/*
* Copyright (c) 2011-2016, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package boofcv.alg.background;
import boofcv.struct.image.GrayF32;
/**
* <p>
* Background model in which each pixel is modeled as an independent Guassian distribution. For computational
* efficiency each band is modeled as having a diagonal covariance matrix with off diagonal terms set to zero,
* i.e. each band is independent. See [1] for a summary. This is an approximation but according to several
* papers it doesn't hurt performance much but simplifies computations significantly.
* </p>
* <p>
* Internally background model is represented by two images; mean and variance, which are stored in
* {@link GrayF32} images. This allows for the mean and variance of each pixel to be interpolated,
* reducing artifacts along the border of objects.
* </p>
*
* <p>Tuning Parameters:</p>
* <ul>
* <li><b>learnRate:</b> Specifies how fast it will adapt. 0 to 1, inclusive. 0 = static 1.0 = instant. Try 0.05</li>
* <li><b>threshold:</b> Pixel's with a Mahalanobis distance ≤ threshold are assumed to be background. Consult
* a Chi-Squared table for theoretical values. 1-band try 10. 3-bands try 20. </li>
* <li><b>initial variance</b> The initial variance assigned to pixels when they are first observed. By default this is
* Float.MIN_VALUE.
* </ul>
*
* <p>
* [1] Benezeth, Y., Jodoin, P. M., Emile, B., Laurent, H., & Rosenberger, C. (2010).
* Comparative study of background subtraction algorithms. Journal of Electronic Imaging, 19(3), 033003-033003.
* </p>
*
* @author Peter Abeles
*/
public interface BackgroundAlgorithmGaussian {
/**
* Returns the initial variance assigned to a pixel
* @return initial variance
*/
float getInitialVariance();
/**
* Sets the initial variance assigned to a pixel
* @param initialVariance initial variance
*/
void setInitialVariance(float initialVariance);
/**
* Returns the learning rate.
* @return 0 (slow) to 1 (fast)
*/
float getLearnRate();
/**
* Specifies the learning rate
* @param learnRate 0 (slow) to 1 (fast)
*/
void setLearnRate(float learnRate);
float getThreshold();
void setThreshold(float threshold);
public float getMinimumDifference();
public void setMinimumDifference(float minimumDifference);
}