose estimation problems.",
author = "Mahdi Rad and Roth, {Peter M.} and Vincent Lepetit",
year = "2017",
language = "English",
booktitle = "British Machine Vision Conference",

}

TY - GEN

T1 - ALCN: Adaptive Local Contrast Normalization for Robust Object Detection and 3D Pose Estimation

AU - Rad, Mahdi

AU - Roth, Peter M.

AU - Lepetit, Vincent

PY - 2017

Y1 - 2017

N2 - To be robust to illumination changes when detecting objects in images, the currenttrend is to train a Deep Network with training images captured under many differentlighting conditions. Unfortunately, creating such a training set is very cumbersome, orsometimes even impossible, for some applications such as 3D pose estimation of specificobjects, which is the application we focus on in this paper. We therefore propose anovel illumination normalization method that lets us learn to detect objects and estimatetheir 3D pose under challenging illumination conditions from very few training samples.Our key insight is that normalization parameters should adapt to the input image. Inparticular, we realized this via a Convolutional Neural Network trained to predict theparameters of a generalization of the Difference-of-Gaussians method. We show that ourmethod significantly outperforms standard normalization methods and demonstrate it ontwo challenging 3D detection and pose estimation problems.

AB - To be robust to illumination changes when detecting objects in images, the currenttrend is to train a Deep Network with training images captured under many differentlighting conditions. Unfortunately, creating such a training set is very cumbersome, orsometimes even impossible, for some applications such as 3D pose estimation of specificobjects, which is the application we focus on in this paper. We therefore propose anovel illumination normalization method that lets us learn to detect objects and estimatetheir 3D pose under challenging illumination conditions from very few training samples.Our key insight is that normalization parameters should adapt to the input image. Inparticular, we realized this via a Convolutional Neural Network trained to predict theparameters of a generalization of the Difference-of-Gaussians method. We show that ourmethod significantly outperforms standard normalization methods and demonstrate it ontwo challenging 3D detection and pose estimation problems.

M3 - Conference contribution

BT - British Machine Vision Conference

ER -