computaional photography portfolio
TRANSCRIPT
![Page 2: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/2.jpg)
PHOTO-MOSAICnot ‘Pixel Perfect’, but ‘Picture Perfect’
Final Project
![Page 3: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/3.jpg)
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-MOSAICnot ‘Pixel Perfect’, but ‘Picture Perfect’
![Page 4: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/4.jpg)
![Page 5: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/5.jpg)
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![Page 7: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/7.jpg)
Average Brightness
Median Brightness
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Assignment #1A Photograph is a Photograph
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Camera: NIKON COOLPIX S3100F-Stop: f/5.5
Exposure time: 1/1000 secISO speed: ISO-80
Focal length: 18mmExposure bias: 0 step
Max aperture: 3.4Metering Mode: Pattern
Flash Mode: No FlashDimension: 3240 X 4320
The Capitol
![Page 11: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/11.jpg)
Assignment #2
![Page 12: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/12.jpg)
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.
![Page 13: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/13.jpg)
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.
![Page 14: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/14.jpg)
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.
![Page 15: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/15.jpg)
Assignment #3Epsilon Photography
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Epsilon Photography
![Page 17: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/17.jpg)
Ghosts on Road
![Page 18: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/18.jpg)
Assignment #4Camera Obscura
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The Setup
Pin Hole was created by covering the window.Pinhole
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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.
![Page 21: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/21.jpg)
The Image
![Page 22: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/22.jpg)
Assignment #5Gradients and Edges
![Page 23: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/23.jpg)
X Gradient
For the X gradient, I subtracted the ith pixel from i+1th pixel looping through the columns.
![Page 24: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/24.jpg)
For the Y gradient, I subtracted the ith pixel from i+1th pixel looping through the rows.
Y Gradient
![Page 25: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/25.jpg)
Original image
Kernel image
Black and white imageThreshold - 100
Original image
Kernel image
Black and white imageThreshold - 150
Edge Detection
![Page 26: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/26.jpg)
Assignment #6Blending
![Page 27: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/27.jpg)
Output
Black
White
Mask
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BlackWhite
Mask
Output Output
When converting the output to grayscale, it looks as It there is shadow of tree on the surface .
![Page 29: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/29.jpg)
To create this mask, I took the Black image and using the threshold of 128 and changed the pixel value to 0 or 255
![Page 30: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/30.jpg)
Black White Mask Output
Similarly I created masks for black image and tried to blend it with the white image.
![Page 31: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/31.jpg)
I tried these with various threshold values to create masks and check the resultant blending.
Mask OutputMask Output
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Assignment #7Feature Detection and Matching
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Score: 3/10
Lighting
![Page 34: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/34.jpg)
Score: 2/10
Rotation
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Score: 4/10
Sample
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Score: 10/10
Scale
![Page 37: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/37.jpg)
Assignment #8Panorama
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Sample Panorama created using inbuilt Phone feature
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Assignment #9Photos of Space
![Page 40: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/40.jpg)
PhotosynthFord Museum + Campionile
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Site 2 (Campionile)
Link to Photosynth
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PanoramaCampionile
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PhotosphereCampionile + Labspace
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Photosphere of Campionile
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Photosphere of Labspace
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Assignment #10HDR
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Output with the given set of images
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Output with the given set of images on the right.
![Page 51: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/51.jpg)
Output with the given set of images on the right.
![Page 52: Computaional Photography portfolio](https://reader031.vdocument.in/reader031/viewer/2022030123/58a48ce31a28ab58738b6cad/html5/thumbnails/52.jpg)
Assignment #11Video Textures
Output Link:https://drive.google.com/file/d/0B5Ncl02d4dOeS2JxeVIycEVMdmc/view?usp=sharing
https://drive.google.com/file/d/0B5Ncl02d4dOeNjVXS3BEa0pLeFU/view?usp=sharing