Computer Vision

Locates a specified number of the strongest transitions between dark and bright pixels along a given path (without a scan map).

Very fast object detection (or presence verification) when the expected number of edges is clearly defined.

Locates a specified number of multiple pairs of opposite edges across a given path (without a scan map).

Very fast detection (or presence verification) of multiple pairs of opposite edges.

Locates multiple dark or bright pixel peaks along a given path (without a scan map).

Very fast detection of multiple thin structures like wires or scale marks - usually for counting or distance measurements.

Locates the strongest transition between dark and bright pixels along a given path (without a scan map).

Very fast detection of an object (e.g. horizontal displacement of a bottle) and simple measurements (e.g. liquid level in a bottle).

Locates the strongest pair of edges across a given path (without a scan map).

Very fast detection or measurement of an object defined by a pair of opposite edges.

Locates a specified number of the strongest high or low peaks of surface height along a given path.

Locates multiple changes of surface height along a given path.

Locates multiple pairs of changes of surface height along a given path.

Locates the strongest high or low peak of surface height along a given path.

Extracts subpixel-precise paths that represent continuous edges.

Consistent detection of contours of variable or unpredictable shape, e.g. screw thread outline or a custom piece of textile.

Extracts a pixel-precise region of continuous edges.

Consistent detection of pixels that belong to contours of variable or unpredictable shape, e.g. screw thread outline or a custom piece of textile.

Extracts subpixel-precise paths that represent bright or dark thin lines.

Consistent detection of thin structures like scratches, cracks or lines.

Translates an array of bar widths to sequence of digits or text in accordance to the selected barcode standard.

Decoding of barcodes whose bars have been measured in a non-standard way.

Detects and recognizes a single barcode on the input image.

To be used as an easy all-in-one solution for typical barcode reading applications.

Select a subset of the given points that forms a grid and assign 2D indices to them.

Recognition of a custom regular grid for the purpose of camera calibration.

Finds the line scan camera intrinsic parameters from calibration grid.

Computes camera parameters which need to be calculated only once for each camera, and are a prerequisite for other calibration filters.

Finds the telecentric camera intrinsic parameters from calibration grids.

Computes camera parameters which need to be calculated only once for each camera, and are a prerequisite for other calibration filters.

Finds the image to world plane transformation parameters using sparse world coordinate information, i.e. world coordinates are known for only a few points of the grid.

Image to world coordinates transformations, also useful for image rectification where exact bounds of output image are important, e.g. stitching.

Finds the image to world plane transformation parameters using multiple grids, using sparse world coordinate information.

Covers cases where a single grid spanning whole working area on a world plane is not feasible. Also useful when high accuracy is desired.

Converts a rectification map to a different format.

Computes a spatial map for removing lens and perspective distortion directly from an image containing circles calibration pattern. Internally uses a pinhole camera model with polynomial lens distortion.

Quick and easy one-step calibration for basic removal of lens and perspective distortions.

Computes a spatial map for transforming distorted images to rectified images defined in world coordinate plane. Defines the output geometry in world units.

Specification of output geometry in world units is especially useful in image stitching.

Detects an arbitrary size symmetric circle pattern on the image.

Generates artificial points for camera calibration. Doesn't support distortion.

Calibration testing.

Finds the world coordinates of image Edge.

Finds the world coordinates of image Gap.

Finds the world coordinates of image Path.

Finds the world coordinates of image Point.

Finds the world coordinates of image Ridge.

Finds the world coordinates of image Segment.

Finds the world coordinates of image Stripe.

Shifts world plane along its normal vector.

Correction of already calibrated world plane. Useful for avoiding recalibration when, for example, the product thickness on a conveyor belt changes by a known amount.

Finds the image coordinates of world plane Edge.

Finds the image coordinates of world plane Gap.

Finds the image coordinates of world plane Path.

Finds the image coordinates of world plane Point.

Finds the image coordinates of world plane Ridge.

Finds the image coordinates of world plane Segment.

Finds the image coordinates of world plane Stripe.

Translates a matrix of 0 or 1 values to a text in accordance to how QR codes are encoded.

Decoding of QR codes whose black and white segments have been determined in a non-standard way.

Detects and recognizes Codes in one image.

Detects and recognizes several PDF417 codes.

Detects and recognizes Codes in one image.

Detects and recognizes one Data Matrix code.

Detects and recognizes a single QR code on the input image.

Transforms an image into frequency domain using Fourier transformation.

Computes the modulus of each frequency domain image pixel.

Transforms an image in frequency domain back to spatial domain using inverse Fourier transformation.

Compute the transformation between the camera and the calibration target reference frames. (all units in mm)

Finds lines in an image using Hough Transform.

Finds a specified shape in an image using Hough Transform.

This is an old algorithm for template matching. Quite slow.

Finds the strongest circle of a given radius in the input image.

Detection of a circular or close-to-circular object like a hole, pin, pill or particle.

Verifies object presence by analysing the amount of edges in the specified region.

