arrayfire-0.4.0.0: Haskell bindings to the ArrayFire general-purpose GPU library

Copyright David Johnson (c) 2019-2020 BSD 3 David Johnson Experimental GHC None Haskell2010

ArrayFire.Vision

Description

Synopsis

# Documentation

Arguments

 :: Array a Array containing a grayscale image (color images are not supported) -> Float FAST threshold for which a pixel of the circle around the central pixel is considered to be greater or smaller -> Int Length of arc (or sequential segment) to be tested, must be within range [9-16] -> Bool Performs non-maximal suppression if true -> Float Maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded -> Int Is the length of the edges in the image to be discarded by FAST (minimum is 3, as the radius of the circle) -> Features Struct containing arrays for x and y coordinates and score, while array orientation is set to 0 as FAST does not compute orientation, and size is set to 1 as FAST does not compute multiple scales

FAST feature detectors

ArrayFire Docs

A circle of radius 3 pixels, translating into a total of 16 pixels, is checked for sequential segments of pixels much brighter or much darker than the central one. For a pixel p to be considered a feature, there must exist a sequential segment of arc_length pixels in the circle around it such that all are greather than (p + thr) or smaller than (p - thr). After all features in the image are detected, if nonmax is true, the non-maximal suppression is applied, checking all detected features and the features detected in its 8-neighborhood and discard it if its score is non maximal.

Arguments

 :: Array a array containing a grayscale image (color images are not supported) -> Int maximum number of corners to keep, only retains those with highest Harris responses -> Float minimum response in order for a corner to be retained, only used if max_corners = 0 -> Float the standard deviation of a circular window (its dimensions will be calculated according to the standard deviation), the covariation matrix will be calculated to a circular neighborhood of this standard deviation (only used when block_size == 0, must be >= 0.5f and <= 5.0f) -> Int square window size, the covariation matrix will be calculated to a square neighborhood of this size (must be >= 3 and <= 31) -> Float struct containing arrays for x and y coordinates and score (Harris response), while arrays orientation and size are set to 0 and 1, respectively, because Harris does not compute that information -> Features

Harris corner detection

ArrayFire Docs

Harris corner detector.

Arguments

 :: Array a Array containing a grayscale image (color images are not supported) -> Float FAST threshold for which a pixel of the circle around the central pixel is considered to be brighter or darker -> Int maximum number of features to hold (will only keep the max_feat features with higher Harris responses) -> Float factor to downsample the input image, meaning that each level will hold prior level dimensions divided by scl_fctr -> Int number of levels to be computed for the image pyramid -> Bool blur image with a Gaussian filter with sigma=2 before computing descriptors to increase robustness against noise if true -> (Features, Array a) Features struct composed of arrays for x and y coordinates, score, orientation and size of selected features

ORB Feature descriptor

ArrayFire Docs

Extract ORB descriptors from FAST features that hold higher Harris responses. FAST does not compute orientation, thus, orientation of features is calculated using the intensity centroid. As FAST is also not multi-scale enabled, a multi-scale pyramid is calculated by downsampling the input image multiple times followed by FAST feature detection on each scale.

Arguments

 :: Array a Array containing a grayscale image (color images are not supported) -> Int number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 -> Float threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 -> Float threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 -> Float the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 -> Bool if true, the input image dimensions will be doubled and the doubled image will be used for the first octave -> Float the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1256, if the ranges are 0-1, the proper intensity-scale value is 11 -> Float maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded -> (Features, Array a) Features object composed of arrays for x and y coordinates, score, orientation and size of selected features Nx128 array containing extracted descriptors, where N is the number of features found by SIFT

SIFT feature detector and descriptor extractor.

ArrayFire Docs

C Interface for SIFT feature detector and descriptor.

Arguments

 :: Array a Array containing a grayscale image (color images are not supported) -> Int number of layers per octave, the number of octaves is computed automatically according to the input image dimensions, the original SIFT paper suggests 3 -> Float threshold used to filter out features that have low contrast, the original SIFT paper suggests 0.04 -> Float threshold used to filter out features that are too edge-like, the original SIFT paper suggests 10.0 -> Float the sigma value used to filter the input image at the first octave, the original SIFT paper suggests 1.6 -> Bool if true, the input image dimensions will be doubled and the doubled image will be used for the first octave -> Float the inverse of the difference between the minimum and maximum grayscale intensity value, e.g.: if the ranges are 0-256, the proper intensity_scale value is 1256, if the ranges are 0-1, the proper intensity-scale value is 11 -> Float maximum ratio of features to detect, the maximum number of features is calculated by feature_ratio * in.elements(). The maximum number of features is not based on the score, instead, features detected after the limit is reached are discarded -> (Features, Array a) Features object composed of arrays for x and y coordinates, score, orientation and size of selected features ^ Nx272 array containing extracted GLOH descriptors, where N is the number of features found by SIFT

SIFT feature detector and descriptor extractor.

ArrayFire Docs

C Interface for SIFT feature detector and descriptor.

