It is one of the most important features for automated vision system, since it is a stage of processing that objects are extracted from a scene for subsequent recognition and analysis.
Segmentation: Segmentation process subdivides a sensed image into constituent parts or objects. It is one of the most important elements of automated vision system, since it is at this stage of processing that objects are extracted from a scene for subsequent recognition and analysis, which is most important process to analysis the blob of an image.
Threshold current image: Uses a threshold version the source image/ROI as the mask. This is the usual mode.
Same as current image: Uses the current image/ROI as the mask. For example, in labeled identification mode, you would not want a thresholded mask.
Copy the selected image: Makes an internal copy of the currently active image/ROI as the mask. When using this option, the blob identifier mask and gray source image are completely independent of each other.
Threshold: In its simplest form, thresholding is a binary conversion technique in which each pixel is converted into a binary value, either black or white. Thresholding can be used to binarize an image. Binarizing reduces an image to two grayscale values (for example, 0 and
255, as below). Note that binary images can be used as a mask to identify blobs in a blob analysis application
Specifies the threshold values when using a Threshold current image mask, otherwise disabled. Enter the low and high threshold values for the operation. You can either type in these values or move the slider bars. The pixel intensities between the two sliders are set to black (pixel values are set to 0) and the others are set to white (pixel values are set to 255). As you change the threshold values, the blob identifier image is updated.
Auto: Sets the threshold values automatically.
Identification Mode: Specifies how the blobs are to be measured.