Quick and easy presence verification, e.g. for missing caps, screws, labels.

Verifies object presence by analysing the amount of pixels that meet the specified criteria.

Quick and easy presence verification, e.g. for missing caps, screws, labels.

Compares image edges with the edges of a perfect template. Significant differences are considered defects.

Finding general object defects by analyzing missing or excessive edges.

Create a model to be used with CompareGoldenTemplate2 filter.

Creates golden template for application in CompareGoldenTemplate_Intensity filter.

Detects corners using the Foerstner algorithm.

Detection of characteristic points on an image.

Finds line peaks on an image.

Created for applications with laser line detection.

Computes surface normals using images with light source in different places.

Computes the surface curvature using previously computed normals.

Segments an image into blobs by dynamic thresholding.

Segments an image into blobs by thresholding using a range of values.

Segments an image basing on distance to model colors.

Detection of objects of undefined shape, but characterized by uniform color and good contrast to the background.

Segments an image into blobs examining differences between neighbouring pixels values.

Detection of objects of undefined shape, but characterized by uniform brightness and good contrast to the background.

Segments an image into blobs examining differences between pixels values, first pass is tiled.

Detection of objects of undefined shape, but characterized by uniform brightness and good contrast to the background.

Provides access to internal data of the CharacterSample structure.

Ready-to-use tool for extracting and splitting character to single characters.

Splits an array of blobs by distance to computed base lines.

Converts OcrMlpModel(old type) to OcrModel.

Converts OcrModel to OcrSvmModel.

Ready-to-use tool for reading text from images using the OCR technique.

Splits the input region into a fixed-size array of regions corresponding to individual characters.

Text segmentation when the number of characters is known, usually followed by a RecognizeCharacters filter.

Trains an OCR multilayer perceptron classifier.

Computes a list of planar cells of a point cloud

Computes a list of planar cells of a surface

Translates, rotates and scales multiple contour sets (PathArray), each separately, to the edges of the input image with subpixel precision.

Fine-tune results of edge-based template matching.

Finds arcs in an image using Line Segment Detection method.

Finds a specified polygons in an image using Line Segment Detection method.

Performs a series of 1D edge detections and finds an arc that best matches the detected points.

Precise detection of an arciform edge, whose rough location is known beforehand.

Performs a series of 1D stripe detections and finds an arc that best matches the detected points.

Precise detection of a ring section, whose rough location is known beforehand.

Performs a series of 1D ridge detections and finds a circle that best matches the detected points.

Precise detection of a thin circular line, whose rough location is known beforehand.

Performs a series of 1D edge detections and creates a path from the detected points.

Tracing of an object contour, whose rough location and shape is known beforehand.

Performs a series of 1D stripe detections and creates a path from the detected points.

Tracing of a stripe, whose rough location and shape is known beforehand.

Performs a series of 1D ridge detections and finds a segment that best matches the detected points.

Precise detection of a thin straight line, whose rough location is known beforehand.

Performs a series of 1D edge detections in 3D and finds a circle that best matches the detected points.

Precise detection of a circular object or hole, whose rough location is known beforehand.

Performs a series of 1D edge detections in 3D and finds a circle that best matches the detected points.

Precise detection of a circular object or hole, whose rough location is known beforehand.

Performs a series of 1D ridge detections in 3D and creates a path from the detected points.

Tracing of a thin line, whose rough location and shape is known beforehand.

Performs a series of 1D edge detections in 3D and finds a segment that best matches the detected points.

Precise detection of a straight edge, whose rough location is known beforehand.

Performs a series of 1D edge detections in 3D and finds a segment that best matches the detected points.

Precise detection of a straight edge, whose rough location is known beforehand.

Limits the size of the EdgeModel objects created by CreateEdgeModel filter.

Creates a model for edge-based template matching.

Dynamic creation of models in the runtime environment (normally they are created interactively in Studio).

Creates a model for NCC or SAD template matching.

Dynamic creation of models in the runtime environment (normally they are created interactively in Studio).

Improves accuracy of single object matching by adding a subpixel-precise adjustment.

Usually used after an edge-based template matching tool.

Finds all occurrences of a predefined template on an image by comparing object edges.

Detection of multiple objects whose outlines are sharp and rigid. Often one of the first filters in a program.

Finds all occurrences of a predefined template on an image by analysing the normalized correlation between pixel values.

Detection of objects with blurred or unclear edges. Often one of the first filters in a program.

Finds a single occurrence of a predefined template on an image by comparing object edges.

Detection of an object whose outlines are sharp and rigid. Often one of the first filters in a program.

Finds a single occurrence of a predefined template on an image by analysing the normalized correlation between pixel values.

Detection of an object with blurred or unclear edges. Often one of the first filters in a program.

Finds a single occurrence of a predefined template on an image by analysing the Square Average Difference between pixel values.

Almost always inferior to NCC, so rarely used in real applications.

Combines results from multiple LocateSingleObject instances.

Creates histogram and map of Linear Binary Patterns (with radius 1 and size 8) of provided image.