Kitov Systems Ltd

Rtc Vision Ltd.

Dvtel, Inc.

Nir Avrahami

Joseph Rubner

Sefy Kagarlitsky

Yohai Falik

Kfir Gedalyahu

Liran Tzafri

Eran Vered

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.

Automated inspection and part enrollment

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.

Automated inspection

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.

Character recognition in distorted images

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.

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Herzliya, Israel

Nir Avrahami