deepLocalPatchMatch: locally constrained deep feature patch matching

In stnava/patchMatchR: Local patch matching with flexible transformation models in n-dimensional images

locally constrained deep feature patch matching

Description

High-level function for deep patch matching constrained to be local.

Usage

deepLocalPatchMatch(
  movingImage,
  fixedImage,
  movingImageMask,
  fixedImageMask,
  patchSize = 32,
  knn = 1,
  localSearchRadius = 5,
  nSamples,
  block_name = "block2_conv2",
  kPackage = "RcppHNSW",
  verbose = FALSE
)

Arguments

movingImage	input image from which we extract patches that are transformed to the space of the fixed image
fixedImage	input image that provides the fixed reference domain.
movingImageMask	defines the object of interest in the movingImage
fixedImageMask	defines the object of interest in the fixedImage
patchSize	integer greater than or equal to 32.
knn	k-nearest neighbors ( should be >= 1 )
localSearchRadius	radius value passed to makePointsImage
nSamples	number of local samples (optional), can speed things up at the cost of some accuracy
block_name	name of vgg feature block, either block2_conv2 or integer. use the former for smaller patch sizes.
kPackage	name of package to use for knn
verbose	boolean

Value

correspondence data

Author(s)

Avants BB

Examples

library( keras )
library( ANTsR )
nP1 = 5
nP2 = 20
psz = 32
img <- ri( 1 ) %>% iMath( "Normalize" )
img2 <- ri( 2 ) %>% iMath( "Normalize" )
mask = randomMask( getMask( img ), nP1 )
mask2 = randomMask( getMask( img2 ), nP2 )
match = deepLocalPatchMatch( img2, img, mask, mask2 )

stnava/patchMatchR documentation built on March 23, 2022, 6:47 a.m.