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What is Image Processing?
In the future, the world will become full with robots which can do
works for the human kind without any fail. Robots will do things like human for
human. So they want human elements in it like eyes, ears, nose, hands etc. With
that they can work with efficiency and accuracy. Now let’s see how they work
with eyes and try to get information of near particle and object in front of
the camera or eye of the robot.
In camera hey catch the image of object and try to analysis the image
and get the information of it after getting information they do their task
related to image. This field of robotics called image processing in robotics or
image processing for the robots. We will see Image processing first then its
implementation in Robotics.
In electrical engineering and computer
science, image processing is any form of signal
processing for which the input is an image, such
as photographs or frames of video; the output of image
processing can be either an image or a set of characteristics
or parameters related to the image. Most image-processing techniques
involve treating the image as a two-dimensional signal and
applying standard signal-processing techniques to it. Image processing usually
refers to digital image processing, but optical and analog
image processing are also possible.
In the field of industrial robotics, the interaction between man and
machine typically consists of programming and maintaining the machine by the
human operator. For safety reasons, a direct contact between the working robot
and the human has to be prevented. As long as the robots act out pre-programmed
behaviours only, a direct interaction between man and machine is not necessary
anyway.
However, if the robot is to assist a human e.g. in a complex assembly
task, it is necessary to have means of exchanging information about the current
scenario between man and machine in real time. For this purpose, the classical
computer devices like keyboard, mouse and monitor are not the best choice as
they require an encoding and decoding of information: if, for instance, the
human operator wants the robot to grasp an object. This way of transmitting
information to the machine is not only unnatural but also error prone.
If the robot is equipped with a camera system, it would be much more
intuitive to just point to the object to grasp and let the robot detect its
position visually.
Introduction
Modern digital technology has made it possible to manipulate
multi-dimensional signals with systems that range from simple digital circuits
to advanced parallel computers. The goal of this manipulation can be divided
into three categories:
- Image Processing image in image out
- Image Analysis image in measurements out
- Image Understanding image in high-level description out
We will focus on the fundamental concepts of image processing. Space
does not permit us to make more than a few introductory remarks about image
analysis. Image understanding requires an approach that differs fundamentally
from the theme of this book. Further, we will restrict ourselves to
two–dimensional (2D) image processing although most of the concepts and
techniques that are to be described can be extended easily to three or more
dimensions.
We begin with certain basic definitions. An image defined in the “real
world” is considered to be a function of two real variables, for example,
a(x,y) with a as the amplitude (e.g. brightness) of the image at the real
coordinate position (x,y). An image may be considered to contain sub-images
sometimes referred to as regions–of–interest, ROIs, or simply regions.
This concept reflects the fact that images frequently contain
collections of objects each of which can be the basis for a region. In a
sophisticated image processing system it should be possible to apply
specific image processing operations to selected regions. Thus one part
of an image (region) might be processed to suppress motion blur while another
part might be processed to improve color rendition.
The amplitudes of a given image will almost always be either real
numbers or integer numbers. The latter is usually a result of a quantization
process that converts a continuous range (say, between 0 and 100%) to a
discrete number of levels. In certain image-forming processes, however, the
signal may involve photon counting which implies that the amplitude would be
inherently quantized. In other image forming procedures, such as magnetic
resonance imaging, the direct physical measurement yields a complex number in
the form of a real magnitude and a real phase. For the remainder of this book
we will consider amplitudes as reals or integers unless otherwise indicated.
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