Artifacts generated by computationally processing photographs.

1. Georgia Tech's
Computational Photography
Portfolio
Apurva Gupta
apurva.gupta@gatech.edu

2. PHOTO-MOSAIC
not ‘Pixel Perfect’, but ‘Picture Perfect’
Final Project

3. Slice Source
image into
tiles
Resize images
from the image
corpus keeping
their aspect ratio
Find average
brightness of each
color channel of the
corpus images
Compare the
distance of the
average brightness
of each image with
the average
brightness of source
image.
Replace each pixel
in the source image
with the image
with the least
distance
PHOTO-MOSAIC
not ‘Pixel Perfect’, but ‘Picture Perfect’

7. Average Brightness
Median Brightness

9. Assignment #1
A Photograph is a Photograph

10. Camera: NIKON COOLPIX S3100
F-Stop: f/5.5
Exposure time: 1/1000 sec
ISO speed: ISO-80
Focal length: 18mm
Exposure bias: 0 step
Max aperture: 3.4
Metering Mode: Pattern
Flash Mode: No Flash
Dimension: 3240 X 4320
The Capitol

11. Assignment #2

12. Black and White
Used nested loop to traverse each pixel and checked the threshold value of 128. Value greater that 128 corresponded to 255
while the smaller one was changed to 0.

13. Horizontal Flip
Used nested loop to traverse each column in the image matrix. The (x,y) pixel values in each column was replaced with the
value of (x, height-y) pixel value and stored in another 2D array.

14. Average of two images
Used nested loop to traverse the images and added the (x,y)th pixels of both the images and divided the
values by 2. This was stored in a 2D array.

15. Assignment #3
Epsilon Photography

16. Epsilon Photography

17. Ghosts on Road

18. Assignment #4
Camera Obscura

19. The Setup
Pin Hole was created by covering the window.
Pinhole

20. The Image
This wasn’t
visible through
eye but came
up well on
camera. The
edges are not
sharp since it
was a big pin
hole. The
building
specifics were
not visible at
all but the sky
came out really
good.

21. The Image

22. Assignment #5
Gradients and Edges

23. X Gradient
For the X gradient, I
subtracted the ith pixel from
i+1th pixel looping through the
columns.

24. For the Y gradient, I subtracted
the ith pixel from i+1th pixel
looping through the rows.
Y Gradient

25. Original image
Kernel image
Black and white image
Threshold - 100
Original image
Kernel image
Black and white image
Threshold - 150
Edge Detection

26. Assignment #6
Blending

27. Output
Black
White
Mask

28. BlackWhite
Mask
Output Output
When converting the output to grayscale, it looks as It there
is shadow of tree on the surface .

29. To create this mask, I took the Black image and using the
threshold of 128 and changed the pixel value to 0 or 255

30. Black White Mask Output
Similarly I created masks for black image and tried to blend it with the white image.

31. I tried these with various threshold values to create masks and check the resultant
blending.
Mask OutputMask Output

32. Assignment #7
Feature Detection and Matching

33. Score: 3/10
Lighting

34. Score: 2/10
Rotation

35. Score: 4/10
Sample

36. Score: 10/10
Scale

37. Assignment #8
Panorama

38. Sample Panorama created using inbuilt Phone feature

39. Assignment #9
Photos of Space

40. Photosynth
Ford Museum + Campionile

41. Link to Photosynth

42. Site 2 (Campionile)
Link to Photosynth

43. Panorama
Campionile

44. Link for Panorama

45. Photosphere
Campionile + Labspace

46. Photosphere of Campionile

47. Photosphere of Labspace

48. Assignment #10
HDR

49. Output with the given set of
images

50. Output with the given set of images on the right.

51. Output with the given set of images on the right.

52. Assignment #11
Video Textures
Output Link:
https://drive.google.com/file/d/0B5Ncl02d4dOeS2JxeVIycEVMdmc/view?usp=sharing
https://drive.google.com/file/d/0B5Ncl02d4dOeNjVXS3BEa0pLeFU/view?usp=sharing