Sightline Innovation Inc.

Other

Wallace Trenholm

Lorenzo Pons

Mark Alexiuk

Jason Cassidy

Maithili Mavinkurve

Hieu Dang

Siavash Malektaji

Kamal Darchinimaragheh

A system and method for non-destructive optical coherence tomography (OCT) is provided. The system includes: an input interface for receiving OCT data including at least a C-scan; a processing unit executable to detect a feature on a surface or subsurface of the object, trained using a training set and configured to: separate the C-scan into A-scans; using a neural network, successively analyze each A-scan to detect the presence of an A-scan feature associated with the object; separate the C-scan into B-scans; segment each of the B-scans to determine thresholds associated with the object; using a neural network, successively analyze each segmented B-scan to detect the presence of an B-scan feature associated with the object; convert the C-scan to one or more two-dimensional representations; and using a neural network, detect the presence of an C-scan feature associated with the object.

Neural network system for non-destructive optical coherence tomography

A method and system for increasing data quality of a dataset for semi-supervised machine learning analysis. The method includes: receiving known class label information for a portion of the data in the dataset; receiving clustering parameters from a user; determining a data cleanliness factor, and where the data cleanliness factor is below a predetermined cleanliness threshold: assigning data without class label information as a data point to a cluster using the clustering parameters, each cluster having a cluster class label associated with such cluster; and determining a measure of assignment, and where the measure of assignment for each data point is below a predetermined assignment threshold, receiving a class label for such data points, otherwise, assigning the respective cluster class label to each data point with the respective measure of assignment below the predetermined assignment threshold; and otherwise, outputting the dataset with associated class labels for machine learning analysis.

System and method for increasing data quality in a machine learning process

A system and method for surface inspection of an object using optical coherence tomography (OCT) is provided. The method includes determining a first working distance; determining a first depth of field, based on the first working distance; changing the depth of field to the first depth of field; performing an A-scan of the object; moving the object; determining a subsequent working distance; determining whether the object is in focus at the subsequent working distance, if the object is not in focus: determining a subsequent depth of field based on the subsequent working distance; changing the depth of field to the first depth of field; and performing an A-scan of the object; and otherwise, performing an A-scan of the object.

Adjustable depth of field optical coherence tomography

A system and method for acquiring imaging data of an object. The system includes a beam splitter for directing a derivative sample beam towards the object and a derivative reference beam towards a reflective element; a detector for detecting at least one of the returned reference beam and the returned sample beam; a mode switching module for selecting an operating mode between an optical coherence tomography (OCT) mode and a hyperspectral imaging (HSI) mode; and a computing module for receiving the detection from the detector and the mode from the mode switching module, wherein in the OCT mode, the computing device generates OCT imaging data by determining an interference pattern produced by a superposition of the returned reference beam and the returned sample beam, and wherein in the HSI mode, the computing module generating hyperspectral imaging data by determining hyperspectral information of the returned sample beam.

Multimodal image data acquisition system and method

A system and method for surface inspection of an object using optical coherence tomography (OCT) with anticipatory depth of field adjustment is provided. The method includes determining a present working distance and one or more forward working distances; determining a present depth of field in which the surface of the object is in focus at the location of the present working distance and at as many of the consecutive forward surface locations as determined possible; changing to the present depth of field; performing an A-scan of the object; moving the object such that the scanner head is directed at each of the consecutive forward surface locations determined to be in the present depth of field; and performing an A-scan at each of the consecutive forward surface locations determined to be in the present depth of field.

Anticipatory depth of field adjustment for optical coherence tomography

A system and method for surface inspection of an object using optical coherence tomography (OCT) is provided. The method includes determining a surface profile of the object, the surface profile includes one or more regions on a surface of the object; moving the object relative to the OCT scanner head; and for each of the one or more regions on the surface of the object, performing: determining a working distance where the surface of the object at the respective region is within a present depth of field; determining an angle where the respective region is at the determined working distance from an OCT scanner head; directing the OCT scanner head at the determined angle towards the respective region when the respective region is at the determined working distance along the respective angle; and performing an A-scan of the object when the respective region is within the present depth of field.

Steerable focal adjustment for optical coherence tomography

A system and method for surface inspection of an object using multiplexed optical coherence tomography (OCT) is provided. The method includes moving the object relative to two or more scanner heads along a direction of travel; alternatingly directing a sample beam to each of the two or more scanner heads; and when the sample beam is directed at each respective scanner head, scanning comprising: steering the sample beam from the respective scanner head to an unscanned region on the surface of the object; and performing an A-scan of the object.

System and method of surface inspection of an object using mulitplexed optical coherence tomography

Embodiments described herein relate to systems and methods for specular surface inspection, and particularly to systems and methods for surface inspection comprising inverse synthetic aperture imaging ('ISAI') and specular surface geometry imaging ('SSGI'). Embodiments may allow an object under inspection, to be observed, imaged and processed while continuing to be in motion. Further, multiple optical input sources may be provided, such that the object does not have to be in full view of all optical sensors at once. Further, multi-stage surface inspection may be provided, wherein an object under inspection may be inspected at multiple stages of an inspection system, such as, for an automotive painting process, inspection at primer, inspection at paint, inspection at final assembly. SSGI imaging modules are also described for carrying out micro-deflectometry.

System and method for specular surface inspection

There is provided systems and methods for laser scanning of one or more surfaces; in a particular embodiment, laser scanning of concrete. The system includes: at least one laser scanning module for acquiring imaging data comprising one or more distance measurements between the laser scanning module and each of the surfaces, according to a laser scanning modality; and a computing module, including one or more processors, in communication with the laser scanning module, for: aggregating the imaging data; processing and denoising the imaging data; and determining the presence or absence of a condition on each of the surfaces based on the imaging data.

System and method for integrated laser scanning and signal processing

An application provisioning system and method. A server provides an application provisioning service. A user of a client provides a schema defining an application. The application interacts with peripherals coupled to the client and receives input from sensors coupled to the peripherals. The sensor data is provided to the server for processing, including by neural networks. The application includes a workflow defining a finite state machine that traverses states at least partially based on the response to sensor data. The server may provide dynamic reallocation of compute resources to resolve demand for classifier training job requests; use of jurisdictional certificates to define data usage and sharing; and data fusion. Applications include manufacturing verification, medical diagnosis and treatment, genomics and viral detection.

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Toronto, Canada

Wallace Trenholm