Abstract: A 3D measurement method including: projecting a pattern sequence onto a moving object; capturing a first image sequence with a first camera and a second image sequence synchronously to the first image sequence with a second camera; determining corresponding image points in the two sequences; computing a trajectory of a potential object point from imaging parameters and from known movement data for each pair of image points that is to be checked for correspondence. The potential object point is imaged by both image points in case they correspond. Imaging object positions derived therefrom at each of the capture points in time into image planes respectively of the two cameras. Corresponding image points positions are determined as trajectories in the two cameras and the image points are compared with each other along predetermined image point trajectories and examined for correspondence; lastly performing 3D measurement of the moved object by triangulation.

Abstract: This invention provides a system and method for finding line features in an image that allows multiple lines to be efficiently and accurately identified and characterized. When lines are identified, the user can train the system to associate predetermined (e.g. text) labels with respect to such lines. These labels can be used to define neural net classifiers. The neural net operates at runtime to identify and score lines in a runtime image that are found using a line-finding process. The found lines can be displayed to the user with labels and an associated probability score map based upon the neural net results. Lines that are not labeled are generally deemed to have a low score, and are either not flagged by the interface, or identified as not relevant.

Abstract: Determining dimensions of an object can include determining a distance between the object and an imaging device, and an angle of an optical axis of the imaging device. One of more features of the object can be identified in an image of the object. The dimensions of the object can be determined based upon the distance, the angle, and the one or more identified features.

Abstract: This invention provides a system and method for finding multiple line features in an image. Two related steps are used to identify line features. First, the process computes x and y-components of the gradient field at each image location, projects the gradient field over a plurality subregions, and detects a plurality of gradient extrema, yielding a plurality of edge points with position and gradient. Next, the process iteratively chooses two edge points, fits a model line to them, and if edge point gradients are consistent with the model, computes the full set of inlier points whose position and gradient are consistent with that model. The candidate line with greatest inlier count is retained and the set of remaining outlier points is derived. The process then repeatedly applies the line fitting operation on this and subsequent outlier sets to find a plurality of line results. The process can be exhaustive RANSAC-based.

Abstract: Techniques include systems, computerized methods, and computer readable media for creating a graphical program in a graphical program development environment. A spreadsheet node having an input terminal in the graphical program is instantiated. The spreadsheet node is associated with a spreadsheet that specifies a list of functions to be executed in a computing device, and the input terminal is connected to the first terminal of the first node, indicating a data connection between the first terminal of the first node and the input terminal of the spreadsheet node. The input terminal of the spreadsheet node is associated with a first cell in the spreadsheet, indicating that the first cell in the spreadsheet be populated with any data received by the input terminal. A human readable file is generated specifying the graphical program, including the spreadsheet node.

Abstract: In some aspects, the techniques described herein relate to systems, methods, and computer readable media for detecting movement in a scene. A first temporal pixel image is generated based on a first set of images of a scene over time, and a second temporal pixel image is generated based on a second set of images. One or more derived values are determined based on values of the temporal pixels in the first temporal pixel image, the second temporal pixel image, or both. Correspondence data is determined based on the first temporal pixel image and the second temporal pixel image indicative of a set of correspondences between image points of the first set of images and image points of the second set of images. An indication of whether there is a likelihood of motion in the scene is determined based on the one or more derived values and the correspondence data.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a three-dimensional model. A three-dimensional model is stored, the three dimensional model comprising a set of probes. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted into a set of fields, comprising generating a first field comprising a first set of values, where each value of the first set of values is indicative of a first characteristic of an associated one or more data entries from the set of data entries, and generating a second field comprising a second set of values, where each second value of the second set of values is indicative of a second characteristic of an associated one or more data entries from the set of data entries, wherein the second characteristic is different than the first characteristic.

Abstract: Described are machine vision systems and methods for simultaneous kinematic and hand-eye calibration. A machine vision system includes a robot and a 3D sensor in communication with a control system. The control system is configured to move the robot to poses, and for each pose: capture a 3D image of calibration target features and robot joint angles. The control system is configured to obtain initial values for robot calibration parameters, and determine initial values for hand-eye calibration parameters based on the initial values for the robot calibration parameters, the 3D image, and joint angles. The control system is configured to determine final values for the hand-eye calibration parameters and robot calibration parameters by refining the hand-eye calibration parameters and robot calibration parameters to minimize a cost function.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model in three-dimensional data. Three-dimensional data of an object is received, the three-dimensional data comprising a set of data entries. The three-dimensional data is converted to a field comprising a set of cells that each have an associated value, comprising determining, for each cell value, representative data based on one or more data entries from the set of data entries of the three-dimensional data. A pose of the model is tested with the field to determine a score for the pose.

Abstract: This invention provides a vision system, typically having at least two imaging systems/image sensors that enable a multi-function unit. The first imaging system, typically a standard, on-axis optical configuration can be used for long distances and larger feature sets and the second imaging system is typically an extended-depth of focus/field (DOF) configuration. This second imaging system allows reading of smaller feature sets/objects and/or at shorter distances. The reading range of an overall (e.g.) ID-code-reading vison system is extended and relatively small objects can be accurately imaged. The extended-DOF imaging system sensor can be positioned with its longest dimension in the vertical axis. The system can allow vision system processes to compute the distance from the vision system to the object to generate an autofocus setting for variable optics in the standard imaging system. An aimer can project structured light onto the object surface around the system optical axis.

