When selecting features for a machine vision application, it is important to identify the desired outcomes and to consider the environment in which the machine vision application will be used. The features you select should help you meet those desired outcomes and should be optimized for your specific application.
The first step is to understand the context in which the application will be used. Consider what kind of images will be used, and be aware of any objects that might need to be detected. Knowing the environment also helps identify optimal lighting conditions, as well as what types of features will be visible or may not be seen at certain angles or distances.
Next, think about what types of features you need in order to detect the objects in the image correctly. For example, color might be helpful, edges or contours might be easier to detect, and textures may provide interesting detail. Location, size, and shape might also be important depending on the task at hand.
Finally, you should consider the performance requirements and computational resources available. This includes the size of the images and objects, the speed and accuracy required, and any hardware constraints. With these limitations in mind, it may be necessary to adjust the feature selection process to match the available resources.
By considering all these factors, you can make an informed decision about what features are necessary for your machine vision application.
