Classic Machine Vision vs Machine Vision with AI

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The origins of classic Machine Vision can be traced back to the 1960s. The first digital computers and the first digital image data were available at that time. But a lot has happened since then. Thanks to the development of many algorithms and new techniques, there are now processes such as machine learning and deep learning.

Does this mean that traditional Machine Vision has been replaced? Not quite! There are still use cases where classic Machine Vision offers a better solution.

But first things first: What are the differences between classic Machine Vision and image processing that uses AI?

Classic Machine Vision is the use of conventional techniques to enhance or manipulate digital images. This includes the application of algorithms to the pixel values of an image, e.g. filtering, edge detection and color correction. These techniques rely on mathematical operations and statistics to analyze and manipulate the image data.

Machine Vision with AI, on the other hand, uses machine learning algorithms and neural networks to process images. These algorithms are able to recognize patterns and features in images and can be trained to perform tasks such as image recognition, segmentation and enhancement. AI Machine Vision can also be used for tasks such as generating realistic images or stylized image modification.

The main difference between these two approaches is that classic Machine Vision is based on predefined algorithms, while AI Machine Vision learns from the data itself.

No two image processing projects are exactly the same. The requirements differ enormously. Therefore, when choosing between classic Machine Vision and Machine Vision with AI, it all depends on the use case.

Classic image processing techniques are generally faster and simpler and can be effective for tasks such as basic image filtering, color correction and image compression. If you have a clearly defined problem that can be solved with predefined algorithms and you have limited computing resources, classical Machine Vision may be the better choice.

However, if you have a more complex task that requires advanced image analysis, such as image recognition, object detection or segmentation, AI-based Machine Vision may be more suitable. It can learn from the data itself and adapt to new situations, making it a powerful tool for image analysis tasks that require high accuracy and flexibility. On the other hand, Machine Vision with AI can be more computationally intensive and requires large amounts of training data and machine learning expertise.

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