Abstract: This invention provides a vision system that is arranged to compensate for optical drift that can occur in certain variable lens assemblies, including, but not limited to, liquid lens arrangements. The system includes an image sensor operatively connected to a vision system processor, and a variable lens assembly that is controlled (e.g. by the vision processor or another range-determining device) to vary a focal distance thereof. A positive lens assembly is configured to weaken an effect of the variable lens assembly over a predetermined operational range of the object from the positive lens assembly. The variable lens assembly is located adjacent to a front or rear focal point of the positive lens. The variable lens assembly illustratively comprises a liquid lens assembly that can be inherently variable over approximately 20 diopter. In an embodiment, the lens barrel has a C-mount lens base.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to test a pose of a model to image data. Image data of an object is received, the image data comprising a set of data entries. A set of regions of the image data are determined, wherein each region in the set of regions comprises an associated set of neighboring data entries in the set of data entries. Processed image data is generated, wherein the processed image data comprises a set of cells that each have an associated value, and generating the processed image data comprises, for each region in the set of regions, determining a maximum possible score of each data entry in the associated set of neighboring data entries from the image data, setting one or more values of the set of values based on the determined maximum possible score, and testing the pose of the model using the processed image data.

Abstract: A 3D measurement method including; projecting a pattern sequence onto a moving object; capturing a first image sequence with a first camera and a second image sequence synchronously to the first image sequence with a second camera; determining corresponding image points in the two sequences; computing a trajectory of a potential object point from imaging parameters and from known movement data for each pair of image points that is to be checked for correspondence. The potential object point is imaged by both image points in case they correspond. Imaging object positions derived therefrom at each of the capture points in time into image planes respectively of the two cameras. Corresponding image points positions are determined as trajectories in the two cameras and the image points are compared with each other along predetermined image point trajectories and examined for correspondence; lastly performing 3D measurement of the moved object by triangulation.

Abstract: This invention provides a system and method for concurrently (i.e. non-serially) calibrating a plurality of 3D sensors to provide therefrom a single FOV in a vision system that allows for straightforward setup using a series of relatively straightforward steps that are supported by an intuitive graphical user interface (GUI). The system and method requires minimal data input about the scene or calibration object used to calibrate the sensors. 3D features of a stable object, typically employing one or more subobjects, are first measured by one of the image sensors, and then the feature measurements are used in a calibration in which each of the 3D sensors images a discrete one of the subobjects, resolves features thereon and computes a common coordinate space between the plurality of 3D sensors. Sensor(s) can be mounted on the arm of an encoderless robot or other conveyance and motion speed can be measured in setup.

Abstract: Systems and methods are provided for acquiring images of objects using an imaging device and a controllable mirror. The controllable mirror can be controlled to change a field of view for the imaging device, including so as to acquire images of different locations, of different parts of an object, or with different degrees of zoom.

Abstract: The present disclosure provides a high resolution structured light system that is also capable of maintaining high throughput. The high resolution structured light system includes one or more image capture devices, such as a camera and/or an image sensor, a projector, and a blurring element. The projector is configured to project a binary pattern so that the projector can operate at high throughput. The binary projection pattern is subsequently filtered by the blurring element to remove high frequency components of the binary projection pattern. This filtering smoothes out sharp edges of the binary projection pattern, thereby creating a blurred projection pattern that changes gradually from the low value to the high value. This gradual change can be used by the structured light system to resolve spatial changes in the 3D profile that could not otherwise be resolved using a binary pattern.

Abstract: This invention provides a vision system that is arranged to compensate for optical drift that can occur in certain variable lens assemblies, including, but not limited to, liquid lens arrangements. The system includes an image sensor operatively connected to a vision system processor, and a variable lens assembly that is controlled (e.g. by the vision processor or another range-determining device) to vary a focal distance thereof. A positive lens assembly is configured to weaken an effect of the variable lens assembly over a predetermined operational range of the object from the positive lens assembly. The variable lens assembly is located adjacent to a front or rear focal point of the positive lens. The variable lens assembly illustratively comprises a liquid lens assembly that can be inherently variable over approximately 20 diopter. In an embodiment, the lens barrel has a C-mount lens base.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to determine parameters for image acquisition. One or more image sensors are each arranged to capture a set of images of a scene, and each image sensor comprises a set of adjustable imaging parameters. A projector is configured to project a moving pattern on the scene, wherein the projector comprises a set of adjustable projector parameters. The set of adjustable projector parameters and the set of adjustable imaging parameters are determined, based on a set of one or more constraints, to reduce noise in 3D data generated based on the set of images.

Abstract: The techniques described herein relate to methods, apparatus, and computer readable media configured to decode a symbol in a digital image. A digital image of a portion of a symbol is received, which includes a grid of pixels and the symbol includes a grid of modules. A spatial mapping is determined between a contiguous subset of modules in the grid of modules to the grid of pixels. Causal relationships are determined, using the spatial mapping, between each module and the grid of pixels. A set of valid combinations of values of neighboring modules in the contiguous subset of modules are tested against the grid of pixels using the causal relationships. A value of at least one module of the two or more neighboring modules is determined based on the tested set of valid combinations. The symbol is decoded based on the determined value of the at least one module.

Abstract: Embodiments related to local tone mapping for symbol reading. A local pixel neighborhood metric is determined for at least one raw pixel in a region-of-interest, which identifies on one or more raw pixels near the at least one raw pixel. A local mapping function is determined for the at least one raw pixel that maps the value of the raw pixel to a mapped pixel value with a mapped bit depth that is smaller than the bit depth associated with the raw image. The local mapping function is based on a value of at least one other raw pixel near the at least one raw pixel within the local pixel neighborhood metric, and at least one parameter determined based on the raw image. A mapped image is computed for the region-of-interest by applying the local mapping function to the raw image.