Exercise 2 - Image Transformation

For this exercise, we were given a code, then we were asked to have certain images to test on the code. There are 4 images, one has high contrast, one has low; one taken in daytime, one taken in night time. The code can convert the images to Image Negative, Dimmed Image, and Brightened Image.

Original Images

Image Negatives. The images look amazing here. Though the converted nighttime image is close to being all-white, it still looks good in negative.

Dimmed Images. The effect of dimming the images made them look like nighttime images.

Brightened Images. The brightened images made them look like some fog was all over the camera causing the effect. It's more noticeable on the daytime photo.

The next task was to get the gray images of the same photos, then get their histograms.

This one has many white and gray levels.

Since the original image was a nighttime image, most of the colors were dark, thus there are more black levels and grayish-black levels.

While the original images was a day time images, due to binarization, there were more gray levels than white levels.

This one had more black levels compared to gray and white levels.

Exercise 11 - Chroma Keying

https://www.youtube.com/watch?v=xPe6O__Iq5o&t=3s

For this exercise, we had to emulate an Opening Theme from an anime series. Fortunately for us, we got the Opening Theme of Slam Dunk. While it was embarrassing to be doing such things, it was for the academic value. It went well, to be honest... except for the rendering part. The code was easily made, but we had difficulties in rendering the video, since it took so much time and it used up so much ram that I had to restart my laptop after a video has rendered. Also, my laptop would really hang when 480p and better videos were rendered, so we had to stick to 360p instead. While the video output was embarrassing to watch, I could say we did a good job on this exercise.

Exercise 10 - Color Tracking

The exercise is now about Color Tracking, and what we were required to do is to create a triangle on the middle, then get the mean of the color value. Once the mean has been retrieved, we use it as a scalar value and use it as the color of the line drawing. We were given a sample code already, so the next step was just to modify the color of the line.

Exercise 9 - Background Subtraction

This exercise involved taking out the background and leaving the foreground of the video. Our lab instructor had already provided the code, but the output of the said code was a black foreground. Our exercise involved producing the actual foreground, and on a video output file.

While we weren't able to finish the exer, we did understand how background subtraction on videos work. The video will have to be spliced to frames, then each frame would be studied for a possible background, then the studied background is subtracted to all frames, then the processed frames are made to a video again.

Exercise 8 - Color Tracking

This exercise was about false coloring, and we were required to apply our own color scale on the sea part of the provided images. It was fairly easy, as we used the inRange() function for identifying the blue waters part of the images.

Exercise 7 - OCR Part 1

For this exercise, we were made to produce 20 images of handwritten numbers, with one number per image, then and another 20 images of computer-made numbers, then using our own implementation of OCR, we must be able to study and learn the features of the computer-made numbers, then identify the numbers written on the handwritten number images. The determinant for identifying the numbers was using a Euclidean distance formula. We used Area, Height, and Width for the exercise.

While our exercise worked without much error, we were only able to identify 25% of the images. It was still good, despite that we already had used 3 features for the exercise. Some identifications were really far (like 3 identified as 7) and some are close (like 9 identified as 0).

Exercise 6 - Erosion-Dilation

This exercise is about using image erosion and image dilation techniques in order to fix up some sample student evaluation forms, then tally the results. What we did was binarize the image first through thresholding, then experiment on using the erode and dilate to emphasize the marks that were shaded. We mapped the individual coordinates first, then tally the results and record them on a text file.