Machine vision, also called automated visual inspection (AVI), is the use of various image processing methods to interpret a series of related images, or frames, in order to produce numerical data or decisions in an effort to improve the quality of the desired output.
There are many different types of machine vision systems, and each has its own set of pros and cons. In this section, we will explore the three most common types of machine vision systems: thresholding, edge detection, and feature extraction.
Types of Machine Vision Systems
There are three main types of machine vision systems: thresholding, edge detection, and feature extraction.
Thresholding is a simple method that can be used to pick out two different types of regions in an image. Basically, a threshold is a gray level below which all pixels are set equal. All pixels with intensity value lower than the threshold will be set the same value as the threshold and all pixels above it will have a different value. Thus, thresholding creates two types of regions in an image.
Thresholding is useful for detecting objects based on the contrast between their intensity and the surrounding environment. It can be used to separate out object from background because object pixels are typically brighter than background pixels. This depends heavily though upon the lighting conditions during inspection.
Edge detection is a method used to find the boundaries of objects in an image. Pixels that are adjacent to each other and have different intensities are called edges. Edge detection algorithms look for these discontinuities in intensity and then create a boundary between them. This boundary can be used to identify the shape and location of objects in an image.
Feature extraction is a method used to identify and describe the important features of an image. Features can be anything from the size, shape, and location of objects to the texture of a surface. Feature extraction algorithms look for specific patterns in an image and then create a representation of these patterns that can be used to identify and describe them.
3. Pros and Cons of Machine Vision Systems
Each type of machine vision system has its own set of pros and cons. Let’s take a look at some of the most common ones.
- Simple to use
- Can be used to detect objects based on contrast between their intensity and the surrounding environment
- Cannot be used to detect all types of objects
- Not good for analyzing multiple objects because it doesn’t provide information about location or shape
- Can be used to find the boundaries of objects in an image. This provides information about the shape and location of objects.
- Good for analyzing multiple objects
- Can be confused by noise in the image
- May not work well in certain lighting conditions
- Can identify and describe important features of an image
- Works well in most lighting conditions
- You can be use it to analyze multiple objects at the same time
- Requires extensive analysis of the image, which can take longer than other methods
- The features that you can extract may not always correspond to physical attributes of an object in an image. This means it could potentially misidentify an object in the image.
As you can see, each type of machine vision system has its own set of pros and cons. It’s important to understand these pros and cons before selecting a machine vision system for your application. Doing so will help you ensure that the system you choose will be able to meet your needs effectively.
Thank you for reading! We hope this article has been helpful in giving you a better understanding of the different types of machine vision systems and their pros and cons. If you have any questions, please don’t hesitate to ask!