After our final critique of the Typology machine project, I was contemplating how I could have delivered the concept in a more efficient and direct way. Some of the feedback mentioned how the 3d captures were not as clear and the details that I wanted to show were not present. Therefore, I wanted to attempt the project again in a different light. (no pun intended…)
I have a lot of shoes at home, around 10. Some are more worn out than others. Some I wear more often than the special-occasion ones. I simply wanted to photograph them with different lights. Original image, image with only diffused light, image with only specular light and image with UV light.
I was more considerate of the presentation of the typology this time. I initially tried to mask out the shoe but I decided to keep it with the background. I wanted a sense of environment that would be absent if I were to mask it out of context.
For the layout of the images, I took inspiration from paper dice templates–how cardboard boxes look when taken apart. I thought it would be interesting because the concept is that the shoes were photographed inside the shoe box.
When you first click on the pages it takes some loading time but without any action the images will slowly change from one state to another.
What I find interesting about this set of typology is that for the leather shoes (shoe2) it is almost pitch black with no details for the diffused light and uv light images but for the specular it almost replicates the figure of the original image. Through the changes we can see how the material completely bounces off the light. In comparison to the slippers (shoe1), which you can hardly make out the lines of the shoe in the specular light image.
How much does your skin color vary across different parts of your body?
While most of us think of ourselves as having one consistent skin color, this typology machine aims to capture the subtle variations of skin tone within a single individual, creating abstract color portraits that highlight these differences.
I started this project by determining which areas of the body would be the focus for color data collection. To ensure comfort and encourage participation, I selected nine areas: the forehead, upper lip, lower lip, top of the ear (cartilage), earlobe, cheek, palm of the hand, and back of the hand. I also collected hair color data to include in the visuals.
I then constructed a ‘capture box’ equipped with an LED light and a webcam, with a small opening for participants to place their skin. This setup ensured standardized lighting conditions and a consistent distance from the camera. To avoid camera’s automatic adjustments to exposure and tint, I used webcam software that disabled color and lighting corrections, allowing me to capture raw and unfiltered skin tones.
Box building and light testing:
Next, I recruited 87volunteers and asked each to have six photos taken that would allow me to capture the 9 specific color areas. The photos included front and back of their hands, forehead, ear, cheek, and mouth.
Once the images were collected, I developed an application to allow me to go through each photo, select a 10×10 pixel area and identified the corresponding body part. The color data was then averaged across the 100 pixels, labeled accordingly, and stored in a JSON file, organized by participant and skin location.
A snippet of the image labeling and color selection process:
Using Adobe Illustrator, I wrote another script to map the captured color values into colors in an abstract designs, creating a unique image for each person.
The original shape in Adobe Illustrator and three examples of how the colors where mapped.
Overall, I’m pleased with the project’s outcome. The capture process was successful, and I gained valuable experience automating design workflows. While I didn’t have time to conduct a deeper analysis of the RGB data, the project has opened opportunities for further exploration, including examining patterns in the collected color data.
A grid-like visual representation of the entire dataset:
In this project, I used the “Scan Documents” feature on the iPhone Notes app to isolate evidence of “work” (paint marks, scratches, debris) from the surface of cutting mats.
The Discovery Process
The project just started with wondering if I could use the scanning feature for something other than documents. The in-class activity where we took portable scanners inspired me mainly because it was so fun to do! I also liked the level of focus it gave to the subject you were scanning. However, the portable scanners were limited because of their size and uniformity. This is why I turned to my Notes app scanner, which I knew had the same purpose.
I began testing with what happened to be in front of me- a cutting mat- and was surprised by the result:
The paint marks and scruffs are not as prominent in person as in the scans. To me, the damage seemed like an overlay on top of the cutting board, which made me test more:
This is the same app, and the same cutting board, but instead, this one was slightly in shadow. And when I say slightly, I mean the only shadow in this image was my own shadow. I was intrigued by how it was able to clearly isolate the scruff marks and paint on top of the board.
I decided to pursue cutting mats as my subject of choice because they are often neglected in the creative process. There is nothing that shows your progress more than the surface you work on, and I wanted to see if I could isolate the evidence of work from the mat itself using the simple Apple Notes app document scanner feature.
The Workflow
The workflow I developed was to place the cutting mat either on the floor or table and use the auto-capture feature to select my mat and take a picture. Sometimes the auto-capture will fail to work and I would have to take a general picture and use the four circles to crop where I want the app to scan:
After taking the picture in even lighting (which I defined by fluorescent overhead lighting), I would move the cutting mat to a shadowed location and take another picture and compare.
The Results
Some Selected Scans:
Some Fun Ones:
Troubleshooting
Things I realized with this project:
The contrast between the floor and the mat matters for the auto-capture to work. The scans look best when it is shot by auto-focused capture as they are less likely to be warped and capture the details better. Example:
Manual ShotAuto-Capture
Diffused lighting is key to getting the best scans without light spots.
The color of the cutting board matters. Black cutting boards offer better contrast and separation between the surface damage and the mat. However, it is more vulnerable to light spots and the details of the cutting mat itself gets lost. This makes it difficult to get a clean image. Example:
Final Thoughts
Ultimately, I am satisfied with where my project went. It is very different from what I had been ideating for the past month, but this project sparked my curiosity more, and it was fun running around Purnell and CFA scanning people’s cutting mats and recognizing some of the projects that were worked on them.
However, I struggled to strike the exact balance I was looking for of both the cutting mat and the damage on top being equally visible due to the factors listed above. If I pursue this more in the future, I would like to standardize my lighting in a studio setting. I feel that this will give me more flexibility and control with the different cutting mats. Further, I wondered if there was a process where I could use the Notes App feature but not the native iPhone camera so that I could capture it in an even higher resolution. Finally, I am pretty sure that there are more cutting boards than the ones I’ve scanned. If I were to broaden the scope of this project, I would see if I could befriend an architecture kid and go around their studio taking scans.
The Rest of It (or at least the ones worth seeing)
