This term I am taking Computer Photography course which gives us better insights on the computations involved in image processing.

Below is the learn log for the first week of the course.

Introduction

There are various modules provided in the course. Below are the list of modules and topics covered under them

• Introduction
• More about Filters
• Deep learning about cameras
• Image blending/munging
• Computational Photography
• Videos Computations
• Computational Cameras
• Advanced Topics - Special Cases

The main expectations of the class is to learn about the tech related to photography.

“Love to learn computing with photographs”

What is Computational Photography?

To understand computational photography we should understand what is Photography? Photography - is way of recording light either electronically or chemically to generate artifacts

Computational Photography is the complete workflow of how light is captured and image is produced

Dual Photography

One of the example of CP. A research paper by Stanford.Main context is if we swap light source and camera rays of light get converted to novel form of image.

Novel Illumination

Also using the reflections we can get different images

Panaromas

Wide angled image produced by combining various images in sequences taken together.

Steps Involved in creating Panaromas

• Take Pictures
• Detecting and Matching
• Warping
• Fade, Blend/Cut
• Cropping(Optional)

Types of Panaromas

• Planar/Rectilinear
• Vertical
• Cylindrical/Rotational
• Path/Route

Why Computational Photography?

In this chapter we learnt about the pevasiveness of photography

• Why should we study camera?
• Cameras and Everywhere- Detailed analysis of camera sales over various periods
• About various images
  • Participatory Images
  • Institutional Images
  • Incidental Images

Main objective of learning computational photography is to understand how cameras can be evolved in next 10 + years. Also the social culture change due to it. Also improving image search methods.