3D object shape acquisition, characterization, and classification

Abstract

An apparatus is disclosed for capturing images of one or more objects (e.g., particles) to enable reconstruction and classification of the 3D shape of the one or more objects, the apparatus comprising an imaging array for capturing images of the one or more objects and an object placement unit for containing or holding the one or more objects during image capture, the imaging array comprising a plurality of imaging devices (e.g., cameras) configured to simultaneously capture images of the one or more objects from a plurality of different angles. The two or more imaging devices may be configured in a polar arrangement, and at least one imaging device may be configured orthogonal to the imaging device configured in the polar arrangement. The object placement unit may comprise an acquisition chamber or tube at least a portion of which is located within the field of view of the imaging device, the one or more objects may be fed into the acquisition chamber or tube and fall under the influence of gravity through the field of view of the imaging device, and a drag field may be applied to the one or more objects to counteract the gravitational force on the one or more objects. The apparatus may further comprise one or more light sources (e.g., LEDs) for illuminating the one or more objects or creating a bright field, and one or more light diffusers for diffusing the light emitted by the one or more light sources. The one or more objects may include one or more objects having at least one of the following scales: centimeter scale, millimeter scale, micron scale, sub-micron scale.

Description

[Technical field]

[0001] Certain examples of the present disclosure provide techniques for obtaining the three-dimensional (3D) shape of one or more objects, such as centimeter, millimeter, micron, or submicron scale particles. Certain examples of the present disclosure provide techniques for characterizing the 3D shape of one or more objects. Certain examples of the present disclosure provide techniques for classifying the 3D shape of one or more objects. [Background technology]

[0002] The 3D shape acquisition, characterization, and classification of particles has numerous applications in various industries. For example, applications involving the analysis of dry particles include mining, minerals, and cement, food, fertilizers, glass beads, battery packaging materials, pharmaceuticals, additive manufacturing, civil engineering and construction materials, and abrasives. Other applications include chemicals, petroleum, beverages, prosthetics, biopharmaceuticals, and drug discovery.

[0003] Typically, one or more images of a particle are captured. The shape of the particle is then constructed based on the captured images. The particle is then characterized and classified based on the reconstructed shape.

[0004] One problem with conventional systems is that it is difficult to obtain images that allow for a complete reconstruction of the 3D shape in a relatively simple manner. For example, some conventional techniques capture images only in one plane / view. However, this means that shape characteristics in other planes / views cannot be reconstructed. Other conventional techniques rely on the rotation of the imaged particles to capture shape characteristics in different planes. For example, some systems image particles that rotate as they fall through a medium such as air, while other systems provide mechanical means such as a turntable to physically rotate the particles during image capture. However, it is difficult to control the rotation of falling particles, and the particles may rotate at different speeds, rotate too quickly, or not rotate at all. Providing mechanical means to physically rotate the particles can be costly and may not be suitable for imaging a relatively large number of particles simultaneously. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] Nadimi, S. and Fonseca, J., 2017. Single-grain virtualization for contact behavior analysis on sand. Journal of Geotechnical and Geoenvironmental Engineering, 143(9), p.06017010 [Non-Patent Document 2] Angelidakis, V., Nadimi, S., Utili, S. 2021. SHape Analyser for Particle Engineering (SHAPE): Seamless characterization and simplification of particle morphology from imaging data. Computer Physics Communications 265, 107983 Summary of the Invention [Means for solving the problem]

[0006] What is desired, therefore, are techniques for obtaining the 3D shape of one or more objects, e.g., particles, to enable characterization and / or classification of the objects.

[0007] The above information is presented solely as background information to aid in the understanding of the present disclosure, and no determination has been made, or any assertion made, as to whether any of the above may be applicable as prior art with respect to the present disclosure.

[0008] It is an object of certain examples of the present disclosure to at least partially address, resolve, mitigate, or avoid at least one of the problems and / or shortcomings associated with the related art, such as at least one of the problems and / or shortcomings noted herein. Certain examples of the present disclosure are intended to provide at least one advantage over the related art, such as at least one of the advantages noted herein.

