Sets image pixels in ROI to the specified value.

Copies pixels from one image to another.

Creates a depth image from the Z-values of the input surface.

Allows for performing 2D operations on 3D data.

Creates an image filled with one color.

Most typically used to prepare a background for image drawing tools.

Extracts an array of pixel values from a single column of an image.

Returns a single pixel of an image.

Extracts an array of pixel values from a single row of an image.

Returns a box corresponding to the dimensions of an image.

Converts a single-channel image to a matrix.

Creates an image by joining 1D profiles, which appear in consecutive iterations.

Usually used for merging 3D profiles into a depth image.

Converts a pixel array to a single-channel image.

Creates a new image with a standard pitch alignment.

Can be used to normalize an image created with a non-standard third-party camera or framegrabber.

Sets pixel values in a single entire column of an image.

Allows for creating images from calculated real values. It is significantly slower than SetImageRow.

Sets multiple pixels of an image to the selected value (all the pixels get the same value).

Sets pixels of an image to the selected values (each pixel gets another value).

Returns a sample image.

Makes it possible to quickly present results of various image processing filters.

Converts color space from Cyan-Magenta-Yellow-Key into Red-Green-Blue.

CMYK color space is designed for printing - not recommended for machine vision.

Converts color space from Hue-Saturation-Luminance to Red-Green-Blue.

Converts color space from L*a*b* to Red-Green-Blue.

Converts a RGB565 image into a RGB888 one.

Decompression of pixel depth from 2 to 3 bytes.

Converts a RGB888 image into a RGB565 one.

Compression of pixel depth from 3 to 2 bytes.

Converts color space from Red-Green-Blue into Hue-Saturation-Intensity.

Color analysis is easier in the HSI color space than in RGB.

Converts color space from Red-Green-Blue to Hue-Saturation-Value.

Color analysis is easier in the HSV color space than in RGB.

Converts color space from Red-Green-Blue to XYZ.

XYZ color space is designed for human perception - not recommended for machine vision.

Converts color space from Red-Green-Blue into pseudointensitY-Orange-Green-with-Reversibility.

Creates several monochromatic images from individual pixels of the input Bayer pattern image.

Converts color space from pseudointensitY-Orange-Green-with-Reversibility into Red-Green-Blue.

Converts a YUV-encoded image into RGB color space.

Adds two images pixel by pixel.

Adds images appearing in consecutive iterations pixel by pixel.

Averages images from an array pixel by pixel.

Computes weighted sum pixel by pixel.

Computes the non-negative distances between corresponding pixel values.

Useful for things like comparing an image against a template or for detecting differences between consecutive video frames.

Computes the non-negative distances between corresponding pixel values.

Useful for things like comparing an image against a template or for detecting differences between consecutive video frames.

Interpolates two images linearly pixel by pixel.

Creates an image from the higher pixel values of each corresponding pair.

Computes the maximum of images appearing in consecutive iterations pixel by pixel.

Creates an image from the lower pixel values of each corresponding pair.

Computes the minimum of images appearing in consecutive iterations pixel by pixel.

Multiplies images of an array pixel by pixel.

For each pixel location, get value of n-th (increasing) pixel among input images.

Averages images appearing in consecutive iterations pixel by pixel using exponential rolling average.

Subtracts two images pixel by pixel. The result is signed.

Creates a monochromatic image by summing the values of the input image channels with saturation.

Creates a monochromatic image by averaging the input image channels.

Creates a monochromatic image by averaging the input image channels.

Conversion to mono which is more consistent with human perception (our eyes are more sensitive to green).

Changes the type of pixel components.

Creates a monochromatic image by taking the maximum value of all the input image channels.

Creates a monochromatic image by taking the minimum value of all the input image channels.

Creates an image by removing a single channel from the input image.

Creates an image from a single channel of the input image.

Creates several monochromatic images from individual channels of the input image and sets Nil for channels that are not present.

Draws a box on an image.

Draws a pair of arrows representing a coordinate system on an image.

Draws a dimension line on an image.

Draws an image as a tile on an image considered to be a grid of tiles.

Draws a grid of images.

Draws a path on an image.

Draws a rectangle on an image.

Draws a segment on an image.

Draws a string on an image.

Creates a new Heatmap object.

Colorizes a heat-map and blends it with a background image.

Adjusts image colors by stretching each channel separately.

Speculatively sets pixel values outside of a region using the fast marching method and coherence analysis.

Fills in a region of an image with pixel values interpolated from the borders of the area.

Speculative setting of unknown (or unwanted) pixels. Usually used for preparing incomplete images for further processing.

