![cognex vidi cognex vidi](https://www.cognex.com/library/media/spotlights/vision-spotlights/deep-learning-electronics-applications-spotlight.png)
Cognex ViDi takes the same approach – users label input images and configure training workflows with a graphical tool rather than writing code, as is required with open source Deep Learning tools such as TensorFlow. The success of Cognex’s VisionPro tool, their flagship rules-based image processing library, is down to its ease of use – application developers can build and configure defect detection workflows without being an expert in computer vision or programming. Cognex, recognizing the potential of Deep Learning in industrial applications, made a decisive move to integrate ViDi into their offering. ViDi Systems SA was launched in Switzerland in 2012, originally with the objective of applying Deep Learning concepts to find defects in textiles. The advances in GPUs (Graphics Processing Units) and the parallelization of neural networks made in 2012-2014 gave rise to Deep Learning, a more powerful version of Machine Learning suited to processing images, video and speech.
![cognex vidi cognex vidi](https://i.ytimg.com/vi/1MJgb0dus1w/maxresdefault.jpg)
As described in a previous Agmanic Vision blog article on Machine Learning, rules-based techniques struggle when faced with tasks requiring human-like interpretation and judgement. BackgroundĬognex’s image processing tools have traditionally been ‘rules-based’, using classic computer vision techniques such as pattern matching and keypoint detection. This article reviews the ViDi Deep Learning tool and its potential impact on industrial computer vision.
![cognex vidi cognex vidi](https://www.cognex.com/library/media/thumbnails/vidi-green-classify-thumbnail.png)
In April 2017 Cognex made a significant move with their acquisition of ViDi Systems SA, which brings Deep Learning capability into Cognex’s offering.