[0009] The invention is defined in the independent claims. Advantageous features are defined in the dependent claims.

[0010] Any embodiments or examples disclosed in the specification and / or figures outside the scope of the claims are to be understood as examples useful for understanding the present invention.

[0011] Other aspects, advantages and salient features of the present disclosure will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses examples of the present disclosure. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 illustrates a schematic diagram of a system according to an example of the present disclosure. [Figure 2a]FIG. 2 is a more detailed schematic diagram of an example of the imaging module 101 of FIG. [Figure 2b] FIG. 2 is a more detailed schematic diagram of an example of the imaging module 101 of FIG. [Diagram 3] FIG. 1 illustrates an exemplary method according to the present disclosure. [Figure 4a] FIG. 1 illustrates an example of acquisition, classification, characterization, simplification, and characterization of multi-sphere particles. [Figure 4b] FIG. 1 illustrates an example of acquisition, classification, characterization, simplification, and characterization of multi-sphere particles. [Figure 4c] FIG. 1 illustrates an example of acquisition, classification, characterization, simplification, and characterization of multi-sphere particles. [Figure 4d] FIG. 1 illustrates an example of acquisition, classification, characterization, simplification, and characterization of multi-sphere particles. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The following description of examples of the present disclosure is provided with reference to the accompanying drawings to aid in a comprehensive understanding of the present invention as defined by the claims. The description includes various specific details to aid in the understanding, but these are to be considered as merely examples. Thus, those skilled in the art will recognize that various changes and modifications of the examples described herein are possible.

[0014] The terms and words used herein are not limited to their literary meaning, but are merely used to enable a clear and consistent understanding of the present disclosure.

[0015] The same or similar components may be labeled with the same or similar reference numbers, but may be shown in different drawings.

[0016] Detailed descriptions of elements, features, components, structures, constructions, functions, operations, processes, properties, attributes, integers, and steps that are known in the art may be omitted for clarity and brevity, and to avoid obscuring the subject matter of the present disclosure.

[0017] Throughout this specification, the words "comprises," "includes," "contains," "has," and variations of these words, such as "comprises" and "comprising," mean "including, but not limited to," and are not intended to (and do not) exclude other elements, features, components, structures, constructions, functions, operations, processes, properties, attributes, integers, steps, and / or groups thereof.

[0018] Throughout this specification, the singular forms "a," "an," and "the" include plural referents unless the context dictates otherwise. For example, a reference to an "object" includes a reference to one or more of such objects.

[0019] The term "substantially" means that the stated characteristic, parameter, or value need not be achieved exactly, but rather that deviations or variations, including, for example, tolerances, measurement errors, limits of measurement accuracy, and other factors known to those of ordinary skill in the art, may occur to an extent that does not interfere with the effect that the characteristic, parameter, or value is intended to provide.

[0020] Throughout this specification, language of the general form "X for Y" (where Y is some action, process, function, activity, operation, or step and X is some means for performing that action, process, function, activity, operation, or step) encompasses, but is not limited to, means X that is specifically adapted, configured, or arranged to perform Y.

[0021] It is to be understood that elements, features, components, structures, constructions, functions, operations, processes, properties, attributes, integers, steps, and / or groups thereof described herein in connection with a particular aspect, embodiment, example, or claim, are also applicable to any other aspect, embodiment, example, or claim disclosed herein, unless inconsistent therewith.

[0022] It will be appreciated that examples of the present disclosure may be realized in the form of hardware, software, or a combination of hardware and software. Such software may be stored in any suitable form of volatile or non-volatile storage device or medium, such as, for example, a ROM, a RAM, a memory chip, an integrated circuit, an optically or magnetically readable medium (e.g., a CD, a DVD, a magnetic disk, a magnetic tape, etc.).

[0023] Certain embodiments of the present disclosure may provide a computer program comprising instructions or code that, when executed, implements a method, system and / or apparatus according to any aspect, claim, example and / or embodiment disclosed herein. Certain embodiments of the present disclosure provide machine-readable storage for storing such a program.

