foveatedFeatures

This is the source code of the Foveated Features Extraction, as a result of research done by Rafael Beserra. It uses Opencv2.4.8 for implementation and no more others dependencies. Consider citing the related paper to this project: Rafael Beserra Gomes, Bruno Motta de Carvalho, Luiz Marcos Garcia Gonçalves, Visual attention guided features selection with foveated images, Neurocomputing, Volume 120, 23 November 2013, Pages 34-44, ISSN 0925-2312, http://dx.doi.org/10.1016/j.neucom.2012.10.033.

This software is under GNU GPL (read LICENSE file for more informations).

Foveation variable summary

The feature extraction process is done by a sequence of n steps. This differs slight from the original paper. If you want a easy configuration, use one of these values:

• numberOfLevels = 4, etavector = [4, 3, 2, 1], levels = [0, 1, 2, 3], b = [1, 1, 1, 1]
• numberOfLevels = 5, etavector = [5, 4, 3, 2, 1], levels = [0, 1, 2, 3, 4], b = [1, 1, 1, 1, 1]

In the original paper, there are a B vector and a eta vector, each one with numberOfLevels elements. For example, 4 levels, B = {1, 1, 0, 1} and eta = {4, 3, 2, 1}, this set means that there are 4 steps for feature extraction:

• first level (largest one) computes features in the fourth octave
• second level computes features in the third octave
• third level would compute features in the second octave, but is does not because B[3] = 0
• fourth level computes features in the first octave

See section 3.2 from neurocomputing paper for examples.

In this implementation, bvector (B), etavector (eta) and levelvector have n elements, the set {bvector[i], etavector[i], levelvector[i]} represents a feature extraction step.

• bvector: [b1, b2, ..., bn]: a vector where bi is 0 if the feature extraction step number i should be discarded or 1, otherwise
• etavector: [e1, e2, ..., en]: a vector where ei is the octave (> 0) for which the feature extraction step number i should be performed
• levelvector: [l1, l2, ..., ln]: a vector where li is the foveated model level (>= 0 and < numberOfLevels) for which the feature extraction step number i should be performed

For example: 4 levels, B = {1, 0, 1, 1, 1}, eta = {3, 4, 2, 3, 1} and levels = {0, 0, 1, 1, 3}, this set means that there are 5 steps for feature extraction:

• first step (B[1] = 1, eta[1] = 3 and levels[1] = 0): a feature extraction in the third octave is performed in the first level (largest one)
• second step (B[2] = 0, eta[2] = 4 and levels[2] = 0): a feature extraction in the fourth octave would be performed in the first level (largest one), but it does not because B[2] = 0
• third step (B[3] = 1, eta[3] = 2 and levels[3] = 1): a feature extraction in the second octave is performed in the second level
• fourth step (B[4] = 1, eta[4] = 3 and levels[4] = 1): a feature extraction in the third octave is performed in the second level
• fifth step (B[5] = 1, eta[5] = 1 and levels[5] = 3): a feature extraction in the first octave is performed in the fourth level

This also means that:

• first level (largest one) computes features in the third octave (levels[1] = 0, levels[2] = 0, eta[1] = 3, eta[2] = 4, but B[2] = 0)
• second level computes features in the second and third octave (levels[3] = 1, levels[4] = 1, eta[3] = 2, eta[4] = 3)
• third level has no feature extraction (since no level = 2)
• fourth level computes features in the first octave (levels[5] = 3, eta[5] = 1)

Usage

First, you must create a yml file that contains initial values of the foveated model. You MUST specify the following parameters:

• smallestLevelWidth: 0 < value < imageWidth
• smallestLevelHeight: 0 < value < imageHeight
• numberOfLevels: value > 1
• bvector: [b1, b2, ..., bn], where each value is 0 or 1
• etavector: [e1, e2, ..., en], where each value > 0
• levelvector: [l1, l2, ..., ln], where value >= 0 and value < numberOfLevels Note that these last 3 sequence must have the same number of elements

Second, use the struct FoveatedHessianDetectorParams to specify:

• the image size (no default value)
• the YML file path containing the foveated model parameters (no default file)
• hessian threshold (default value: 100);
• the number of layers in each octave (it is preferable to use default value: 3).

Use FoveatedHessianDetectorParams(int imageWidth, int imageHeight, String ymlFile) construtor to specify the original image size and the yml file file path.

After that, use foveatedHessianDetector function: static void foveatedHessianDetector(InputArray _img, InputArray _mask, vector& keypoints, FoveatedHessianDetectorParams params);