Abstract: The present application provides an image processing method, an image processing apparatus, an electronic device and a computer-readable storage medium, and relates to the field of image processing technologies. An implementation includes: acquiring an image to be processed; converting the image to be processed into a three-channel YUV image; performing a convolution operation on a Y-channel image, a U-channel image and a V-channel image in the three-channel YUV image to generate an R-channel image, a G-channel image and a B-channel image, respectively, and acquiring a three-channel RGB image; and pre-processing the three-channel RGB image. According to the present application, the image pre-processing speed can be improved.

Abstract: An optical device coating assembly is provided. The optical device coating assembly includes a substrate support operable to retain an optical device substrate. The coating assembly further includes a first actuator connected to the substrate support. The first actuator is configured to rotate the substrate support. The coating assembly includes a holder configured to hold a coating applicator against an edge of the optical device substrate when the optical device substrate is rotated on the substrate support and a second actuator operable to apply a force on the holder in a direction towards the substrate support. The second actuator is a constant force actuator.

Abstract: An optical device coating assembly is provided. The optical device coating assembly includes a substrate support operable to retain an optical device substrate. The coating assembly further includes a first actuator connected to the substrate support. The first actuator is configured to rotate the substrate support. The coating assembly includes a holder configured to hold a coating applicator against an edge of the optical device substrate when the optical device substrate is rotated on the substrate support and a second actuator operable to apply a force on the holder in a direction towards the substrate support. The second actuator is a constant force actuator.

Abstract: Systems and methods for managing multi-objective alignments in imprinting (e.g., single-sided or double-sided) are provided. An example system includes rollers for moving a template roll, a stage for holding a substrate, a dispenser for dispensing resist on the substrate, a light source for curing the resist to form an imprint on the substrate when a template of the template roll is pressed into the resist on the substrate, a first inspection system for registering a fiducial mark of the template to determine a template offset, a second inspection system for registering the imprint on the substrate to determine a wafer registration offset between a target location and an actual location of the imprint, and a controller for controlling to move the substrate with the resist below the template based on the template offset, and determine an overlay bias of the imprint on the substrate based on the wafer registration offset.

Abstract: Methods of dicing optical devices from an optical device substrate are disclosed. The methods include disposing a protective coating only over the optical devices. The optical device substrate includes the optical devices disposed on the surface of the optical device substrate with areas therebetween. The areas of the optical device substrate are exposed by the protective coating. The protective coating includes a polymer, a solvent, and an additive. The methods further include curing the protective coating via a cure process so that the protective coating is water-soluble after the solvent is removed by the cure process, dicing the optical devices from the optical device substrate by projecting a laser beam to the areas between the optical devices, and exposing the protective coating to water to remove the protective coating from the optical devices that are diced.

Abstract: Embodiments of the present disclosure generally relate to optical devices. More specifically, embodiments described herein relate to apparatuses and methods for gripping optical devices. In an embodiment, an apparatus for gripping an optical device includes a base coupled to a proximal end of a stem extending from a bottom surface of the base. The apparatus also includes a plurality of arms movably coupled to the bottom surface of the base. The plurality of arms are coupled to an actuator operable to move the plurality of arms laterally along a X-Y plane parallel to the bottom surface of the base. In some embodiments, the apparatus includes a suction pad operable to provide a noncontact vertical suction force.

Abstract: Embodiments of the present disclosure relate to a carrier mechanism for retaining optical devices. The carrier mechanism includes adjacent tray assemblies stacked such that a plurality of optical device lenses are retained therebetween. The carrier mechanism retains the plurality of optical device lenses without damaging the plurality of optical device lenses by contacting corners of the optical device lenses. The plurality of optical device lenses are retained with a plurality of support pins and a plurality of capture pins disposed in the tray assemblies. Each tray includes a plurality of openings corresponding to the plurality of optical device lenses such that fluids may contact the plurality of optical device lenses. The carrier mechanism may be utilized in multiple processing methods of the plurality of optical device lenses.

Abstract: In an example method of forming an optical film for an eyepiece, a curable material is dispensed into a space between a first and a second mold surface. A position of the first mold surface relative to the second mold surface is measured using a plurality of sensors. Each sensor measures a respective relative distance along a respective measurement axis between a respective point on a planar portion of the first mold surface and a respective point on a planar portion of the second mold surface. The measurement axes are parallel to each other, and the points define corresponding triangles on the first and second mold surfaces, respectively. The position of the first mold surface is adjusted relative to the second mold surface based on the measured position, and the curable material is cured to form the optical film.

Abstract: Implementations are described herein for performing depth estimation in the agricultural domain, including generating synthetic training data. In various implementations, one or more three-dimensional synthetic plants may be generated in in a three-dimensional space, wherein the one or more three-dimensional synthetic plants include homogenous and densely-distributed synthetic plant parts. The plurality of three-dimensional synthetic plants may be projected onto two-dimensional planes from first and second perspectives in the three-dimensional space to form a pair of synthetic stereoscopic images. The first and second synthetic stereoscopic images of the pair may be annotated to create a mapping between the individual synthetic plant parts across the first synthetic stereoscopic images. A feature matching machine learning model may be trained based on the mapping.

Abstract: Implementations are described herein for automatically generating synthetic training images that are usable, for instance, as training data for training machine learning models to detect and/or classify various types of plant diseases at various stages in digital images. In various implementations, one or more environmental features associated with an agricultural area may be retrieved. One or more synthetic plant models may be generated to visually simulate one or more stages of a progressive plant disease, taking into account the one or more environmental features associated with the agricultural area. The one or more synthetic plant models may be graphically incorporated into a synthetic training image that depicts the agricultural area.

