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FSI Machine Vision
Solutions and Systems


• Welding seam inspection

• Machined part inspection

• Appearance-based product identification

• Solar Panel Inspection

• Aesthetic textile inspection

• Pad printing aesthetic inspection

• Watch Part Inspection

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ViDi Suite

Deep Learning-based industrial image analysis

ViDi offers the first ready-to-use Deep Learning-based software dedicated to industrial image analysis. 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 (ViDi blue, red, green).

ViDi blue
Feature localisation

ViDi blue products
ViDi blue is used to find and localize single or multiple features within an image. Be it strongly deformed characters on very noisy backgrounds or complex objects in bulk; the blue tool can localise 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.

ViDi Red
Anomaly detection

ViDi red products
ViDi red is used to detect qualitative defects of any type. 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.

To train the red tool, all you need to provide are images of good objects.

Anomaly detection ViDi green
Object & scene classification

ViDi green products
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 inacceptable defects; the green tool learns to separate different classes based on a collection of labelled images.

To train the green tool, all you need to provide are images assigned to and labelled in accordance with the different classes.

Anomaly detection