To be honest, not much covered here that wasn’t covered extensively in Computer Vision.
- 8.1 explains that images are simply 2d signals
- 8.2 explains that CNNs generally do a better job than hand-constructed techniques
- 8.3 gives some examples of basic image processing techniques
- 8.4 explains stereo image processing
- 8.5 ML + CV = Best friends
Take home lessons:
- Unlike the sensors from Chapter 7, our brains can directly process the 2D information that is captured by a vision sensor. It is difficult to unthink the amount of processing that we perform automatically, augmenting the signal with knowledge and other information that the computer does not necessarily have.
- Algorithms described in this chapter aim at reducing information to a lower-dimensional space by removing noise and other spurious information, making the related challenge of understanding the data more tractable.
- There is a trade-off between making the data stream more tractable and preserving actual information. As computers and algorithms, in particular machine learning, become more powerful, modern vision systems often blend pre-processing and actual image understanding into a single pipeline.