Your Object Recognition software is tailored to meet the needs of your unique use-case. The reason for this is because generic off-the-shelf software is unable to accommodate the vast differences encountered from one project to the next.
The software we develop combines multiple approaches to the challenges of Object Recognition such as algorithms from image processing, pattern recognition, computer vision and machine learning.
With the current technology, we can do a lot, but not everything is feasible.
Before taking on an Object Recognition project, we normally do preliminary assessment work for which we need to have sufficient samples of images and/or videos.
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This method is appropriate when the object you want to recognize follows readily identifiable patterns, attributes and rules. These characteristics make it easier to distinguish among different types of objects. For example, such attributes can be the size, shape and pattern of the tread on a particular type of car tire.
This method is appropriate when the objects of interest share common characteristics but must be distinguished and a huge number of variations must be taken into account. To use a machine learning solution, a large number of images is required for learning to occur.
When both methods are needed, a blended approach is best.