This video shows first steps in a project defining a pixel classifier. Our example is from a food product sorting using NIR spectral camera. We separate product (two types of nuts) from known foreign objects also represented by several types of materials such as nut shells, stoned or plant leaves.
In this video, we show how to segment objects out from the results of a pixel classifier. We focus on the use-case where each object is composed of a single material type (class). This leads to object detection solution. Removing small objects is demonstrated.
In this video, we demonstrate classification of complex objects based on their content. We show how to grade objects using majority voting or custom rules. The example shows sorting of "French fries" removing pieces with rot or greening defects.
In this tutorial, we demonstrate how to define common types of spectral indices in interactive manner. We visualize an index related to chlorophyll content in potato plants affected by a virus infection. We limit the index visualization to plant leaves and stem and show how to obtain comparable results on multiple spectral cubes.
In this tutorial, we show how to estimate mixing proportion of powders using regression modeling. We show the full process from object definition, object annotation and regression modeling. The resulting solution can be applied to new scans both at object level and pixel level (distribution of regression output).
In this tutorial, we show how to apply the model to a live data stream from a Specim hyperspectral sensor. We show how the processing speed can be measured and how the classifier can be sped up by band reduction. In addition to classification, object sorting on the live stream is illustrated providing identical outputs to perClass Mira Runtime embedded in a custom application.