Fills in a region of an image with pixel values interpolated from the borders of the area; uses fast marching method.

Speculative setting of unknown (or unwanted) pixels. This version is trying to do this better than with simple interpolation.

Normalizes local brightness of the image. Internally uses Gauss smoothing.

Compensates uneven illumination.

Normalizes local contrast of the image using Wallis filter.

Computes an image in which the pixel values denote the estimated distances to the nearest bright pixel in the input image.

This is an initial step of some advanced traditional image analysis algorithms.

Finds ridge pixels in a gradient image.

Computes the histogram of the image pixel values.

Finds image locations characterized by locally minimal pixel values.

Detection of characteristic points, usually after some image transformations.

Computes the selected moment of an image in regular and normalized (divided by sum of pixel values) variant.

Returns an array of pixels from the input image.

Creates a series of segments across the input path, measures the average pixel intensity on each of the segments, and creates the final profile from those values.

This is the first step of all 1D Edge Detection operations. Here available for direct use by the user.

Finds ridge pixels in an image.

Calculates standard deviation of image pixel values.

Scans a disk directory for image files and then returns the images one by one in consecutive iterations sorted randomly.

Emulates image acquisition with images stored on disk.

Simulates capturing a frame from a camera.

Can be used as EnumerateImages, but its state is global in a program - does not reset when some task is finished.

Loads a single image from a file.

Loads a single image from a file stored in memory.

Use this filter when you received an image file through I/O communication, e.g. through a TcpIp connection.

Opens and creates a video stream from a file.

Acquires a frame from a previously opened video stream.

Saves an image to a file stored in ByteBuffer.

Use this filter if you want to send image file through I/O communication, e.g. through a TcpIp connection.

Saves an image to a JPEG file in the background thread.

Saves an image to a PNG file in the background thread.

Saves an image to a TIFF file in the background thread.

Writes an image to a previously opened video stream.

Performs a morphological black top hat (bottom hat) operation on a image using an arbitrary kernel.

Removes small dark structures from an image (or fills in bright ones) by applying consecutive dilation and erosion.

E.g. removal of the "pepper" component of salt-and-pepper noise.

Removes small dark structures from an image (or fills in bright ones) by applying consecutive dilation and erosion.

E.g. removal of the "pepper" component of salt-and-pepper noise.

Applies difference of Gaussians on an image, i.e. computes difference of two Gaussian smoothed images.

Emphasizes high-frequency image features such as lines or patches / dots.

Replaces each pixel with the maximum of pixels within a kernel.

Thickens bright features in an images and thins dark ones.

Replaces each pixel with the maximum of pixels within a small rectangular kernel.

Replaces each pixel with the minimum of pixels within an arbitrary kernel.

Computes an image of gradient directions mapped to the range from 1 to 255. Zero means "no edge".

For highly optimized analysis of gradient directions.

Computes a gradient image with a Sobel or Prewitt operator.

Computes an image of gradient for only selected direction.

For highly optimized analysis of gradient directions.

Removes small bright structures from an image (or fills in dark ones) by applying consecutive erosion and dilation.

E.g. removal of the "salt" component of salt-and-pepper noise.

Smooths an image while preserving sharp edges.

Smooths the result of GradientDirAndPresenceImage.

Smooths an image using a predefined gaussian kernel.

Removal of gaussian noise from images (fast).

Smooths an image by averaging pixels within an arbitrary kernel.

Usually used for computing features related to local image "windows" having non-standard shape.

Replaces each pixel with the median of pixels within a kernel.

Edge-preserving noise removal (but slow).

Replaces each pixel with the average of maximum and minimum calculated within a kernel.

Useful for calculating per-pixel threshold values for image binarization.

Smooths an region by averaging pixels within a rectangular kernel.

Usually used for computing features related to local image "windows". Can be also used for noise removal, but Gauss is superior here.

Performs a morphological white top hat operation on a image using a predefined kernel.

Performs a morphological white top hat operation on a image using a predefined mask.

Creates Look Up Table for gamma correction operation on image pixels.

Creates Look Up Table for power operation on image pixels.

Compares each pixel with the specified color using chromatic and non-chromatic information.

Color analysis insensitive to changes of illumination.

Compares two pixels using CIE94 delta E. Assumes RGB color space.

Compares two images tile by tile using one of several available methods.

Computes the correlation of pattern and image.

Measures dissimilarity between two images.

Computes the average direction within the region of interest.

Computes the average of the HSV, HSL or HSI image pixel values.

