Abstract: A method of inspecting an object of manufacture, the method including: receiving: a model of the object of manufacture, the model including one or more feature of a region of interest (ROI) of the object of manufacture; and inspection requirements including a specification of required quality of image data of the ROI; moving an imager with respect to the object of manufacture, while acquiring at least one image of the ROI using the imager, wherein the moving is controlled so that the at least one image provides the required quality of image data of the ROI; extracting one or more feature of the ROI, from the at least one image; and comparing the one or more feature of the ROI with a corresponding one or more feature of the model.

Abstract: Automatic enrollment of an item of manufacture to a quality inspection system comprises using associations of enrollment images of an example of the item of manufacture to their corresponding camera poses. Enrollment images which show inspection targets (e.g., components of the item of manufacture) in views which are also suitable for use in visual inspection of further instances of the item of manufacture are identified. Their associated camera poses are selected and provided for use in inspection planning. In some embodiments, suitability of the camera pose is verified by performing inspection tests on the enrollment images.

Abstract: Systems, methods, and related technologies for automated inspection are described. In certain aspects, one or more images of a reference part can be captured and the one or more images of the reference part can be processed to generate an inspection model of the reference part. One or more regions of the inspection model can be associated with one or more analysis parameters. An inspection plan can be generated based on the inspection model and the one or more analysis parameters. Based on the inspection plan, one or more images of a part to be inspected can be captured and the one or more images of the part can be processed in relation to the analysis parameters to compute one or more determinations with respect to the part. One or more outputs can be providing based on the one or more determinations.

Abstract: Systems and methods are disclosed for automated inspection and part enrollment. In one implementation, a selection of an area of a part is received within a graphical user interface depicting a representation of the part. Based on the selection of the area of the part, a location of the selected area is determined. Image capture parameter(s) are determined based on the determined location. Based on (a) the determined image capture parameters and (b) the location of the selected area, an inspection path is computed with respect to the part. The computed inspection path is executed with respect to the part via an inspection system.

Abstract: Systems, methods, and related technologies for automated inspection are described. In certain aspects, one or more images of a reference part can be captured and the one or more images of the reference part can be processed to generate an inspection model of the reference part. One or more regions of the inspection model can be associated with one or more analysis parameters. An inspection plan can be generated based on the inspection model and the one or more analysis parameters. Based on the inspection plan, one or more images of a part to be inspected can be captured and the one or more images of the part can be processed in relation to the analysis parameters to compute one or more determinations with respect to the part. One or more outputs can be providing based on the one or more determinations.

Abstract: Systems, methods, and related technologies for automated inspection are described. In certain aspects, one or more images of a reference part can be captured and the one or more images of the reference part can be processed to generate an inspection model of the reference part. One or more regions of the inspection model can be associated with one or more analysis parameters. An inspection plan can be generated based on the inspection model and the one or more analysis parameters. Based on the inspection plan, one or more images of a part to be inspected can be captured and the one or more images of the part can be processed in relation to the analysis parameters to compute one or more determinations with respect to the part. One or more outputs can be providing based on the one or more determinations.

Abstract: Systems and methods are provided for recognizing characters within a distorted image. According to a one aspect, a method for recognizing one or more characters within a distorted image includes rendering one or more imitation images, the imitation images including simulations of the distorted image, applying one or more distortion models to the imitation images, thereby generating distorted imitation images, comparing the distorted imitation images with the distorted image in order to compute similarities between the distorted imitation images and the distorted image, and identifying the characters based on the best similarity. According to other aspects, the systems and methods can be configured to provide recognition of other distorted data types and elements.

Abstract: Systems and methods are disclosed for enhancing digital signals. In one implementation, a digital signal that has undergone a non-linear distortion can be received. The non-linear distortion can be reformulated as one or more linear operators that yield a statistical connection between a first signal and a second signal and one or more convex constraints on the first signal and/or the second signal. A convex minimization problem can be formulated in view of the first signal, the second signal, and the one or more convex constraints. The digital signal can be processed to solve the convex minimization problem, thereby generating an enhanced digital signal.

Abstract: Systems and methods are provided for recognizing characters within a distorted image. According to a one aspect, a method for recognizing one or more characters within a distorted image includes rendering one or more imitation images, the imitation images including simulations of the distorted image, applying one or more distortion models to the imitation images, thereby generating distorted imitation images, comparing the distorted imitation images with the distorted image in order to compute similarities between the distorted imitation images and the distorted image, and identifying the characters based on the best similarity. According to other aspects, the systems and methods can be configured to provide recognition of other distorted data types and elements.

Abstract: Provided is an iterative method of estimating the pose of a moving PTZ camera. The first step is to use an image registration method on a reference image and a current image to calculate a matrix that estimates the motion of sets of points corresponding to the same object in both images. Information about the absolute camera pose, embedded in the matrix obtained in the first step, is used to simultaneously recalculate both the starting positions in the reference image and the motion estimate. The recalculated starting positions and motion estimate are used to determine the pose of the camera in the current image. The current image is taken as a new reference image, a new current image is selected and the process is repeated in order to determine the pose of the camera in the new current image. The entire process is repeated until the camera stops moving.

Abstract: Provided is an iterative method of estimating the pose of a moving PTZ camera. The first step is to use an image registration method on a reference image and a current image to calculate a matrix that estimates the motion of sets of points corresponding to the same object in both images. Information about the absolute camera pose, embedded in the matrix obtained in the first step, is used to simultaneously recalculate both the starting positions in the reference image and the motion estimate. The recalculated starting positions and motion estimate are used to determine the pose of the camera in the current image. The current image is taken as a new reference image, a new current image is selected and the process is repeated in order to determine the pose of the camera in the new current image. The entire process is repeated until the camera stops moving.