[0024] The techniques described herein may be implemented using any suitably configured apparatus and / or system. Such an apparatus and / or system may be configured to perform a method according to any aspect, embodiment, example, or claim disclosed herein. Such an apparatus may include one or more elements, such as, for example, a receiver, a transmitter, a transceiver, a processor, a controller, a module, a unit, etc., each of which may be configured to perform one or more corresponding processes, operations, and / or method steps for implementing the techniques described herein. For example, an operation / function of X may be performed by a module configured to perform X (or an X module). One or more elements may be implemented in the form of hardware, software, or any combination of hardware and software.

[0025] As described in further detail below, various examples of the present disclosure provide techniques (eg, methods and apparatus) for acquiring, characterizing, and / or classifying objects (eg, particles) in 3D.

[0026] In certain instances, the object may be in motion (eg, flowing).

[0027] This technique may involve simultaneous capture of a dynamic object by multiple imaging devices (e.g., cameras). The images captured by the imaging devices provide a 2D projection / image of the 3D object. The positions of the imaging devices relative to the imaged object and / or relative to each other may be known within a certain tolerance.

[0028] The technique may further include means for calibrating the 2D image to remove any potential misalignment and / or blurring due to motion of dynamic objects. This dynamic calibration technique allows for flexibility in the position and acquisition frequency (e.g., frames per second) of the acquisition device.

[0029] The technique may further include means for adjusting the drag force applied to the object to control the speed of the object's movement and the level of image blur.

[0030] The technique may further include means for processing and reconstructing the 2D images into a 3D object and characterizing and classifying its morphology. For example, classifications may include "flat," "compact," "blade-like," and "elongated."

[0031] By simultaneously acquiring the morphology of an object from different viewpoints or views, the morphology can be robustly reconstructed regardless of the object's orientation and dynamic rotation.

[0032] In certain examples, a continuous stream of objects may flow with an effective "reduced gravity" to facilitate acquisition of the morphology. For example, deceleration of the free fall of an object may be facilitated by introducing a drag field that opposes the natural gravitational field to the motion of the object. In certain examples, the drag field may be generated by one or more of the following non-limiting examples: air flow, liquid flow, acoustic streaming, magnetic field.

[0033] The techniques disclosed herein are applicable to objects of various sizes, from sub-micron to several microns to several centimeters.

[0034] In a particular example, the object can move simultaneously in the view axes of all imaging devices.

[0035] In certain examples, the 2D projections may be calibrated to account for any misalignment of the center of the field of view. In certain examples, the 2D projections may be calibrated to account for misalignment of the imaging devices. In certain examples, the 2D projections may be aligned to a center of the field of view common to all imaging devices.

[0036] In certain instances, the 2D projections may be calibrated to resolve any blurring by dynamic calibration objects moving in the flow. These may be objects of known size and / or shape that cover some or all of the classification region, which may be, for example, "flat," "compact," "blade-like," "elongated," etc.

[0037] In certain examples, certain properties of the 3D object (such as volume and surface area) may be calculated from its 3D reconstruction. In certain examples, 3D morphological descriptors may be calculated based on the 3D reconstruction.

[0038] The image processing and 3D reconstruction may be implemented in the form of a computer-based algorithm. The algorithm may identify the object, characterize the 3D morphology of the object, and classify the object based on the 3D morphology. The algorithm may then proceed to characterize the next object.

[0039] 1 is a schematic diagram of a system 100 according to an example of the present disclosure. As shown in FIG. 1, the system includes an imaging module 101, a 3D shape reconstruction module 103, and a 3D shape classification module 105.