Finds the location and the value of the darkest pixel.

Computes the sum of the image pixel values.

Adds random noise to the image.

Sets pixels below the low value to the low value and above the high value to the high value.

Performs gamma correction.

Image enhancement for human perception. For computer vision consider LogarithmImage.

Applies numeric inversion (1/x) of the pixel values.

Changes pixel values for data stored in array.

Reverts the pixel value range (unsigned) or applies numeric negation (signed).

Substitutes pixels having the specified value with a new value.

Applies linear transformation to pixel values.

Raises pixel values to the second power pixel by pixel.

Subtracts a scalar value from each pixel.

Tests whether two images equal.

Tests whether a location is contained in the dimensions of an image.

Tests whether a rectangle is contained in the dimensions of an image.

Creates an array of images, each downsampled from the previous one.

Frequently used to create scale-invariant image analysis algorithms.

Creates an image from a box-shaped fragment of the input image (with margins if requested).

Reduction of the amount of image data to be stored in memory.

Creates an image from a rectangular fragment of another image (with black margins if requested).

Usually used for creating images of individual objects, e.g. after Template Matching.

Shrinks an image by the factor of 1.5 or SQRT(2) along each axis.

Usually used to speed-up image analysis at the cost of reduced precision. It is internally used to implement the pyramid strategy.

Creates an image from pixels traversed along a path.

E.g. "Unwrapping" of object contours, so that they appear as 1D structures on the output image.

Transforms an image to polar or log-polar space.

Creates a single image by glueing together many input images in horizontal or vertical direction.

Reverses the order of the input image columns or rows depending on inMirrorDirection value.

Changes one dimension of the image to a desired length while the other dimension is set to a length such that the aspect ration of the image is maintained.

Rotates an image clockwise.

Shrinks an image by a natural factor along each axis.

Translates an image by a vector, without changing its dimensions.

E.g. camera shaking reduction.

Flips and rotates an image so that columns are exchanged with rows.

Useful when operations on image columns should be replaced with operations on image rows, which are much faster due to the cache memory.

Inverse of CropImage.

Combines two spatial maps.

Makes it possible to use single RemapImage filter even if there are two or more image remapping operations (e.g. lens distortion + perspective distortion).

Joins two matrices of coordinates to produce a SpatialMap for use in RemapImage.

Splits a spatial map into two matrices of source coordinates.

Creates a spatial map for transformations from a cylinder surface to a flat rectangle.

Inspection of the surface of bottles and other cylindrical objects. The result is used by RemapImage.

Creates a spatial map representing an image polar transform.

Data preprocessing for fast image polar transform. The result is used by RemapImage.

Creates a spatial map representing an image rotation.

Preprocessing data for fast image rotation by a constant angle. The result is used by RemapImage.

Creates a perspective transform map from planes defined by paths.

Data preprocessing for fast perspective correction. The result is used by RemapImage.

Creates a spatial map for removing/adding pincushion distortion, using divisional lens distortion model.

The easiest way to remove typical lens distortion. Use together with RemapImage.

Crops a spatial map to the specified input and output boxes.

Applies a precomputed image transform, defined by a spatial map object.

Fast (precomputed) image transformations, especially for view undistortion or object geometry correction (e.g. pos recognition of labels on cylindrical bottles).

Checks if a spatial map may be applied to transform the given image.

Selects best threshold value using the image histogram.

Usually used before a thresholding filter when the image brightness is variable. Use with care.

Transforms each pixel value to maximum or minimum depending on the distance from a given color.

Color analysis with a given reference color.

Transforms each pixel value to minimum or maximum depending on whether it belongs to specified region in the HSV, HSL or HSI color space.

Color analysis.

Transforms each pixel value to maximum or minimum depending on whether they belong to the specified range.

Image binarization when the illumination is constant and uniform.

Transforms each pixel value to minimum or maximum depending on whether it belongs to the specified range for each individual pixel component.

Multi-channel thresholding.

Creates a region containing image pixels with values close to the given color.

Color analysis with a given reference color.

Creates a region containing image pixels which belongs to specified region in HSV, HSL or HSI space.

Extraction of a region characterized with a specific color.

Thresholds an image using two relative thresholds and produces two regions (Dark and Bright) in a single image pass.

This filter can replace two instances of ThresholdToRegion_Relative which may slightly improve performance.

Generates an array of regions and an array of boxes covering the area of an image.

Generates an array of small images by cutting the input image.

Joins previously cut tiles into single image.

Adjusts gradient angles to a given range.

Rescales an image treating pixels as vectors.