The present application relates to 3D object imaging, and more specifically, to a referencing system for general imaging including hyperspectral imaging, RGB imaging, greyscale imaging etc.
Image calibration with white referencing is one important step of scientific imaging. The goal of the calibration is to eliminate the impact from uneven lighting conditions. Typically, shortly before or after the raw image of the target object is taken, a white tile is placed at the same position as the object and is imaged too. For each image pixel, the pixel value in the raw image is divided by the corresponding pixel value of the white reference image:
Corrected Image from the camera=Image of sample/Image of white tile
Sometimes the dark reading image is also taken by closing the aperture or simply putting on the lens cover, in which case:
Corrected Image from the camera=(Image of sample−Dark reading Image)/(Image of white tile−Dark reading Image)
The above method works well for flat objects, but when the object has complicated 3D shapes this method has serious problems because 1) the object surface may be at different depth distances from the camera, where the lighting intensity can differ a lot from where the flat white tile is located, and 2) the object surface may be at many different tilted angles, which will severely change the reflectance not only in intensity, but also in color. Take plant leaf reflectance for example, where the PROSAIL model (http://teledetection.ipgp.jussieu.fr/prosail/) shows the different leaf angles completely change the reflectance spectra, which may cause 300% change in color index calculation such as NDVI, as shown in
The problem may be solved by replacing the flat white tile with a 3D white referencing. The 3D white reference should have exactly the same size and 3D shape as the target object. Since each target object is different, one solution can be achieved by placing a 3D scanner and a 3D printer on the spot. Every time a new object arrives, it is scanned by the 3D scanner. The scanning result is then sent to the 3D printer immediately to print out the 3D white reference. This 3D white reference is then scanned for the white reference image, which is used to calibrate the raw image of the object. Preliminary data from experiments confirm the improved calibration quality with 3D reference compared with 2D flat reference.
However, producing the 3D white reference for each object is an expensive and impractical approach, incurring increased processing time and resources. Therefore, improvements are needed in the field.
One or more embodiments are illustrated by way of example, and not by limitation, in the figures of the accompanying drawings, wherein elements having the same reference numeral designations represent like elements throughout. It is emphasized that, in accordance with standard practice in the industry, various features may not be drawn to scale and are used for illustration purposes only. In fact, the dimensions of the various features in the drawings may be arbitrarily increased or reduced for clarity of discussion.
In the following description, some aspects will be described in terms that would ordinarily be implemented as software programs. Those skilled in the art will readily recognize that the equivalent of such software can also be constructed in hardware, firmware, or micro-code. Because data-manipulation algorithms and systems are well known, the present description will be directed in particular to algorithms and systems forming part of, or cooperating more directly with, systems and methods described herein. Other aspects of such algorithms and systems, and hardware or software for producing and otherwise processing the signals involved therewith, not specifically shown or described herein, are selected from such systems, algorithms, components, and elements known in the art. Given the systems and methods as described herein, software not specifically shown, suggested, or described herein that is useful for implementation of any aspect is conventional and within the ordinary skill in such arts.
The present disclosure provides a referencing system which calibrates images. The referencing system includes a multi-axis moving apparatus (e.g.
In operation, step 1 begins with the referencing system collecting and storing a library of images of the reference piece at a multitude of positions and slope angles using the camera and the 3D scanner. In at least one embodiment, step 1 includes collecting a library of images of all possible locations, orientations, and slopes of the reference piece. The library is stored in a processing unit such as a computer. In step 2, a target object is scanned by the camera and the 3D scanner, thereby producing a camera image and a 3D scanner image, respectively. The target object can include a plant, a piece of meat, sculpture, or any 3D object. In step 3, each pixel from the camera image is matched with a corresponding pixel in the 3D scanner image so as to determine a 3D location, orientation, and slope of this pixel in the camera image. In one or more embodiments, step 3 is performed by the processing unit such as a computer. This step results in determination of location, orientation, and slope of all pixels in camera image, according to at least one embodiment.
In step 4, the determined 3D locations, orientations, and slopes of various pixels in the camera image are used to identify relevant corresponding images within the library. The relevant corresponding images within the library have locations, orientations and slopes that are commensurate with the determined 3D locations, orientations and slopes of various pixels of the camera image. In one or more embodiments, step 4 is performed by the processing unit such as a computer. In step 5, the relevant corresponding images are then used to virtually reconstruct a 3D reference image of the target object. In one or more embodiments, step 5 is performed by the processing unit such as a computer. In step 6, the camera image taken from the camera is calibrated based on the reconstructed 3D reference image. In one or more embodiments, the calibration includes using the 3D reference image to reference the camera image. In one or more embodiments, step 6 is performed by the processing unit such as a computer.
The reference platform automatically generates the reference image library.
In one or more embodiments, the system of
Once drawback of the embodiment shown in
The spatial resolution of the embodiment of
The invention is inclusive of combinations of the aspects described herein. References to “a particular aspect” and the like refer to features that are present in at least one aspect of the invention. Separate references to “an aspect” (or “embodiment”) or “particular aspects” or the like do not necessarily refer to the same aspect or aspects; however, such aspects are not mutually exclusive, unless so indicated or as are readily apparent to one of skill in the art. The use of singular or plural in referring to “method” or “methods” and the like is not limiting. The word “or” is used in this disclosure in a non-exclusive sense, unless otherwise explicitly noted.
The invention has been described in detail with particular reference to certain preferred aspects thereof, but it will be understood that variations, combinations, and modifications can be effected by a person of ordinary skill in the art within the spirit and scope of the invention.
The present U.S. patent application is a divisional of U.S. patent application Ser. No. 17/239,686 filed Apr. 26, 2021, which is a continuation of U.S. Pat. No. 11,017,563 issued May 25, 2021, prior application Ser. No. 16/705,649 filed Dec. 6, 2019, which is a divisional of U.S. Pat. No. 10,515,461 issued Dec. 24, 2019, prior application Ser. No. 16/038,161 filed Jul. 17, 2018, which claims the priority benefit of U.S. Provisional Patent Application Ser. No. 62/533,587 filed Jul. 17, 2017. The contents of these prior applications are hereby incorporated by reference in their entirety into the present disclosure.
Number | Date | Country | |
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62533587 | Jul 2017 | US |
Number | Date | Country | |
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Parent | 17239686 | Apr 2021 | US |
Child | 18077290 | US | |
Parent | 16038161 | Jul 2018 | US |
Child | 16705649 | US |
Number | Date | Country | |
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Parent | 16705649 | Dec 2019 | US |
Child | 17239686 | US |