Abstract: A method and apparatus for dicing optical devices from a substrate are described herein. The method includes the formation of a plurality of trenches using radiation pulses delivered to the substrate. The radiation pulses are delivered in a pattern to form trenches with varying depth as the trenches extend outward from a top surface of the optical device. The varying depth of the trenches provides edges of each of the optical devices which are slanted. The radiation pulses are UV radiation pulses and are delivered in bursts around the silhouette of the optical devices.

Abstract: Embodiments described herein provide for devices and methods for retaining optical devices. The devices and methods described herein provide for retention of the substrate without contacting sensitive portions of the substrate. The devices and methods utilize retention pads or vacuum pins to contact the exclusion zones i.e., inactive areas of the substrate to retain the substrate and prevent the substrate from moving laterally. Additionally, a holding force retains the substrate in the vertical direction, without contacting the substrate. The methods provide for adjusting the devices to account for multiple geometries of the substrate. The methods further provide for adjusting the devices, such as adjusting a gap between the optical device and a suction pad, to alter the holding force of the devices on the optical devices.

Abstract: Implementations are described herein for automatically generating quasi-realistic synthetic training images that are usable as training data for training machine learning models to perceive various types of plant traits in digital images. In various implementations, multiple labeled simulated images may be generated, each depicting simulated and labeled instance(s) of a plant having a targeted plant trait. In some implementations, the generating may include stochastically selecting features of the simulated instances of plants from a collection of plant assets associated with the targeted plant trait. The collection of plant assets may be obtained from ground truth digital image(s). In some implementations, the ground truth digital image(s) may depict real-life instances of plants having the target plant trait.

Abstract: A method and apparatus for substrate dicing are described. The method includes utilizing a laser to dice a substrate along a dicing path to form a perforated line around each device within the substrate. The dicing path is created by exposing the substrate to bursts of laser pulses at different locations around each device. The laser pulses are delivered to the substrate and may have a pulse repetition frequency of greater than about 25 MHz, a pulse width of less than about 15 picoseconds, and a laser wavelength of about 1.0 ?m to about 5 ?m.

Abstract: Implementations are described herein for automatically generating synthetic training images that are usable as training data for training machine learning models to detect, segment, and/or classify various types of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The digital image may depict the area under a lighting condition that existed in the area when a camera captured the digital image. Based at least in part on an agricultural history of the area, a plurality of three-dimensional synthetic plants may be generated. The synthetic training image may then be generated to depict the plurality of three-dimensional synthetic plants in the area. In some implementations, the generating may include graphically incorporating the plurality of three-dimensional synthetic plants with the digital image based on the lighting condition.

Abstract: Embodiments described herein provide for devices and methods for retaining optical devices. The devices and methods described herein provide for retention of the substrate without contacting sensitive portions of the substrate. The devices and methods utilize retention pads or vacuum pins to contact the exclusion zones i.e., inactive areas of the substrate to retain the substrate and prevent the substrate from moving laterally. Additionally, a holding force retains the substrate in the vertical direction, without contacting the substrate. The methods provide for adjusting the devices to account for multiple geometries of the substrate. The methods further provide for adjusting the devices, such as adjusting a gap between the optical device and a suction pad, to alter the holding force of the devices on the optical devices.

Abstract: Implementations are described herein for automatically generating labeled synthetic images that are usable as training data for training machine learning models to make an agricultural prediction based on digital images. A method includes: generating a plurality of simulated images, each simulated image depicting one or more simulated instances of a plant; for each of the plurality of simulated images, labeling the simulated image with at least one ground truth label that identifies an attribute of the one or more simulated instances of the plant depicted in the simulated image, the attribute describing both a visible portion and an occluded portion of the one or more simulated instances of the plant depicted in the simulated image; and training a machine learning model to make an agricultural prediction using the labeled plurality of simulated images.

Abstract: Implementations are described herein for automatically labeling synthetic plant parts in synthetic training images, where the synthetic training images and corresponding labels can be used as training data for training machine learning models to detect, segment, and/or classify various parts of plants in digital images. In various implementations, a digital image may be obtained that captures an area. The synthetic training image may be generated to depict one or more three-dimensional synthetic plants in the area. In many implementations, a plant mask, identifying individual plants as a whole in the synthetic training image, as well as a part mask, uniquely identifying one or more parts of the synthetic plant models, can be overlaid on the synthetic training image to label the one or more parts of the synthetic plant models.

Abstract: Implementations are described herein for realistic plant growth modeling and various applications thereof. In various implementations, a plurality of two-dimensional (2D) digital images that capture, over time, one or more of a particular type of plant based on one or more machine learning models to generate output, may be processed. The output may be analyzed to extract temporal features that capture change over time to one or more structural features of the particular type of plant. Based on the captured temporal features, a first parameter subspace of whole plant parameters may be learned, wherein the whole plant parameters are usable to generate a three-dimensional (3D) growth model that realistically simulates growth of the particular type of plant over time. Based on the first parameter subspace, one or more 3D growth models that simulate growth of the particular type of plant may be non-deterministically generated and used for various purposes.

Abstract: Implementations are described herein for automatically generating synthetic training images that are usable, for instance, as training data for training machine learning models to detect and/or classify various types of plant diseases at various stages in digital images. In various implementations, one or more environmental features associated with an agricultural area may be retrieved. One or more synthetic plant models may be generated to visually simulate one or more stages of a progressive plant disease, taking into account the one or more environmental features associated with the agricultural area. The one or more synthetic plant models may be graphically incorporated into a synthetic training image that depicts the agricultural area.