Locality-based detection of tray slot types and tube types in a vision system

A method for offline image patch classifier training, comprising steps of:

• receiving a series of images of a tray from a plurality of cameras, the tray having a plurality of tube slots;
• extracting a plurality of image patches from each image, wherein each of the plurality of image patches are substantially centered on one of a tube slot and a tube top;
• providing, using a processor, each image patch of the plurality of images to a classifier;
• collecting, using the processor, image patch data for each image patch provided to the classifier, the data indicating one of:
• whether each tube slot in the tray contains a tube;
• whether each of the tubes contained in the one or more tube slots has a cap;
• and whether each tube contained in the one or more tube slots has a tube-top sample cup or is a plain tube;
• and
• determining, using the processor, image patch classifiers corresponding to each image patch based on the image patch data.

A vision system for use in an in vitro diagnostics environments comprising:

• a tray comprising a plurality of slots arranged in a matrix of rows and columns, each tube slot configured to receive a sample tube;
• a surface configured to receive the tray;
• an image capture system having a first camera configured to capture a series of images of the tray;
• and
• a processor configured to:
• receive the series of images of the tray captured by the first camera;
• extract a plurality of image patches from each image of the series of images, wherein each of the plurality of image patches are substantially centered on one of the plurality of tube slots or a tube top;
• assign, to each image patch, a first location group that defines whether the image patch is from one of:
• the center of the image, a corner of the image, and a middle edge of the image;
• select, for each image patch, based on the first location group, a trained classifier to use in processing the image patch;
• and
• automatically determine, from the plurality of image patches, whether each tube slot in the tray contains a corresponding sample tube using the trained classifier for each image patch.