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20.1.3. PIKS Operators, Tools, Utilities, and Mechanisms
PIKS operators are elements that manipulate images or manipulate data objects
extracted from images in order to enhance or restore images, or to assist in the
extraction of information from images. Exhibit 20.1-1 is a list of PIKS operators
categorized by functionality.
PIKS tools are elements that create data objects to be used by PIKS operators.
Exhibit 20.1-2 presents a list of PIKS tools functionally classified. PIKS utilities are
elements that perform basic mechanical image manipulation tasks. A classification
of PIKS utilities is shown in Exhibit 20.1-3. This list contains several file access and
display utilities that are defined in a proposed amendment to PIKS. PIKS mecha-
nisms are elements that perform control and management tasks. Exhibit 20.1-4 pro-
vides a functional listing of PIKS mechanisms. In Exhibits 20.1-1 to 20.1-4, the
elements in PIKS Core are identified by an asterisk.
EXHIBIT 20.1-1. PIKS Operators Classification
Analysis: image-to-non-image operators that extract numerical information from
an image
*Accumulator
Difference measures
*Extrema
*Histogram, one-dimensional
Histogram, two-dimensional
Hough transform
*Line profile
*Moments
*Value bounds
Classification: image-to-image operators that classify each pixel of a multispectral
image into one of a specified number of classes based on the ampli-
tudes of pixels across image bands
Classifier, Bayes
Classifier, nearest neighbour
Colour: image-to-image operators that convert a colour image from one colour
space to another
*Colour conversion, linear
*Colour conversion, nonlinear
*Colour conversion, subtractive
Colour lookup, interpolated
*Luminance generation
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Complex image: image-to-image operators that perform basic manipulations of
images in real and imaginary or magnitude and phase form
*Complex composition
*Complex conjugate
*Complex decomposition
*Complex magnitude
Correlation: image-to-non-image operators that compute a correlation array of a
pair of images
Cross-correlation
Template match
Edge detection: image-to-image operators that detect the edge boundary of objects
within an image
Edge detection, orthogonal gradient
Edge detection, second derivative
Edge detection, template gradient
Enhancement: image-to-image operators that improve the visual appearance of an
image or that convert an image to a form better suited for analysis by
a human or a machine
Adaptive histogram equalization
False colour
Histogram modification
Outlier removal
Pseudocolour
Unsharp mask
Wallis statistical differencing
Ensemble: image-to-image operators that perform arithmetic, extremal, and logical
combinations of pixels
*Alpha blend, constant
Alpha blend, variable
*Dyadic, arithmetic
*Dyadic, complex
*Dyadic, logical
*Dyadic, predicate
*Split image
Z merge
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Feature extraction: image-to-image operators that compute a set of image
features at each pixel of an image
Label objects
Laws texture features
Window statistics
Filtering: image-to-image operators that perform neighbourhood combinations of
pixels directly or by Fourier transform domain processing
Convolve, five-dimensional
*Convolve, two-dimensional
Filtering, homomorphic
*Filtering, linear
Geometric: image-to-image and ROI-to-ROI operators that perform geometric
modifications
Cartesian to polar
*Flip, spin, transpose
Polar to cartesian
*Rescale
*Resize
*Rotate
*Subsample
*Translate
Warp, control point
*Warp, lookup table
*Warp, polynomial
*Zoom
Histogram shape: non-image to non-image operators that generate shape measure-
ments of a pixel amplitude histogram of an image
Histogram shape, one-dimensional
Histogram shape, two-dimensional
Morphological: image-to-image operators that perform morphological operations
on boolean and grey scale images
*Erosion or dilation, Boolean
*Erosion or dilation, grey
*Fill region
Hit or miss transformation
*Morphic processor
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Morphology
Neighbour count
Open and close
Pixel modification: image-to-image operators that modify an image by pixel draw-
ing or painting
Draw pixels
Paint pixels
Point: image-to-image operators that perform point manipulation on a pixel-by-
pixel basis
*Bit shift
* Complement
Error function scaling
*Gamma correction
Histogram scaling
Level slice
*Lookup Lookup, interpolated
*Monadic, arithmetic
*Monadic, complex
*Monadic, logical
Noise combination
*Power law scaling
Rubber band scaling
*Threshold
*Unary, integer
*Unary, real
*Window-level
Presentation: image-to-image operators that prepare an image for display
*Diffuse
*Dither
Shape: Image-to-non-image operators that label objects and perform measurements
of the shape of objects within an image
Perimeter code generator
Shape metrics
Spatial moments, invariant
Spatial moments, scaled
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Unitary transform: image-to-image operators that perform multi-dimensional for-
ward and inverse unitary transforms of an image
Transform, cosine
*Transform, Fourier
Transform, Hadamard
Transform, Hartley
3D Specific: image-to-image operators that perform manipulations of three-dimen-
sional image data
Sequence average
Sequence Karhunen-Loeve transform
Sequence running measures
3D slice
EXHIBIT 20.1-2 PIKS Tools Classification
Image generation: Tools that create test images
Image, bar chart
*Image, constant
Image, Gaussian image
Image, grey scale image
Image, random number image
Impulse response function array generation: Tools that create impulse response
function neighbourhood array data objects
Impulse, boxcar
*Impulse, derivative of Gaussian
Impulse, difference of Gaussians
*Impulse, elliptical
*Impulse, Gaussian
*Impulse, Laplacian of Gaussian
Impulse, pyramid
*Impulse, rectangular
Impulse, sinc function