[0040] The imaging module 101 is configured to capture images of one or more objects. In the present disclosure, the objects are particles. However, one skilled in the art will appreciate that the present disclosure is not limited to particles and may be applied to other suitable types of objects. Furthermore, in the present disclosure, the objects may be of any suitable scale, such as, for example, centimeter (cm) scale (e.g., 1-10 cm), millimeter (mm) scale (e.g., 1-10 mm), micron (μm) scale (e.g., 1-1000 μm), or sub-micron scale (<1 μm). In a particular example, the techniques disclosed herein may be applied to objects from 100 μm to 50 mm. However, one skilled in the art will appreciate that the present disclosure is not limited to these scales and may be applied to objects / particles of any suitable scale or size. An example of a cm-scale object is ballast particles. An example of a mm-scale object is particles of alumina or recycled glass. An example of a micron-scale object is gas-atomized steel powder for additive manufacturing. Examples of sub-micron scale objects are magnesium stearate in pharmaceutical tablets and pyrite particles in shale samples. Further examples include any particulate geological material, chemicals, pharmaceutical powders, active pharmaceutical ingredients, foods, batteries, energy storage materials, spray dried materials, metal powders, mining, and minerals.

[0041] The 3D shape reconstruction module 103 is configured to process the images captured by the imaging module 101 to reconstruct a 3D shape of the object. In a particular example, the 3D shape reconstruction module 103 may be implemented in the form of software executed by a processor. Any suitable technique may be used for the 3D reconstruction. One suitable technique is disclosed in Nadimi, S. and Fonseca, J., 2017. Single-grain virtualization for contact behaviour analysis on sand. Journal of Geotechnical and Geoenvironmental Engineering, 143(9), p.06017010. A person skilled in the art will appreciate that any suitable alternative technique may be used.

[0042] The 3D shape classification module 105 is configured to classify and / or characterize the shape of the object based on the reconstructed shape determined by the 3D shape reconstruction module 103. In a particular example, the 3D shape classification module 103 may be implemented in the form of software executed by a processor, which may be the same processor as that used by the 3D shape reconstruction module 103 or may be a different processor. Any suitable technique may be used for shape classification. One suitable technique is disclosed in Angelidakis, V., Nadimi, S., Utili, S. 2021. SHape Analyser for Particle Engineering (SHAPE): Seamless characterisation and simplification of particle morphology from imaging data. Computer Physics Communications 265, 107983. A person skilled in the art will appreciate that any suitable alternative technique may be used.

[0043] Various examples of the present disclosure allow for the quantification, characterization and classification of various sizes and shapes. For example, various examples allow for both characterization and classification of particle size and shape. Particle size can be characterized in terms of volume estimation, equivalent volume sphere, inscribed and bounding sphere, bounding box, fitted ellipsoid, major particle dimensions. Particle shape can be characterized in terms of morphology (e.g., sphericity, convexity, flatness, elongation, compactness, etc., corresponding to larger particle features) and roundness and angle (e.g., sharpness of particle edges, etc., corresponding to smaller particle features).

[0044] For example, particle shape classification can be performed using the following classification system: Zingg (1935) classification system Sneed and Folk (1958) classification system Blott and Pye (2008) classification system Angelidakis et al. (2022) classification system The method may be performed using one or more of:

[0045] Size and shape characterization and classification may include the following examples: volume surface area Inertia Tensor 3D center of gravity Sieve size (equivalent sphere radius) inscribed sphere Circumscribing sphere (several methods can be used to calculate) Fitting Ellipsoid Oriented bounding box (can be calculated using multiple methods) Major particle size (several methods can be used for calculation) average surface curvature Gaussian Surface Curvature Sphericity Intercept sphericity Convexity Flatness (several different measures can be used) Elongation (several different measures can be used) Compactness (several different metrics can be used) irregularity Roundness (several different measures may be used) Angle (several different indices may be used)

[0046] A more detailed schematic diagram of an example of the imaging module 101 of Figure 1 is shown in Figures 2a and 2b. As shown in Figures 2a and 2b, the imaging module 101 includes an object placement unit 201, an imaging array 203, and a controller 205.

[0047] The object placement unit 201 is a component configured to house or hold one or more objects during image acquisition. For example, one or more objects to be imaged are placed in or on the object placement unit 201 during image acquisition. The objects may be stationary or moving during image acquisition, depending on the configuration of the object placement unit 201.

