A comparative analysis of the hybrid optical neural network-type filters performance within cluttered scenes

We compare the hybrid optical neural network-type of filters´ performance within cluttered scenes. We have tested the unconstrained-, constrained-, and modified-hybrid optical neural network filters´ tolerance to background clutter in the input scene by the insertion of training images and non-training out-of-class images into different car park scenes. We have plotted the isometrics of the correlation planes for the different conducted tests and we recorded the peak-to-secondary peak ratio values. All the filters have been proven to be able to recognize the true-class objects. However, the comparison for first time of each of the filter´s performance in relation with the others demonstrates the benefits of each filter to be employed for recognizing the true-class objects within the cluttered scenes.