Arguments

 :: Array a is the Array containing the data to be queried -> Array a is the Array containing the data used as training data -> Int indicates the dimension to analyze for distance (the dimension indicated here must be of equal length for both query and train arrays) -> Int is the number of smallest distances to return (currently, only 1 is supported) -> (Array a, Array a) is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the index of the Jth smallest distance to the Ith query value in the train data array. the index of the Ith smallest distance of the Mth query. is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the Hamming distance of the Jth smallest distance to the Ith query value in the train data array.

Hamming Matcher

ArrayFire Docs

Calculates Hamming distances between two 2-dimensional arrays containing features, one of the arrays containing the training data and the other the query data. One of the dimensions of the both arrays must be equal among them, identifying the length of each feature. The other dimension indicates the total number of features in each of the training and query arrays. Two 1-dimensional arrays are created as results, one containg the smallest N distances of the query array and another containing the indices of these distances in the training array. The resulting 1-dimensional arrays have length equal to the number of features contained in the query array.

Arguments

 :: Array a is the array containing the data to be queried -> Array a is the array containing the data used as training data -> Int indicates the dimension to analyze for distance (the dimension indicated here must be of equal length for both query and train arrays) -> Int is the number of smallest distances to return (currently, only values <= 256 are supported) -> MatchType is the distance computation type. Currently AF_SAD (sum of absolute differences), AF_SSD (sum of squared differences), and AF_SHD (hamming distances) are supported. -> (Array a, Array a) is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the index of the Jth smallest distance to the Ith query value in the train data array. the index of the Ith smallest distance of the Mth query. is an array of MxN size, where M is equal to the number of query features and N is equal to n_dist. The value at position IxJ indicates the distance of the Jth smallest distance to the Ith query value in the train data array based on the dist_type chosen.

Nearest Neighbor

ArrayFire Docs

Calculates nearest distances between two 2-dimensional arrays containing features based on the type of distance computation chosen. Currently, AF_SAD (sum of absolute differences), AF_SSD (sum of squared differences) and AF_SHD (hamming distance) are supported. One of the arrays containing the training data and the other the query data. One of the dimensions of the both arrays must be equal among them, identifying the length of each feature. The other dimension indicates the total number of features in each of the training and query arrays. Two 1-dimensional arrays are created as results, one containg the smallest N distances of the query array and another containing the indices of these distances in the training array. The resulting 1-dimensional arrays have length equal to the number of features contained in the query array.

Arguments

 :: Array a is an Array with image data -> Array a is the template we are looking for in the image -> MatchType is metric that should be used to calculate the disparity between window in the image and the template image. It can be one of the values defined by the enum af_match_type -> Array a will have disparity values for the window starting at corresponding pixel position

Nearest Neighbor

ArrayFire Docs

C Interface for image template matching.

Arguments

 :: Array a is input grayscale/intensity image -> Int nucleus radius for each pixel neighborhood -> Float intensity difference threshold a.k.a t from equations in description -> Float geometric threshold -> Float is maximum number of features that will be returned by the function -> Int indicates how many pixels width area should be skipped for corner detection -> Features

SUSAN corner detector.

ArrayFire Docs

SUSAN is an acronym standing for Smallest Univalue Segment Assimilating Nucleus. This method places a circular disc over the pixel to be tested (a.k.a nucleus) to compute the corner measure of that corresponding pixel. The region covered by the circular disc is M, and a pixel in this region is represented by m M where m 0 is the nucleus. Every pixel in the region is compared to the nucleus using the following comparison function:

Arguments

 :: Array a is input image -> Int is the radius of first gaussian kernel -> Int is the radius of second gaussian kernel -> Array a is difference of smoothed inputs

Difference of Gaussians.

ArrayFire Docs

Given an image, this function computes two different versions of smoothed input image using the difference smoothing parameters and subtracts one from the other and returns the result.

Arguments

 :: AFType a => Array a x coordinates of the source points. -> Array a y coordinates of the source points. -> Array a x coordinates of the destination points. -> Array a y coordinates of the destination points. -> HomographyType htype, can be AF_HOMOGRAPHY_RANSAC, for which a RANdom SAmple Consensus will be used to evaluate the homography quality (e.g., number of inliers), or AF_HOMOGRAPHY_LMEDS, which will use Least Median of Squares method to evaluate homography quality. -> Float If htype is AF_HOMOGRAPHY_RANSAC, this parameter will five the maximum L2-distance for a point to be considered an inlier. -> Int maximum number of iterations when htype is AF_HOMOGRAPHY_RANSAC and backend is CPU, if backend is CUDA or OpenCL, iterations is the total number of iterations, an iteration is a selection of 4 random points for which the homography is estimated and evaluated for number of inliers. -> (Int, Array a) is a 3x3 array containing the estimated homography. is the number of inliers that the homography was estimated to comprise, in the case that htype is AF_HOMOGRAPHY_RANSAC, a higher inlier_thr value will increase the estimated inliers. Note that if the number of inliers is too low, it is likely that a bad homography will be returned.

Homography Estimation.

ArrayFire Docs

Homography estimation find a perspective transform between two sets of 2D points.