System, method and apparatus for macroscopic inspection of reflective specimens
An inspection apparatus includes a specimen stage, one or more imaging devices and a set of lights, all controllable by a control system. By translating or rotating the one or more imaging devices or specimen stage, the inspection apparatus can capture a first image of the specimen that includes a first imaging artifact to a first side of a reference point and then capture a second image of the specimen that includes a second imaging artifact to a second side of the reference point. The first and second imaging artifacts can be cropped from the first image and the second image respectively, and the first image and the second image can be digitally stitched together to generate a composite image of the specimen that lacks the first and second imaging artifacts.
1. An inspection system, comprising:
a specimen stage configured to retain a specimen;
an imaging device positioned above the specimen stage, the imaging device configured to capture an image of the specimen from the specimen stage;
a set of lights disposed on a platform between the specimen stage and the imaging device; and
a control system in communication with at least one of the specimen stage, the imaging device, and the platform, the control system configured to perform operations comprising:
receiving, via the imaging device, the image of the specimen;
analyzing, via a machine learning model, the image to determine a classification of the specimen;
based on the classification, automatically selecting an illumination profile for the specimen; and
based on the illumination profile, activating and adjusting one or more of an intensity, color, or position of the set of lights to achieve the illumination profile for the specimen.
2. The inspection system of claim 1 , wherein the operations further comprise:
receiving image data associated with the image of the specimen, the image data comprising pixel intensity values.
3. The inspection system of claim 2 , wherein analyzing, via the machine learning model, the image to determine the classification of the specimen comprises:
determining the classification of the specimen based on the image data associated with the image of the specimen.
4. The inspection system of claim 1 , wherein analyzing, via the machine learning model, the image to determine the classification of the specimen comprises:
outputting a classification for a feature of the specimen.
5. The inspection system of claim 1 , wherein automatically selecting the illumination profile comprises:
querying an illumination profile database that comprising a plurality of illumination profiles, each illumination profile of the plurality of illumination profiles is associated with a specimen feature type.
6. The inspection system of claim 5 , wherein the illumination profile comprises:
a configuration for the set of lights; and
a range of distances between the specimen and a lens.
7. The inspection system of claim 5 , wherein automatically selecting the illumination profile for the specimen comprises:
matching the classification of the specimen to the plurality of illumination profiles to identify the illumination profile for the specimen.
8. A method comprising:
receiving, by a control system from an imaging device, image data of a specimen positioned on a specimen stage, wherein a set of lights disposed on a platform between the specimen stage and the imaging device illuminates the specimen for generating the image data;
analyzing, by the control system via a machine learning model, the image data to determine a classification of the specimen;
based on the classification, automatically selecting, by the control system, an illumination profile for the specimen; and
based on the illumination profile, activating and adjusting, by the control system, one or more of an intensity, color, or position of the set of lights to achieve the illumination profile for the specimen.
9. The method of claim 8 , wherein the image data comprises an image of the specimen and pixel intensity values of the image.
10. The method of claim 9 , wherein analyzing, by the control system via the machine learning model, the image data to determine the classification of the specimen comprises:
determining the classification of the specimen based on the image and the pixel intensity values associated with the image of the specimen.
11. The method of claim 8 , wherein analyzing, by the control system via the machine learning model, the image data to determine the classification of the specimen comprises:
outputting a classification for a feature of the specimen.
12. The method of claim 8 , wherein automatically selecting, by the control system, the illumination profile comprises:
querying an illumination profile database that comprising a plurality of illumination profiles, each illumination profile of the plurality of illumination profiles is associated with a specimen feature type.
13. The method of claim 12 , wherein the illumination profile comprises:
a configuration for the set of lights; and
a range of distances between the specimen and a lens.
14. The method of claim 12 , wherein automatically selecting, by the control system, the illumination profile for the specimen comprises:
matching the classification of the specimen to the plurality of illumination profiles to identify the illumination profile for the specimen.
15. An inspection system, comprising:
a specimen stage configured to retain a specimen;
an imaging device positioned above the specimen stage, the imaging device configured to capture an image of the specimen from the specimen stage; and
a control system in communication with at least one of the specimen stage and the imaging device, the control system configured to perform operations comprising:
receiving, via the imaging device, the image of the specimen;
analyzing, via a machine learning model, the image to determine a classification of the specimen;
based on the classification, automatically selecting an illumination profile for the specimen; and
based on the illumination profile, activating and adjusting an elevation of the specimen stage based on the illumination profile for the specimen.
16. The inspection system of claim 15 , wherein the operations further comprise:
receiving image data associated with the image of the specimen, the image data comprising pixel intensity values.
17. The inspection system of claim 16 , wherein analyzing, via the machine learning model, the image to determine the classification of the specimen comprises:
determining the classification of the specimen based on the image data associated with the image of the specimen.
18. The inspection system of claim 15 , wherein analyzing, via the machine learning model, the image to determine the classification of the specimen comprises:
outputting a classification for a feature of the specimen.
19. The inspection system of claim 15 , wherein automatically selecting the illumination profile comprises:
querying an illumination profile database that comprising a plurality of illumination profiles, each illumination profile of the plurality of illumination profiles is associated with a specimen feature type.
20. The inspection system of claim 19 , wherein automatically selecting the illumination profile for the specimen comprises:
matching the classification of the specimen to the plurality of illumination profiles to identify the illumination profile for the specimen.