Dmitry Karasik > IPA-1.08 > IPA::Morphology

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NAME ^

IPA::Morphology - morphological operators

DESCRIPTION ^

Quote from http://www.dai.ed.ac.uk/HIPR2/morops.htm:

Morphological operators often take a binary image and a structuring element as input and combine them using a set operator (intersection, union, inclusion, complement). They process objects in the input image based on characteristics of its shape, which are encoded in the structuring element.

Usually, the structuring element is sized 3x3 and has its origin at the center pixel. It is shifted over the image and at each pixel of the image its elements are compared with the set of the underlying pixels. If the two sets of elements match the condition defined by the set operator (e.g. if the set of pixels in the structuring element is a subset of the underlying image pixels), the pixel underneath the origin of the structuring element is set to a pre-defined value (0 or 1 for binary images). A morphological operator is therefore defined by its structuring element and the applied set operator.

Morphological operators can also be applied to gray-level images, e.g. to reduce noise or to brighten the image.

BWTransform IMAGE [ lookup ]

Applies 512-byte lookup LUT string ( look-up table ) to image and returns the convolution result ( hit-and-miss transform). Each byte of lookup is a set of bits, each corresponding to the 3x3 kernel index:

   |4 3 2|
   |5 0 1|
   |6 7 8|

Thus, for example, the X-shape would be represented by offset 2**0 + 2**2 + 2**4 + 2**6 + 2**8 = 341 . The byte value, corresponding to the offset in lookup string is stored in the output image.

IPA::Morphology defines several basic LUT transforms, which can be invoked by the following code:

    IPA::Morphological::bw_METHOD( $image);

or its alternative

    IPA::Morphology::BWTransform( $image, lookup => $IPA::Morphology::transform_luts{METHOD}->());

Where METHOD is one of the following string constants:

dilate

Morphological dilation

erode

Morphological erosion

isolatedremove

Remove isolated pixels

togray

Convert binary image to grayscale by applying the mean filter

invert

Inversion operator

prune

Removes 1-connected end points

break_node

Removes node points that connect 3 or more lines

Supported types: Byte

dilate IMAGE [ neighborhood = 8 ]

Performs morphological dilation operation on IMAGE and returns the result. neighborhood determines whether the algorithm assumes 4- or 8- pixel connectivity.

Supported types: Byte, Short, Long, Float, Double

erode IMAGE [ neighborhood = 8 ]

Performs morphological erosion operation on IMAGE and returns the result. neighborhood determines whether the algorithm assumes 4- or 8- pixel connectivity.

Supported types: Byte, Short, Long, Float, Double

opening IMAGE [ neighborhood = 8 ]

Performs morphological opening operation on IMAGE and returns the result. neighborhood determines whether the algorithm assumes 4- or 8- pixel connectivity.

Supported types: Byte, Short, Long, Float, Double

closing IMAGE [ neighborhood = 8 ]

Performs morphological closing operation on IMAGE and returns the result. neighborhood determines whether the algorithm assumes 4- or 8- pixel connectivity.

Supported types: Byte, Short, Long, Float, Double

gradient IMAGE [ neighborhood = 8 ]

Returns the result or the morphological gradient operator on IMAGE. neighborhood determines whether the algorithm assumes 4- or 8- pixel connectivity.

Supported types: Byte, Short, Long, Float, Double

algebraic_difference IMAGE1, IMAGE2 [ inPlace = 0 ]

Performs the algebraic difference between IMAGE1 and IMAGE2. Although this is not a morphological operator, it is often used is conjunction with ones. If the boolean flag inPlace is set, IMAGE1 contains the result.

Supported types: Byte, Short, Long, Float, Double

watershed IMAGE [ neighborhood = 4 ]

Applies the watershed segmentation to IMAGE with given neighborhood.

Supported types: Byte

reconstruct IMAGE1, IMAGE2 [ neighborhood = 8, inPlace = 0 ]

Performs morphological reconstruction of IMAGE1 under the mask IMAGE2. Images can be two intensity images or two binary images with the same size. The returned image, is an intensity or binary image, respectively.

If boolean inPlace flag is set, IMAGE2 contains the result.

neighborhood determines whether the algorithm assumes 4- or 8- pixel connectivity.

Supported types: Byte, Short, Long, Float, Double

thinning IMAGE

Applies the skeletonization algorithm, returning image with binary object maximal euclidian distance points set.

Supported types: Byte

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