[0048] In certain examples, the object placement unit 201 may be configured to hold the object in a stationary position during image acquisition. For example, the object placement unit 201 may comprise a platform or pedestal on which the object is placed or secured. The platform or pedestal may be transparent so that an image of the object can be taken from any direction. Alternatively, the object placement unit 201 may include a transparent fluid (gas or liquid) in which the object is suspended, e.g., due to the viscosity of the fluid. In this case, the object placement unit 201 may include an acquisition chamber in which the fluid is contained. In another example, the object placement unit 201 may include a transparent solid material in which one or more objects are encapsulated.

[0049] In other particular examples, the object placement unit 201 may be configured such that objects pass through it during image acquisition. In one example, the object placement unit 201 may include a conveyor belt or similar moving platform. In another example, the object placement unit 201 may include a fluid (gas or liquid) through which the objects move during image acquisition. In this case, the object placement unit 201 may include an acquisition chamber, container, or tube in which the fluid is housed. In one example, the objects may fall through the fluid contained within the acquisition chamber (which may be static) due to the influence of gravity. In another example, the fluid may flow through the tube, pulling the objects along with it. In these examples, one or more objects may be fed into the top of the acquisition chamber or one end of the tube, and the objects may then move through the imaging field of the imaging device while images are captured, and the objects may be collected at the bottom of the acquisition chamber or the other end of the tube. This type of arrangement allows multiple objects to be imaged more quickly and easily by gradually feeding the objects into the object placement unit such that a continuous stream of objects is imaged and analyzed.

[0050] Dynamic image acquisition of free-falling particles typically requires high-speed imaging devices due to the high particle velocities, since the frame rates of conventional imaging devices are insufficient to capture a complete particle within a narrow field of view on the micrometer scale.

[0051] Thus, certain examples of the present disclosure apply one or more techniques for deceleration of otherwise relatively fast moving dynamic objects. In particular, certain examples provide a drag field 207 that acts in opposition to the direction of movement of the object. For example, the drag field can be deployed in a direction that opposes the physical gravitational field 208 to create conditions of effective gravity reduction. The drag field can be realized using any suitable technique, such as, for example, one or more of air flow, magnetic field, acoustic streaming, liquid suspension, increased viscosity (of the fluid through which the object moves), and the like. The applied technique can be selected based on the material to be analyzed, the size of the object, and their physical properties (e.g., density, magnetic behavior, solubility, etc.). The strength of the field is adjustable, for example, via a power supply unit. This may allow the technique to be applied to objects of various densities and sizes. Providing a drag field allows imaging of dynamic objects using conventional imaging techniques, such as low-cost industrial or commercial optical cameras.

[0052] The speed at which the object falls through the chamber can be reduced using several techniques, for example by introducing a drag field, which eliminates the need for high speed imaging devices, reducing costs.

[0053] The imaging array 203 comprises a number of imaging devices 206 for capturing images of one or more objects disposed at or on the object placement unit 201 (e.g., in the acquisition chamber). The imaging devices may include cameras (e.g., digital cameras), although one skilled in the art would appreciate that any other suitable type of imaging device may be used. In certain examples, the imaging devices may be monochrome to reduce the amount of data and facilitate image processing. However, one skilled in the art would appreciate that the present disclosure is not limited to monochrome imaging devices, and that grayscale or color imaging devices may be used in alternative examples. In the example shown in FIG. 2, there are six imaging devices. However, one skilled in the art would appreciate that any suitable number of cameras may be used, which may be more or less than six. In various examples of the present disclosure, the imaging array 203 is configured such that the imaging devices capture images simultaneously. In this manner, different views of the object may be captured simultaneously, facilitating 3D reconstruction of the object.

[0054] The imaging devices are positioned and oriented such that images of one or more objects are captured from a number of different angles, preferably such that each object is simultaneously visible in the field of view of all imaging devices.

[0055] Various configurations of the number, location and orientation of imaging devices can be used in various examples of the present disclosure and are not limited to the specific examples disclosed herein. In a particular example, the level of reconstruction error for different arrangements can be compared to determine an optimal arrangement.

