Developing our Final Report

Throughout most of the first week of May, my mentor and research partner have met to discuss the CREU template and additional topics that should be mentioned in our final report. We went through our blogs and project files to compile screenshots of our process along with results that we obtained throughout the project.

4/19/17: Gathering sample Images

I’ve been trying to accurately detect a very simple object like a watch first before moving on to our positive samples. Today I wrote scripts to download large sets of watch samples from ImageNet an image database. If the success rate increases for detecting watches within this new environment. I will apply the same techniques to the images I cropped of the narrow helix.

4/18/17: New Environment and Cropped Samples

In my previous post I mentioned that we had a 32% accuracy rate of identifying a narrow helix. To provide an alternative environment to train our cascade classifier I set up a 2gb ubuntu server provided from digital ocean to provide an alternative environment to train our cascade classifier. Using a computer with higher specs may help the performance when training, so I dowloaded the required libraries and python bindings for OpenCV on the server. In addition in my last post I also discussed how we wanted to also change our technique so I manually cropped all of our positive sample to include solely the narrow helix instead of the entire ear.

Narrow Helix Accuracy Reports

We're getting to the point where we want to explore our preliminary results.
To get a better sense of I examined and collected metrics from our most successful narrow helix trials. The following are the results from conducting 5 trials.

Narrow helix accuracy reports: 

trial 1: 1/4

trial 2: 3/7

trial 3: 1/4

trial 4: 1/5

trial 5: 2/5

Overall Accuracy: 8/25 = 32 %

We acknowledge that this is a pretty low accuracy result, but seeing where we currently is helpful for deciding our next steps. We discussed ways of increasing our accuracy, one promising method is by changing our technique to incorporate only the focused regions of the sample. (i.e. instead of a positive sample of the complete ear for detecting a narrow helix, the sample to train with will only consist of a narrow helix)

March 10th Abstract Development

I haven't made much of a contribution to the research project because I was studying for midterms and also starting feeling ill at the beginning of the week. I was able to assist with the development of the abstract that we submitted for the HU Research Day at Capitol Hill.

Retraining Classifier Results

This week I worked on retraining the haar cascade. After being able to create 1000 positive samples I used 500 negative samples as input for our narrow helix classifier. The first attempt of training only passed through 1 stage then terminated with the following error "Train dataset for temp stage can not be filled. Branch training terminated."

I learned that this occurred because the paths within my negative descriptor were incorrect,  I quickly fixed this and retrained. On the second attempt stages 0 and 1 were loaded and I was able to enter into stage 2, but once again I encountered another issue "Required leaf false alarm rate achieved. Branch training terminated."

The third attempt I made to train the classifier was much more successful than the previous. I ended up starting from scratch without loading the last xml containing the results of prior stages and reached the third stage. I then tried testing the classifier with my detect script, but I was unable to detect any narrow helixes in my sample image.

Even though I wasn't able to detect helixes within my test images, we've made meaningful progress in training the classifier. Prior to now training didn't proceed past the 1st stage. With some minor adjustments we should be able to accurately detect segments of the ear.

Project Update February 22nd

I did not blog about the project last week.

This week we met with a grad research student, Ayotunde. He provided some great insights and assisted us in moving further with the project. I was able to generate over 1000 images from one positive sample image.

This breakthrough will allow us to provide more sample images when training our classifier. The issue that prevented us from properly detecting parts of the ear was the poor accuracy of our cascade. During training our sample size was too small to go through many stages. Once I can create a larger amount of negative samples I will be able to build a more accurate cascade.