Abstract: The method of monitoring an operation includes acquiring data from sensors including images of a workspace in which the operation is to be performed, identifying a human operator and a controlled element within the workspace using the acquired images, determining whether the operation has initiated based on a known activation trigger, estimating pose of the human operator using the images, monitoring state of the controlled element based on acquired data, and determining whether an abnormality occurred based on the estimated pose, the state of the controlled element, a duration of the operation, or a combination thereof.

Abstract: The method of monitoring an operation includes acquiring data from sensors including images of a workspace in which the operation is to be performed, identifying a human operator and a controlled element within the workspace using the acquired images, determining whether the operation has initiated based on a known activation trigger, estimating pose of the human operator using the images, monitoring state of the controlled element based on acquired data, and determining whether an abnormality occurred based on the estimated pose, the state of the controlled element, a duration of the operation, or a combination thereof.

Abstract: Methods and systems for detecting defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.

Abstract: The present disclosure provides a computer implemented method of image processing comprising, upon receiving of first and second images from an imaging unit, the first and second images being respectively associated with first and second rotational changes between a reference orientation and the orientations of the first and second images: processing data representative of the first image and of the second image to compensate the first and second rotational changes between the reference orientation and the respective orientations of the first and second images, thereby obtaining first and second corrected images; processing the first corrected image to detect distinctive keypoints within a fronto-parallel strip of the first corrected image; searching keypoints in the second corrected image corresponding to the detected keypoints, and estimating a geometric transformation between the first and second images based on matching the keypoints in the first and the second corrected images.

Abstract: A system and method for fraud detection may include (a) receiving, by a model based fraud detection unit, a plurality of images related to fraud attempts, (b) determining, by the unit, for each of the images, whether or not the image is related to a fraud attempt, (c) if a percentage of the images identified as related to fraud attempts is lower than a threshold, then automatically modifying at least one coefficient in the model, and repeating steps (a), (b) and (c) until a percentage of the images identified as related to fraud attempts is higher than the threshold.

Abstract: Methods and systems for detecting defects in patterns formed on a specimen are provided. One system includes one or more components executed by one or more computer subsystems, and the component(s) include first and second learning based models. The first learning based model generates simulated contours for the patterns based on a design for the specimen, and the simulated contours are expected contours of a defect free version of the patterns in images of the specimen generated by an imaging subsystem. The second learning based model is configured for generating actual contours for the patterns in at least one acquired image of the patterns formed on the specimen. The computer subsystem(s) are configured for comparing the actual contours to the simulated contours and detecting defects in the patterns formed on the specimen based on results of the comparing.

Abstract: Images are processed to compensate for rolling shutter effects. A pair of images are registered. A set of pixel rows in the first image and a corresponding set of pixel rows in the second image are obtained. A parametric model is generated characterizing a transformation between pixels in the set of pixel rows in the first image with pixels in the corresponding set of pixel rows of the second image. Using the generated parametric model, the set of pixel rows in the second image is warped with respect to the set of pixel rows in the first image, reducing rolling shutter effects.

Abstract: The present disclosure provides a computer implemented method of image processing comprising, upon receiving of first and second images from an imaging unit, the first and second images being respectively associated with first and second rotational changes between a reference orientation and the orientations of the first and second images: processing data representative of the first image and of the second image to compensate the first and second rotational changes between the reference orientation and the respective orientations of the first and second images, thereby obtaining first and second corrected images; processing the first corrected image to detect distinctive keypoints within a fronto-parallel strip of the first corrected image; searching keypoints in the second corrected image corresponding to the detected keypoints, and estimating a geometric transformation between the first and second images based on matching the keypoints in the first and the second corrected images.

Abstract: A system receives, based on processing of an inspected frame of an inspected image generated by collecting signals indicative of a pattern on an article, at least one candidate defect location in the inspected frame. The system defines a candidate patch within the inspected frame. The candidate patch is associated with the candidate defect location. The system identifies at least one similar patch in the inspected frame using a predefined similarity criterion and determines whether a defect exists at the candidate defect location based on a comparison of at least a portion of the candidate patch with at least a corresponding portion of the at least one similar patch.

Abstract: A system receives, based on processing of an inspected frame of an inspected image generated by collecting signals indicative of a pattern on an article, at least one candidate defect location in the inspected frame. The system defines a candidate patch within the inspected frame. The candidate patch is associated with the candidate defect location. The system identifies at least one similar patch in the inspected frame using a predefined similarity criterion and determines whether a defect exists at the candidate defect location based on a comparison of at least a portion of the candidate patch with at least a corresponding portion of the at least one similar patch.