[0056] In certain examples, the number, position, and orientation of the imaging devices may be configured such that every portion of the object's exterior is visible in at least one of the captured images. That is, the entire exterior of the object is captured in the set of images. In certain examples, only a portion of the object's exterior may be visible in a set of images captured at a particular time. In this case, the imaging devices may capture two or more images at different times, with the object being rotated in between such that a portion of the object that was not visible in the image captured at the first time becomes visible in the image captured at the second time.

[0057] In a particular example, a set (some or all) of the imaging devices may be arranged to point toward substantially the same point. For example, the imaging devices may be arranged to be located at different points on the surface of a virtual sphere (or other suitable 3D shape) and arranged to point toward the center of the virtual sphere. The imaging devices may span the entire surface of the virtual sphere or may span only a portion of the surface (e.g., a hemisphere). In a particular example, a first subset of the imaging devices may be arranged to point toward a first point and a second subset of the imaging devices may be arranged to point toward a second point. More generally, the imaging devices of two or more different subsets may be arranged to point toward two or more respective different points.

[0058] In a particular example, a set (some or all) of the imaging devices may be arranged in the same plane. For example, the imaging devices may be arranged to be located at different points of the circumference of a virtual circle (or other suitable 2D shape) and point toward the center of the virtual circle. The imaging devices may span the entire circumference of the virtual circle or may span only a portion of the circumference (e.g., a semicircle). In a particular example, a first subset of the imaging devices may be arranged in a first plane and a second subset of the imaging devices may be arranged in a second plane. More generally, two or more different subsets of imaging devices may be arranged in two different respective planes.

[0059] In certain examples, various combinations of the above configurations may be applied: for example, a set of imaging devices may be arranged in a plane, while one or more imaging devices may be arranged outside that plane, for example at the endpoints of a line perpendicular to the plane.

[0060] In certain examples, the imaging devices need not be positioned at the same distance from the object being imaged. When the imaging devices are positioned at different distances from the object, a calibration procedure can be performed, e.g., using one or more test objects of known size, to determine a scaling factor for each imaging device to correct for differences in distance between the imaging device and the object.

[0061] Next, one exemplary technique for calibrating the optical system of the system will be briefly described. A set of calibration objects (e.g., spheres, etc.) of known size is provided to the device before the material to be analyzed. The calibration objects are used to calculate the pixel size of each imaging device. In a specific example, 10 calibration objects may be used, but those skilled in the art will appreciate that any other suitable number may be used. The calibration objects (e.g., spheres) are manufactured with high precision (e.g., diameter 6±0.002 mm) and have manufacturing tolerances smaller than the resolution of the imaging device (e.g., 0.0045 mm). The use of multiple calibration objects (e.g., 10 precision spheres) improves the confidence interval of the calibration compared to using a single static calibration object. This is because, while the actual dimensions of the calibration objects are known with high precision, each calibration object is usually at a different position in the field of view, allowing a more accurate estimation of the pixel size.

[0062] Next, another exemplary technique for calibrating the optics of the system after the pixel size has been calibrated is described. This may be introduced to optimize the placement of the camera and minimize blurring during imaging of dynamic particles. A set of calibration objects of known size and / or shape covering different classification regions is fed into the device before the material to be analyzed. In a particular example, for example, 20 calibration objects may be used, such as 5 objects from each classification region, but those skilled in the art will appreciate that any other suitable number may be used. The calibration objects (e.g., ellipsoids, superellipsoids, polyhedrons, etc.) are manufactured with high precision (e.g., particle dimensions in the range of 0.02±0.001 mm to 0.2±0.001 mm) and manufacturing tolerances smaller than the resolution of the imaging device.

[0063] The dynamic calibration technique described above also provides a robust calibration of the employed frame rate, which is a key image acquisition parameter for dynamic objects. By knowing the exact dimensions of the calibration object, the level of drag required is adjusted so that the 3D reconstruction corresponds to an acceptable range of errors. The calibration of the frame rate is crucial in determining the light intensity. Thus, using the dynamic calibration technique described above ensures the robustness of the imaging capabilities of the system.

