6 January 2020
Computer Vision(1)
by Jerry Zhang
real-world applications
- Optical character recognition (OCR)
- Machine inspection
- Retail
- 3D model building (photogrammetry)
- Medical imaging
- Automotive safety
- Match move
- Motion capture (mocap)
- Surveillance
- Fingerprint recognition and biometrics
Applications in this book:
- Stitching: turning overlapping photos into a single seamlessly stitched panorama
- Exposure bracketing: merging multiple exposures taken under challenging lighting conditions (strong sunlight and shadows) into a single perfectly exposed image
- Morphing: turning a picture of one of your friends into another, using a seamless morph transition
- 3D modeling: converting one or more snapshots into a 3D model of the object or person you are photographing
- Video match move and stabilization: inserting 2D pictures or 3D models into your videos by automatically tracking nearby reference points or using motion estimates to remove shake from your videos
- Photo-based walkthroughs: navigating a large collection of photographs, such as the interior of your house, by flying between different photos in 3D
- Face detection: for improved camera focusing as well as more relevant image searching
- Visual authentication: automatically logging family members onto your home computer as they sit down in front of the webcam
three levels of description of a (visual) information processing system
- Computational theory: What is the goal of the computation (task) and what are the constraints that are known or can be brought to bear on the problem?
- Reprsentations and algorithms: How are the input, output, and intermediate information represented and which algorithms are used to calculate the desired result?
- Hardware implementation: How are the representations and algorithms mapped onto actual hardware, e.g., a biological vision system or a specialized piece of silicon? Conversely, how can hardware constraints be used to guide the choice of representation and algorithm? With the increasing use of graphics chips (GPUs) and many-core architectures for computer vision, this question is again becoming quite relevant.