The present disclosure relates to a speckles detection system for detecting one or more speckles on a surface of an optical element of an image capturing device. The system comprises: one or more light sources configured to emit a light beam towards the optical element, the optical element being configured to reflect light from the light sources when speckles are located on the surface of the optical element. The system further comprises one or more light receivers configured to receive the light beam reflected by the optical elements such that speckles on the surface of the optical elements are detected. Methods for detecting one or more speckles on a surface of an optical element of an image capturing device are also provided.

A workpiece holder is configured to hold a workpiece and is utilized in a metrology system which includes a sensing configuration for obtaining 3-dimensional surface data for the workpiece. The workpiece holder includes at least three reference features (e.g., spherical reference features extending from sides) that are configured to be sensed by the sensing configuration when the workpiece holder is in different orientations (e.g., as rotated 180 degrees between first and second orientations for presenting front and back sides of the workpiece towards the sensing configuration). A determination of 3-dimensional positions of the reference features for each orientation enables a combining (e.g., in a common coordinate system) of 3-dimensional surface data that is acquired for the workpiece in each orientation. Interchangeable workpiece holding portions may be provided that fit within the workpiece holder for holding workpieces with different characteristics (e.g., having different sizes and/or shapes).

Systems and methods for acquiring and inspecting lens images of ophthalmic lenses using one or more cameras to acquire the images of the lenses in a dry state or a wet state. The images are preprocessed and then inputted into an artificial intelligence network, such as a convolutional neural network (CNN), to analyze and characterize for type of lens defects. The artificial intelligence network identifies defect regions on the images and output defect categories or classifications for each of the images based in part on the defect regions.

A method and system for assessing the quality of at least one component of optical material which has at least one first center axis includes directing at least one light beam towards at least one detector device such that while changing the position and/or orientation of the component relative to the light beam, the light beam crosses at least from time to time the component and determining, with at least one determination device, at least one characterizing value of at least one figure of merit of the component based on analyzing, with at least one analyzing device, the dependency of a parameter of the light beam detected by the detector device on the position and/or orientation of the component.

A method and system for assessing the quality of at least one component of optical material which has at least one first center axis includes directing at least one light beam towards at least one detector device such that while changing the position and/or orientation of the component relative to the light beam, the light beam crosses at least from time to time the component and determining, with at least one determination device, at least one characterizing value of at least one figure of merit of the component based on analyzing, with at least one analyzing device, the dependency of a parameter of the light beam detected by the detector device on the position and/or orientation of the component.

Systems and methods for providing optical distortion information of a vehicle glazing are disclosed. In one example, a method comprises obtaining and analyzing, via at least one processor of a computing device, optical characteristics of the vehicle glazing; generating digitized optical distortion information for the vehicle glazing based on analysis results; generating identification information for the vehicle glazing; and associating the digitized optical distortion information with the identification information.

Systems and methods for providing optical distortion information of a vehicle glazing are disclosed. In one example, a method comprises obtaining and analyzing, via at least one processor of a computing device, optical characteristics of the vehicle glazing; generating digitized optical distortion information for the vehicle glazing based on analysis results; generating identification information for the vehicle glazing; and associating the digitized optical distortion information with the identification information.

A method for an automatic inspection of a plurality of plate-like plastic carriers, whereby each plastic carrier is closed by a cover and has a unique identification code and a culture medium. A storage container including a multiplicity of plastic carriers is provided, and a computer-controlled handling unit including an optical inspection system is provided. While performing the automatic inspection in an inspection routine, a plastic carrier is removed from the storage container with the aid of the handling unit, the cover of the plastic carrier is subsequently removed, the plastic carrier is supplied to the inspection system, the identification code is read, and at least one image of the plastic carrier, including the surface of the culture solution is recorded. The image is evaluated for a growth of germs and/or faults in the plastic carrier and the result of the evaluation is stored for each plastic carrier.

Disclosed herein are methods for quantifying contact lens deposition. An example method may comprise disposing a contact lens sample in a fluid well. The example method may comprise disposing a volume of tear fluid in the well with the contact lens sample. The example method may comprise capturing pre-rinse images of the contact lens sample. The example method may comprise rinsing the contact lens sample. The example method may comprise capturing post-rinse images of the contact lens after the rinsing. The example method may comprise determining, using one or more of the tear images or the post-rinse images, a deposition metric. The example method may comprise outputting the deposition metric.

Disclosed herein are methods for quantifying contact lens deposition. An example method may comprise disposing a contact lens sample in a fluid well. The example method may comprise disposing a volume of tear fluid in the well with the contact lens sample. The example method may comprise capturing pre-rinse images of the contact lens sample. The example method may comprise rinsing the contact lens sample. The example method may comprise capturing post-rinse images of the contact lens after the rinsing. The example method may comprise determining, using one or more of the tear images or the post-rinse images, a deposition metric. The example method may comprise outputting the deposition metric.