[0064] The example shown in Figure 2 includes a fixed arrangement of six cameras arranged around a central pillar (e.g., an acquisition chamber). A sample object moves around the central pillar through the imaging fields of the cameras. As the object passes the central pillar, the cameras simultaneously capture images of the object to obtain image data for processing by the 3D shape reconstruction module 103.

[0065] As mentioned above, in various examples, any suitable number of imaging devices can be used. Three orthogonal cameras may provide only limited morphological information and may result in relatively low quality 3D reconstructions. For example, when using such an arrangement, concavities on the particle surface, or small roundness and angular features may not be adequately captured and characterized. Therefore, preferably more than three cameras are used. Increasing the number of imaging devices typically improves the fidelity of the 3D reconstruction, but at the expense of increased cost. In certain examples, providing around six imaging devices may provide a reasonable balance between accuracy and cost. For example, an arrangement of six imaging devices, five in a polar arrangement and one in an orthogonal arrangement to the other devices, may provide high quality 3D reconstructions, for example, concavities and finer particle features associated with roundness and angularity may be represented.

[0066] The controller 205 is configured to control the capture of images by the imaging devices. For example, the controller can send control signals to the imaging devices to control the imaging devices to capture images simultaneously. In a particular example, the controller 205 can control each imaging device to capture two or more images at two or more respective times. The controller 205 is also configured to control the storage of the images in memory and the transmission of the images to the 3D shape reconstruction module 103. In this case, the controller can receive image data from each imaging device and store the image data in memory. The controller can retrieve the image data from the memory when all images are captured and transmit the image data to the 3D shape reconstruction module 103. Alternatively, the controller 205 can control the transfer of image data from the imaging devices to the 3D shape reconstruction module 103 as the images are captured. In this case, the image data may not be stored in memory and may be processed in real time (i.e., the shape reconstruction and classification of the object may be performed in a fraction of a second before moving on to the next particle).

[0067] The system may be calibrated, for example, using one or more test objects of known shape and / or size.

[0068] In certain examples, the imaging module 101 may further include one or more light sources 209 (e.g., LEDs) for illuminating one or more objects or for creating a bright field when the light source is in front of the imaging device to create contrast. The light sources 209 may be positioned to uniformly illuminate the objects.

[0069] Various examples of the present disclosure rely on optical imaging, and consistent lighting conditions may be employed to minimize the calibration effort spent on camera settings. To achieve high frame rates, a relatively strong LED light source may be required during image acquisition to capture particle morphology with high quality. This may cause glare even on matte textured particle surfaces. Thus, in certain examples, the imaging module 101 may include one or more light diffusers 210 for diffusing the light emitted by the light source to minimize glare due to direct reflection of light on the particle surface. For example, the light diffuser may be placed between the light source and the object being imaged. This significantly reduces image processing effort and enhances automation of post-processing and 3D reconstruction.

[0070] FIG. 3 illustrates an exemplary method 300 according to the present disclosure. In a first step 301, images of one or more objects are captured. The image capture may be performed according to any suitable technique, for example any of the techniques disclosed herein. In a next step 303, based on the captured images, a 3D shape of the one or more objects is reconstructed. The shape reconstruction may be performed according to any suitable technique, for example any of the techniques disclosed herein. In a next step 305, based on the reconstructed shape, the 3D shape of the one or more objects is classified. The classification may be performed according to any suitable technique, for example any of the techniques disclosed herein.

[0071] Figures 4a-4d show examples of acquisition, classification, characterization, simplification, and characterization of multisphere particles.

[0072] Although the present disclosure has been shown and described with reference to particular examples, it will be understood by those skilled in the art that various changes in form and details can be made therein without departing from the scope of the invention as defined by the appended claims. [Explanation of symbols]

[0073] 100 Systems 101 Imaging module 103 3D Shape Reconstruction Module 105 3D Shape Classification Module 201 Object Placement Unit 203 Imaging Array 205 Controller 206 Imaging Device 207 Drag field 208 Physical gravitational field 209 Light source 210 Light Diffuser 300 ways

Claims

1. 1. An apparatus for capturing images of one or more objects (e.g. particles) to enable 3D shape reconstruction and classification of the one or more objects, the apparatus comprising: an imaging array for capturing images of the one or more objects; an object placement unit in which the one or more objects are placed during the image capture; Equipped with the imaging array comprises a plurality of imaging devices (e.g., cameras) configured to simultaneously capture images of the one or more objects from a plurality of different angles; Device.

