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Computers and automated systems have accelerated productivity and improved quality and reliability for nearly everything in our modern world, and are destined to take on increasing roles as time moves on. One major limiting factor for automated systems is their inability to categorize and recognize objects, particularly under changing lighting or other conditions. Examples of how this could be useful include automatically detecting manufacturing defects, analyzing changes between two images (e.g. medical scans), noise filtering in radio frequency communications, and extracting weak signals or images from various sources.

Current approaches to making “smart” systems generally build custom solutions for every problem with very specific outcomes. Examples include self-driving car systems and facial recognition software, which have very specific features and approaches built in that usually do not translate to other applications very well. Other examples, such as automatic defect detection, require tightly controlled lighting and often require the object being inspected to be in the same position to be able to identify problems. Generally, these automated systems can, when conditions match the programmed expectations, identify that there is a problem, but have very limited ability when measurement conditions are dynamic or the situation changes in unanticipated ways.

Researchers at INL have developed an analysis system known as MorphoHawk for automatic feature detection and classification across a host of applications in changing environmental conditions. In general, MorphoHawk can be trained to identify features of interest, and will then group features in a scene (e.g. image, signal, etc) and categorize them according to the rules it was conditioned with. After it has categorized an image or other multi-dimentional data set, it can compare the features it has identified with subsequent data sets, allowing it to detect changes (e.g. manufacturing quality control) or detect the introduction of new features (e.g. a tumor in medical scans or a person entering a scene monitored by a camera). It has shown that it can discern between an object and its shadow, meaning it can handle differences in registration and light conditions in dynamic environments.  This is possible because MorphoHawk algorithms characterize and compare morphological features, rather than conducting a binary analysis (e.g. light vs. dark).

MorphoHawk has shown utility as a signal filtering tool to differentiate between noise and meaningful data in analysis of digital images and electronic signals, resulting in sharp, cleaned images and clearly extracting the message of the signals while removing the noise. MorphoHawk can be applied to analyze images for manufacturing defects, enhancing the capability of existing inspection systems. Feature extraction is another unique capability of MorphoHawk.  For example, metal surface topology can be separated into effects of rolling and grinding, allowing discrepancies to be assigned to the appropriate process. It has even been used to identify a facture path in materials and examine structural changes in battery electrodes to predict battery lifetime.

INL Technology ID: BA-481

Applications and Industries

  • Manufacturing (e.g. automated defect detection, quality control on processes, etc)
  • Wireless communications (e.g. message extraction from noisy signal)
  • Medical (e.g. comparison of medical images to detect changes)
  • Image processing (e.g. sharpening filters)
  • Tracking (bar code reading)
  • Surveillance / security (e.g. automatically detecting what type of vehicle enters a scene)
  • Counterfeit detection
  • Object classification for autonomous vehicles and aircraft
  • Other applications where automated identification and classification is required


  • Trainable object identification and analysis
  • Ability to correctly identify and categorize objects in different environmental conditions
  • Ability to identify noise or artifacts and clean up images and signals
  • Ability to track changes over time
  • Ability to separate different types of changes to help identify root causes of problems