What is the function of

Τεχνίτη Νοημοσύνη και Ρομποτική

5 Νοε 2013 (πριν από 4 χρόνια και 8 μήνες)

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What is the function of
Image Processing?

In high resolution field, in addition to the usual
preprocessing functions (offset, dark and flat
corrections), the usefulness of image processing
can be divided into two main functions:
increasing the contrast of planetary details and
reducing the noise.

Increasing the contrast of planetary
detail

Increasing the contrast of small details is
the aim of many processing algorithms
which all act in the same way: they
amplify the high frequencies in the image.
This is the reason why they are called
high
-
pass filters
, and probably the most
famous of them is
. This
technique is well
-
known but hard to use in
astrophotography. In digital image
processing the general principle of

What is a MTF curve ?
):

a fuzzy image (blue curve) is made
from the initial image (red curve) by
application of a low
-
pass filter
(gaussian) whose strenght is
suppressed,

this fuzzy image is substracted from
the initial image; the result (green
curve) contains only the small details
(high frequencies) but its appearance
is very strange and unaesthetic
(unfortunately, this image also
contains noise),

MTF Curve

What is Sampling?

Sampling

is choosing which points you
want to have represent a given image.
Given an analog image, sampling
represents a mapping of the image from a
continuum of points in space (and possibly
time, if it is a moving image) to a discrete
set. Given a digital image, sampling
represents a mapping from one discrete
set of points to another (smaller) set.

Original Picture

Manroc Sampled

LINEAR FILTERING

Low pass filters

Low pass filtering, otherwise known as
"smoothing", is employed to remove high
spatial frequency

noise from a digital
image. Noise is often introduced during
the analog
-
to
-
digital conversion process
as a side
-
effect of the physical conversion
of patterns of light energy into electrical
patterns

There are several common
approaches to removing this noise:

If several copies of an image have
been obtained from the source, some
static image, then it may be possible
to sum the values for each pixel from
each image and compute an
average. This is not possible,
however, if the image is from a
moving source or there are other
time or size restrictions.

Bone Marrow Image

If such averaging is not possible, or if it is
insufficient, some form of
low pass
spatial filtering

may be required. There
are two main types:

reconstruction filtering
, where an
image is restored based on some
knowledge of of the type of degradation it
has undergone. Filters that do this are
often called "optimal filters"

enhancement filtering
, which
attempts to improve the
(subjectively measured) quality of an
image for human or machine
interpretability. Enhancement filters
are generally heuristic and problem
oriented

Moving window operations

The form that low
-
pass filters usually
take is as some sort of
moving
window operator
. The operator
usually affects one pixel of the image
at a time, changing its value by
some function of a "local" region of
pixels ("covered" by the window).
The operator "moves" over the
image to affect all the pixels in the
image.

Some common types are:

Neighborhood
-
averaging filters

Median filters

Mode filters

Neighborhood
-
averaging filters

These replace the value of each
pixel, by a weighted
-
average of the
pixels in some neighborhood around
it, i.e. a weighted sum of the weights
are non
-
negative. If all the weights
are equal then this is a
mean

filter.
"linear"

Median filters

This replaces each pixel value by the
median of its neighbors, i.e. the
value such that 50% of the values in
the neighborhood are above, and
50% are below. This can be difficult
and costly to implement due to the
need for sorting of the values.
However, this method is generally
very good at preserving edges.

Mode filters

Each pixel value is replaced by its
most common neighbor. This is a
particularly useful filter for
classification

procedures where each
pixel corresponds to an object which
must be placed into a class; in
remote sensing, for example, each
class could be some type of terrain,
crop type, water, etc..

These are all
space invariant

in that
the same operation is applied to each
pixel location.

A non
-
space invariant filtering, using
the above filters, can be obtained by
changing the type of filter or the
weightings used for the pixels for
different parts of the image.

Non
-
linear

filters also exist which
are not space invariant; these
attempt to locate edges in the noisy
image before applying smoothing, a
difficult task at best, in order to
reduce the blurring of edges due to
smoothing.

