Computer vision is an area of great opportunity in the fields of Artificial Intelligence and Machine Learning. In this video, find out how to optimize a computer vision system for varied applications.
There are numerous challenges to solve when developing a computer vision system, which can involve:
- • Complexity with scaling
- • KPI definition for tuning
- • Model training and dataset
- • Performance
- • Accuracy
- • Privacy & ethics
Luckily, there are companies like eInfochips and Arrow that work constantly to develop solutions for the above problems. These can include:
- • Performance optimization with hardware and software accelerators
- • Definition of key quality parameters
- • Definition of AI & ML end goals
- • Extensive test & tuning datasets
- • Market conditions insights
In the design cycle of a computer vision system, there is a fairly clearly defined development flow:
- 1. Selection of optics, including glass quality and focal parameters
- 2. Optics sensor selection
- 3. Data Bus selection and support
- 4. ISP/SoC platform selection, which varies based on the specific demands of the system
- 5. Software-based image tuning
In each of these steps, there are quite a few selections to make, with regards to hardware and software. How is it possible to make sense of it all, though? Click through to the video for more in-depth information.
