Activity 17 – Neural Networks

In this activity, we were given the task to classify objects using neural network. As an overview, a neural network is a computational model of how neurons work in the brain and this model can also be used to other networks.

Figure 1 shows the training subjects used. These objects were taken from Activity 15 but there is an additional object used. These training subjects act as the combination of networks and place each class their corresponding number by applying neural network model.

Figure 1. Training subjects

Figure 2 shows the test subjects to be classified if they correspond to their real class.

Figure 2. Test subjects

Figure 3 shows the result of classifying each class from Figure 2. The number 1 corresponds that the test subject is correlated to its corresponding training subject. Another trial was done using different sequence shown in Figure 4. It is seen that the corresponding class does not give the real class of the subject.

Figure 3. Result with the same order as the training subject

Figure 4. Another result with different order of test subjects

I am giving myself a grade of 10. I would like to thank TJ Abregana for helping me do the past 3 activities. Thank you for you time in reading this blog.

Activity 16 – Probabilistic Classification

In this activity, 2 classes from Activity 15 were used, 5 peso coin and cards and a new classification technique was used. The technique is quite tricky but with the help of TJ Abregana, I finally understand how it works. Linear Discriminant Analysis (LDA) is used for linearly separable groups thus these groups can be separated by linear combination of features that describe an object. The formula for LDA is given by:

where λ_jk is the loss from the wrong decision,p(ω_k|x) is the probability that a pattern x came from the class ω_k and r_k is the risk of loss associated with class ω_k.

From this classification technique, five out of five test subjects of both cards and 5 peso coin where classified to their proper classes. I guess LDA is a more precise technique in classifying features of one object since it factors a loss from a wrong decision made.

2 more activities and I’m done. I will be giving myself a grade of 9.2 for this activity. Sorry for the late submission of this blog.

Activity 15 – Pattern Recognition

In this activity, we were given a task to find a pattern depending on the training subjects that we captured. Figure 1 shows the training subjects. These were cropped from a picture taken in class. From these training subjects, the class representative of each type was obtained.

Figure 1. Training subjects subjects

Next figure shows the test subjects. Each test subject were classified using Minimum Distance to know if the test subject belongs a particular class. I have chosen that the test subject is part of one particular class if the Minimum Distance value is the largest value of the 3 class.

Figure 2. Test subjects

Out of the five test subjects of 5-peso coin, three was identified as a 5-peso coin. For the cards, five out of five cards were identified while for the leaves, there were no test subjects identified as part of the class of leaves.

Finally, I have finished blogging this one. Sorry for the late upload. I will rate myself as 8.6. Thank you.

Activity 14 – Color Image Segmentation

In image segmentation, a region of interest (ROI) must be selected before any processing is done. This ROI will be use to segment the whole image based on its feature.

Figure 1 shows an image of the object which is a pair of red shoes.

Figure 1. Original image

The ROI for this image was selected and is shown in Figure 2. Below the ROIs are its corresponding histogram. It can be seen that both histograms are different from one another thus the red-ness of the two ROIs are different.

Figure 2. (top,left)Horizontal and (top,right)vertical patches from the object and (bottom)their corresponding histograms

Using the ROIs, the image was reconstructed using Parametric Probability Distribution and Non-Parametric Probability Distribution.

Table 1. Comparison between the two techniques

It can be seen in the 2nd column that both images are not the same in terms of the lighting. This is because the two patches were not cropped on the same area so the area where the patches were obtained  was highlighted. For both patches in the 3rd column, both images have a few difference in terms of lighting. Comparing the two techniques, even the non-parametric probability distribution takes time to finish the reconstruction, it produces almost similar reconstruction even though the patches are not taken on the same place.

I know this is too late but still I made it. I give myself a grade of 9.3 for doing this activity. Thank you for your time.