Posted on: December 12, 2017
SABER IS SELECTED AS COGNEX DEEP LEARNING INTEGRATOR
Saber has been trained on the new Cognex ViDi Deep Learning Software. We have a license here in-house and are able to evaluate applications immediately.
Cognex offers the first ready-to-use Deep Learning-based software dedicated to industrial image analysis. Cognex ViDi Suite is a field-tested, optimized and reliable software solution based on a state-of-the-art set of algorithms in Machine Learning. It allows tackling otherwise impossible to program inspection & classification challenges. This results in a powerful, flexible and straightforward solution for countless challenging machine vision applications.
The Suite consists of 3 different tools:
Feature localization & identification Cognex ViDi blue is used to find and localize single or multiple features within an image. Be it strongly deformed characters on very noisy backgrounds (OCR) or complex objects in bulk; the blue tool can localize and identify complex features and objects by learning from annotated images. To train the blue tool, all you need to provide are images where the targeted features are marked.
Segmentation & defect detection Cognex ViDi red is used to detect anomalies and aesthetic defects. Be it scratches on a decorated surface, incomplete or improper assemblies or even weaving problems in textiles; the red tool can identify all of these
and many more problems simply by learning the normal appearance of an object including its significant but tolerable variations. The red tool is also used to segment specific regions such as defects or other areas of interest. Be it a specific foreign material on a medical fabric or the cutting zone on lace; the red tool can identify all of these regions of interest simply by learning the varying appearance of the targeted zone.
Object & scene classification Cognex ViDi green is used to classify an object or a complete scene. Be it the identification of products based on their packaging, the classification
of welding seams or the separation of acceptable or unacceptable defects; the green tool learns to separate different classes based on a collection of labeled images. To train the green tool, all you need to provide are images assigned to and labeled in accordance with the different classes.
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