Lookup table generation: Tools that create entries of a lookup table data object
* Array to LUT
Matrix generation: tools that create matrix data objects
*Colour conversion matrix
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Region-of-interest generation: tools that create region-of-interest data objects from
a mathematical description of the region-of-interest
*ROI, coordinate
*ROI, elliptical
*ROI, polygon
*ROI, rectangular
Static array generation: tools that create filter transfer function, power spectrum,
and windowing function static array data objects
*Filter, Butterworth
*Filter, Gaussian
Filter, inverse
Filter, matched
Filter, Wiener
Filter, zonal
Markov process power spectrum
Windowing function
EXHIBIT 20.1-3. PIKS Utilities Classification
Display: utilities that perform image display functions
*Boolean display
*Close window
*Colour display
*Event display
*Monochrome display
*Open titled window
*Open window
*Pseudocolour display
Export From Piks: Utilities that export image and non-image data objects from
PIKS to an application or to the IIF or BIIF
*Export histogram
*Export image
*Export LUT
*Export matrix
*Export neighbourhood array
*Export ROI array
*Export static array
*Export tuple
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*Export value bounds
*Get colour pixel
*Get pixel
*Get pixel array
Get pixel record
*Output image file
Output object
Import to PIKS: utilities that import image and non-image data objects to PIKS
from an application or from the IIF or the BIIF
*Import histogram
*Import image
*Import LUT
*Import matrix
*Import neighbourhood array
*Import ROI array
*Import static array
*Import tuple
*Import value bounds
Input object
*Input image file
*Input PhotoCD
*Put colour pixel
*Put pixel
*Put pixel array
Put pixel record
Inquiry: utilities that return information to the application regarding PIKS data
objects, status and implementation
Inquire chain environment
Inquire chain status
*Inquire elements
*Inquire image
Inquire index assignment
*Inquire non-image object
*Inquire PIKS implementation
*Inquire PIKS status
*Inquire repository
*Inquire resampling
Internal: utilities that perform manipulation and conversion of PIKS internal image
and non-image data objects
*Constant predicate
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*Convert array to image
*Convert image data type
*Convert image to array
*Convert image to ROI
*Convert ROI to image
*Copy window
*Create tuple
*Equal predicate
*Extract pixel plane
*Insert pixel plane
EXHIBITS 20.1-4 PIKS Mechanisms Classification
Chaining: mechanisms that manage execution of PIKS elements inserted in chains
Chain abort
Chain begin
Chain delete
Chain end
Chain execute
Chain reload
Composite identifier management: mechanisms that perform manipulation of
image identifiers inserted in arrays, lists, and
records
Composite identifier array equal
Composite identifier array get
Composite identifier array put
Composite identifier list empty
Composite identifier list equal
Composite identifier list get
Composite identifier list insert
Composite identifier list remove
Composite identifier record equal
Composite identifier record get
Composite identifier record put
Control: mechanisms that control the basic operational functionality of PIKS
Abort asynchronous execution
*Close PIKS
*Close PIKS, emergency
*Open PIKS
Synchronize
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Error: mechanisms that provide means of reporting operational errors
*Error handler
*Error logger
*Error test
System management: mechanisms that allocate, deallocate, bind, and set attributes
of data objects and set global variables
Allocate chain
Allocate composite identifier array
Allocate composite identifier list
Allocate composite identifier record
*Allocate display image
*Allocate histogram
*Allocate image
*Allocate lookup table
*Allocate matrix
*Allocate neighbourhood array
Allocate pixel record
*Allocate ROI
*Allocate static array
*Allocate tuple
*Allocate value bounds collection
Allocate virtual register
Bind match point
*Bind ROI
*Deallocate data object
*Define sub image
*Return repository identifier
*Set globals
*Set image attributes
Set index assignment
Virtual register: mechanisms that manage the use of virtual registers
Vreg alter
Vreg clear
Vreg conditional
Vreg copy
Vreg create
Vreg delete
Vreg get
Vreg set
Vreg wait
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20.1.4. PIKS Operator Model
The PIKS operator model provides three possible transformations of PIKS data
objects by a PIKS operator:
1. Non-image to non-image
2. Image to non-image
3. Image to image
Figure 20.1-3 shows the PIKS operator model for the transformation of non-image
data objects to produce destination non-image data objects. An example of such a
transformation is the generation of shape features from an image histogram. The
operator model for the transformation of image data objects by an operator to pro-
duce non-image data objects is shown in Figure 20.1-4. An example of such a trans-
formation is the computation of the least-squares error between a pair of images. In
this operator model, processing is subject to two control mechanisms: region-of-
interest (ROI) source selection and source match point translation. These control
mechanisms are defined later. The dashed line in Figure 20.1-4 indicates the transfer
of control information. The dotted line indicates the binding of source ROI
objects to source image objects. Figure 20.1-5 shows the PIKS operator model for
FIGURE 20.1-3. PIKS operator model: non-image to non-image operators.
FIGURE 20.1-4. PIKS operator model: image to non-image operators.
Source
Non-image
Objects
Destination
Non-image
Objects
Operator
Source
Image
Objects
Source
ROI
Objects
ROI
Source
Selection
Source
Match
Point
Translation
Operator
Destination
Non-image
Objects
Source Match
Points
Tagged
Source
Images
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