2. The apparatus of claim 1 , wherein the imaging array comprises more than three imaging devices (e.g., at least six imaging devices).

3. 3. The apparatus of claim 1 or 2, wherein two or more of the imaging devices are configured in a polar arrangement, with at least one imaging device configured orthogonal to the imaging devices configured in the polar arrangement.

4. the object placement unit comprises a capture chamber or tube, at least a portion of which is located within a field of view of the imaging device; the one or more objects are fed into the acquisition chamber or tube and allowed to fall under the influence of gravity through the field of view of the imaging device; a drag field is applied to the one or more objects to counteract gravitational forces on the one or more objects; 4. Apparatus according to any one of claims 1 to 3.

5. The apparatus of claim 4 , wherein the drag field is based on one or more of air flow, liquid flow, magnetic field, acoustic streaming, liquid suspension, and increased viscosity.

6. 6. The apparatus of claim 1 , further comprising one or more light sources (e.g. LEDs) for illuminating one or more objects or creating a bright field.

7. 7. The apparatus of claim 6, further comprising one or more light diffusers for diffusing light emitted by the one or more light sources.

8. The one or more objects are at the following scales: Centimeter scale Millimeter Scale Micron scale Submicron scale 8. The apparatus of claim 1 , comprising one or more objects having at least one of:

9. an imaging module (e.g. a device according to any one of claims 1 to 8) for capturing images of one or more objects; a 3D shape reconstruction module for reconstructing the 3D shape of the one or more objects based on the captured images; a 3D shape classification module for classifying the 3D shape of the one or more objects based on the reconstructed shape; A system comprising:

10. A calibration framework for transforming 2D acquisitions to the same scale and coordinates; a calibration framework for aligning the images based on the actual positions of the cameras; A calibration system for adjusting the drag system and acquisition speed; The system of claim 9, further comprising one or more of:

11. 1. A method for capturing images of one or more objects (e.g., particles) to enable reconstruction and classification of the 3D shape of the one or more objects, the method comprising simultaneously capturing images of the one or more objects from a plurality of different angles.

12. reconstructing the 3D shape of the one or more objects based on the captured images; classifying the 3D shapes of the one or more objects based on the reconstructed shapes; The method of claim 11 , further comprising one or more of:

13. The method further comprises the step of performing a calibration procedure, the calibration procedure comprising: Capturing images of one or more calibration objects of known size and / or shape.

13. The method of claim 12, comprising:

14. 14. The method of claim 13, wherein the calibration procedure further comprises determining a pixel size (e.g., a scale corresponding to an image pixel) of the imaging device based on the known size of the one or more calibration objects and the size (in pixels) of the one or more calibration objects as they appear in an image captured by the imaging device.

15. The calibration procedure comprises: reconstructing the 3D shape of the one or more calibration objects; determining the size and / or shape of the one or more calibration objects based on the reconstructed shape; comparing the determined size and / or shape to the known size and / or shape; determining one or more calibration coefficients based on a result of the comparison; 15. The method of claim 13 or 14, further comprising:

16. 16. The method of claim 11, further comprising applying a drag field to the one or more objects to counteract gravitational forces on the one or more objects.

17. The one or more objects and / or the one or more calibration objects are at the following scales: Centimeter scale Millimeter Scale Micron scale Submicron scale 17. The method of claim 11 , further comprising one or more objects and / or calibration objects having at least one of:

18. A computer program comprising instructions which, when executed by a computer or processor, cause the computer or processor to perform a method according to any one of claims 11 to 17.

19. 20. A computer or processor readable data carrier storing a computer program according to claim 18.

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