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.
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.