High Pass Filter

A high pass filter is used in digital image
processing to remove or suppress the low
frequency component, resulting in a
sharpened image. High pass filters are
often used in conjunction with low pass
filters. For example, the image may be
smoothed using a low pass filter, then a
high pass filter can be applied to sharpen
the image, therefore preserving boundary
detail.

What Is An Edge?

An edge may be regarded as a
boundary between two dissimilar
regions in an image.

These may be different surfaces of
the object, or perhaps a boundary
between light and shadow falling on
a single surface.

edges have been loosely defined as pixel
intensity discontinuities within an
image.

While two experimenters
processing the same image for the same
purpose may not see the same edge pixels
in the image, two for different applications
may never agree.

In a word, edge detection is usually a

In principle an edge is easy to find
since differences in pixel values
between regions are relatively easy

Many edge extraction techniques
can be broken up into two distinct
phases:

Finding pixels in the image where
edges are likely to occur by looking

Candidate points for edges in the
image are usually referred to as
edge points
,
edge pixels
, or
edgels
.

Linking these edge points in some
way to produce descriptions of edges
in terms of lines, curves
etc.

An edge point can be regarded as a
point in an image where a
across some line. A discontinuity
may be classified as one of three
types

Types of Edges

--

where the gradient of the pixel
values changes across a line. This
type of discontinuity can be classed
as

roof

edges

ramp

edges

convex

edges

concave

edges

--
by noting the sign of the component
of the gradient perpendicular to the
edge on either side of the edge.

Ramp edges have the same signs in
side of the discontinuity, while roof
edges have opposite signs in the

A Jump or Step Discontinuity

--

where pixel values themselves
change suddenly across some line.

A Bar Discontinuity

--

where pixel values rapidly increase
then decrease again (or
vice versa
)
across some line.

For example, if the pixel values are
depth values,

jump discontinuities occur where one
object occludes another (or another
part of itself).

between adjacent faces of the same
object.

If the pixel values are intensities,

a bar discontinuity would represent
cases like a thin black line on a white
piece of paper.

Step edges may separate different
objects, or may occur where a

second order derivatives.

Since First derivative operators
exaggerate the effects of noise,
Second derivatives exaggerate noise
twice as much.

edge is given.

Edge detectors yield pixels in an
image lie on edges.

Next collect these pixels together
into a set of edges.

Replace many points on edges with a
few edges themselves.

Problems…

Small pieces of edges may be
missing,

Small edge segments may appear to
be present due to noise where there
is no real edge,
etc
.

--

where edge points are grouped to
form edges by considering each
point's relationship to any
neighbouring edge points.

--

where all edge points in the image
plane are considered at the same
time and sets of edge points are
sought according to some similarity
constraint, such as points which
share the same edge equation.

Most edge detectors yield
the gradient at an edge point and,
more importantly, the direction of
the edge in the locality of the point.

Texture Analysis

In many machine vision and image
processing algorithms, simplifying
uniformity of intensities in local
image regions. However, images of
real objects often do not exhibit
regions of uniform intensities.

Image texture, defined as a function
of the spatial variation in pixel
intensities (gray values), is useful in
a variety of applications and has
been a subject of intense study by
many researchers. One immediate
application of image texture is the
recognition of image regions using
texture properties.

Texture Segmentation

Texture boundaries can be found even
if the texture surfaces cannot be
classified. The goal of texture
segmentation is to obtain the
boundary map separating the
differently textured regions in an
image.

Texture Synthesis

Texture synthesis

is often used for
image compression applications. It is
also important in computer graphics
where the goal is to render object
surfaces which are as realistic
looking as possible.

Shape From Texture

The
shape from texture

problem is
one instance of a general class of
vision problems known as ``shape
from X.'' The goal is to extract three
-
dimensional surface shape from
variations in textural properties in
the image. The texture features are
distorted due to the imaging process
and the perspective projection which