This feature makes scanning and counting objects in large regions fast, objective and simple. Scanning large regions is done automatically by defining opposite corners in the region. After that, image capture and recording is done with ease. The count can be done automatically after the scanning process or later as a batch count of images in a directory.

Adaptive thresholding is especially suitable for detecting and counting small particles. Unlike global thresholding, where a single threshold value is used to divide an image into two regions based on pixel intensity, adaptive thresholding uses a local threshold value that is calculated for each pixel in the image based on the characteristics of the neighboring pixels. This allows adaptive thresholding to handle images with different lighting conditions, as the local threshold is adjusted to take these variations into account.

There are several methods for adaptive thresholding, including average thresholding and Gaussian thresholding. Each method uses a different approach to calculating the local threshold value and has its own strengths and weaknesses. The choice between Gaussian and mean thresholding for adaptive thresholding depends on the characteristics of the image being processed and the specific requirements of the application. In general, Gaussian thresholding is most effective when dealing with images that have uneven lighting, as it uses a weighted average of the pixel intensities of each pixel. This means that the Gaussian threshold gives more weight to pixels closer to the center of the neighborhood and less weight to pixels further away, helping to account for any lighting variations.

After segmentation, basic morphological operations can be applied.

Finally, parameters such as object area, Feret object minimum and maximum, and M1 can be used to include or exclude counted objects.

The result is displayed as a simple count or as count/cm². The result of counting multiple images can be automatically exported to an Excel report.