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Automatic Measurement Module

Automatically inspect and measure the dimensions of parts or products and detect defects or anomalies with the Automatic Measurement module.

Measurement processes are streamlined to deliver automated pass/fail results, reducing the need for human inspection, and improving the overall efficiency of the inspection process.

Feature detection and segmentation algorithms combined with easy-to-use measurement features offer an advanced toolkit for analysis of complex features lacking geometric structure or uniformity.



  • Streamline the measurement process
    With instant image alignment and unique measurement and algorithm toolkit to deliver automated pass/fail judgments.
  • Combines AI inspection with measurement processes
    Reduces the need for separate procedures, process lines, resourcing, and tools.
  • Automated decision making / assistance
    Enables operational continuity when staff shortages or lack of trained inspectors occur.
  • Reduces downtime on inspection lines
    The ability to integrate cobot/robot automation to operate outside of working hours.
  • Reduces inspection time and improves productivity
    Fast processing, automatic decision making or assistance.
  • Reduces the demand for skilled human inspectors doing routine tasks
    Enables deployment to higher value adding tasks.
  • Keeps records of decisions
    Automatically saves numerical data with pass/fail judgements and captured images.

How it works

  • Open a measurement project.
  • Optimise camera system settings and save to the project.
  • Create alignment features and align the part, if not using a jig.
  • Create features on parts and then measure using measurement tools, edge detection, feature detection and segmentation.
  • Add measurement tolerances.
  • Generate pass/fail judgements and save all dimensional measurements.
Automatic measurement being carried out on a medical device part by an automated visual inspection system
Automatic Measurement Test Results on the Acumen AI



This is an important factor that contributes to image quality. Optimal illumination should reduce shadow, noise, and reflection and increase image contrast. The Acumen system can be easily configured to achieve optimal illumination which is saved to the AMM project for measurement accuracy and consistency.

A Wide FOV (Field of View) Range

This can be achieved with the Acumen system dynamic zoom capability to accommodate a range of part sizes.


without a jig can be easily achieved with image alignment. The AMM algorithm detects the positional relationship of the test object relative to the alignment reference features in the project file and corrects the positioning of the test object. Onscreen graticules can also be used to aid object placement


These are parts or patterns of an object in an image that is to be used for measurement. Features include properties like corners, edges, regions of interest, points etc. Feature types are defined using edge detection, feature detection or segmentation.

Easy to Apply Region of Interest (ROI)

around the object feature defines the image pixels where image processing is to be performed.

Edge Detection

Locates the sharp changes in pixel intensities in the image ROI to find the edge boundaries between the object feature and background. The direction of the feature edge can then be selected in the toolbar. A strong edge is important for repeatable and accurate robust measurements – thresholding and blur can be applied to improve the edge pixel boundary. Selecting the output requirement for each feature edge makes it available for measurement of distances, angles, areas, and offsets in the measurement mode.

Feature Detection

This is used to detect an object feature that matches the template image in the project.  When a specific feature on an object may not have a significant edge, feature detection may be useful in detecting that feature for measurement. The feature must be unique within the specified ROI. Several images can be used to create a template feature image, where the necessary features have been extracted, grouped together to obtain a normalized feature image. The template matching then uses matching criterion to determine the similarity between the template image and test image.


This can be used to distinguish and detect features by colour. A clustering algorithm divides the image ROI into colour clusters. The number of clusters must be selected for the algorithm to cluster the image data accordingly. The colour selector allows the user to select which colour cluster is to be detected., creating a mask where that that cluster is located on the image.   Edges of this mask can then be used as measurement features.

Virtual features

These can be created, enabling measurements from lines, circles, or points.


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