**5.3 Discrimination ability**

The third tests series (Jamal-Aldin et al., 1997; Jamal-Aldin et al., 1998; Kumar & Hassebrook, 1990) was carried out to assess the discrimination ability (Refregier, 1990; Refregier, 1991, Kypraios et al., 2008) of the M-HONN system. In the tests, M-HONN system tried to discriminate between objects of different classes while retaining invariance to inclass distortions. The training set consisted of images of the Jaguar S-type for adistortion range over 20 to 70 at 10 increments. The test set consisted of one training image outof-plane rotated at 40 of the Jaguar S-type and a second image of the out-of-class RX-7 Police patrol car at the same angle of out-of-plane rotation. Two different training set configuration of still images were experimented with. Firstly, we added two images of the Jaguar S-type at 130 and 140 for the false-class of the system's training set. We constrained the false-class images of the objects to zero correlation peak-height in the synthesis of the M-HONN system's composite image. Secondly, we conducted experiments

Performance Analysis of

of M-HONN system.

**5.4 Clutter tolerance 5.4.1 Training sets** 

the Modified-Hybrid Optical Neural Network Object Recognition System Within Cluttered Scenes 57

two different training set still images configurations that we had experimented with (in first, false-class images zero peak constrained and, in second, false-class images not included in the system's composite image) helped us make a useful observation about the M-HONN system's ability to distinguish between two different classes. More specifically, the falseclass images included in the composite image, and zero peak constrained, were taken from the true-class object in different poses not included in the training set. In effect, when we tested the RX-7 police patrol car images, the system separated the input images from the trained images (unity peak constrained) of the true-class object and the false-class images (of the same true-class but zero peak constrained) as a third class. Apparently, that caused the drop of the M-HONN system discrimination ability by almost half. We have found to be a solution to the problem by including false-class images not belonging in the same the trueclass object but from a different one which it could increase further the discrimination ability

Table 1. (adapted by Kypraios et al., 2008) Discrimination Ability of M-HONN system

We have conducted several tests (Kypraios et al., 2009) for evaluating the performance of the M-HONN system in recognising multiple objects of the same class or of different classes. Several training sets were created for testing the system's performance with still images and with video sequences. The first training set consisted of still images of the Jaguar S-type car for a distortion range over 0 to 360 out-of-plane rotated at 10 increments. The second training set consisted of still images of the RX-7 Police patrol car for a distortion range approximately over 0 to 360 out-of-plane rotated at 10 increments. The third training set consisted of video frames of a Ferrari Testarossa car within a background clutter scene. The fourth training set consisted of still images of different car park scenes. A fifth training set consisted of video frames we have taken showing a sequence of a Jaguar S-type car and a Ferrari Testarossa car within a background clutter scene. All the training and test sets of the still images and of the video sequence frames were used in grey-scale bitmap format, and they were sized to 256x256. All the test and train input still images and video frames were concatenated row-by-row into a vector form prior being processed by the NNET block of the M-HONN system.

with no inclusion in the system's composite image of any false-class images. For both cases, we aimed in observing if there was any change in the class separation ability of the M-HONN system. We constrained the true-class objects to unity correlation peak-height and we used the same as before Targets for the false- and true- class images of the NNET block. The target of the false-class object is T 40 false , and the Target of the true-class object is T 40 true in the training set of the NNET block for the M-HONN system. It had no built-in information on the test images.

Fig. 8. (Adapted by Kypraios et al., 2008) shows (a) the reference angle, Θ0, and the two inclass training images at the angles Θ1 and Θ2. The test image is on the bisector at angle Θ3; (b) the correlation peak-heights for each input image over a range of Θ3 = [5° 40°] for the M-HONN system.

From the conducted experiments we drew Table 1. The forth column of Table 1 records the values taken for the in-class training image and the fifth column contains all the values taken for the out-of-class training image. It is shown from the third column of Table 1 that M-HONN system gave sufficient discrimination ability between the two objects, the Jaguar S-type car and the RX-7 Police patrol car. It produced 12% class separation (with the falseclass images included in the synthesis of the composite image with zero correlation peakheight constraint). By not including any false-class images in the system's composite image, but by setting to unity correlation peak-height constraint the true-class images and keeping constant the target of the false-class object to T 40 false and the target of the true-class object to T 40 true , the M-HONN system increased the class separation to 27%. Thus, the two different training set still images configurations that we had experimented with (in first, false-class images zero peak constrained and, in second, false-class images not included in the system's composite image) helped us make a useful observation about the M-HONN system's ability to distinguish between two different classes. More specifically, the falseclass images included in the composite image, and zero peak constrained, were taken from the true-class object in different poses not included in the training set. In effect, when we tested the RX-7 police patrol car images, the system separated the input images from the trained images (unity peak constrained) of the true-class object and the false-class images (of the same true-class but zero peak constrained) as a third class. Apparently, that caused the drop of the M-HONN system discrimination ability by almost half. We have found to be a solution to the problem by including false-class images not belonging in the same the trueclass object but from a different one which it could increase further the discrimination ability of M-HONN system.


Table 1. (adapted by Kypraios et al., 2008) Discrimination Ability of M-HONN system
