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Illumination and Reflectance Estimation with its Application in Foreground Detection.

Gang Jun Tu¹, Henrik Karstoft², Lene Juul Pedersen³

¹Department of Animal Science, Aarhus University, 8830 Tjele, Denmark. gangjun.tu@agrsci.dk.

Sensors (Basel, Switzerland)

|September 8, 2015

Summary

This study presents a new method using wavelet theory to separate image illumination and reflectance. This approach enables accurate sow segmentation in complex farm environments, even with changing light.

Keywords:

foreground detection grayscale video recordings homomorphic wavelet filter illumination and reflectance estimation wavelet quotient image model

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Area of Science:

Computer Vision
Image Processing
Machine Learning

Background:

Accurate image analysis is crucial for livestock monitoring.
Existing methods struggle with complex environmental factors like variable lighting.

Purpose of the Study:

To develop a novel image processing technique for estimating illumination and reflectance.
To create an algorithm for segmenting sows in challenging video recordings.

Main Methods:

Utilizing an illumination-reflectance model and wavelet theory.
Applying a homomorphic wavelet filter (HWF) and wavelet quotient image (WQI) model.
Employing dyadic wavelet transform for component estimation.

Main Results:

Successfully estimated illumination and reflectance components.
Developed a sow segmentation algorithm effective in complex farrowing pen environments.
Demonstrated robustness against light changes, static foregrounds, and dynamic backgrounds.

Conclusions:

The proposed method accurately estimates image illumination and reflectance.
The developed algorithm effectively segments domestic animals in challenging real-world conditions.
This technique offers a viable solution for automated livestock monitoring.