04-08-2012, 11:06 AM
Digital Image Processing with Matlab
2Digital Image.pdf (Size: 4 MB / Downloads: 101)
Introduction
Images and pictures
As we mentioned in the preface, human beings are predominantly visual creatures: we rely heavily
on our vision to make sense of the world around us. We not only look at things to identify and
classify them, but we can scan for dierences, and obtain an overall rough feeling for a scene with
a quick glance.
Humans have evolved very precise visual skills: we can identify a face in an instant; we can
dierentiate colours; we can process a large amount of visual information very quickly.
However, the world is in constant motion: stare at something for long enough and it will change
in some way. Even a large solid structure, like a building or a mountain, will change its appearance
depending on the time of day (day or night); amount of sunlight (clear or cloudy), or various shadows
falling upon it.
We are concerned with single images: snapshots, if you like, of a visual scene. Although image
processing can deal with changing scenes, we shall not discuss it in any detail in this text.
For our purposes, an image is a single picture which represents something. It may be a picture
of a person, of people or animals, or of an outdoor scene, or a microphotograph of an electronic
component, or the result of medical imaging. Even if the picture is not immediately recognizable,
it will not be just a random blur.
What is image processing?
Image processing involves changing the nature of an image in order to either
1. improve its pictorial information for human interpretation,
2. render it more suitable for autonomous machine perception.
We shall be concerned with digital image processing, which involves using a computer to change the
nature of a digital image (see below). It is necessary to realize that these two aspects represent two
separate but equally important aspects of image processing. A procedure which satises condition
(1)a procedure which makes an image look bettermay be the very worst procedure for satisfying
condition (2). Humans like their images to be sharp, clear and detailed; machines prefer their
images to be simple and uncluttered.
Examples of (1) may include
Images and digital images
Suppose we take an image, a photo, say. For the moment, lets make things easy and suppose the
photo is black and white (that is, lots of shades of grey), so no colour. We may consider this image
as being a two dimensional function, where the function values give the brightness of the image at
any given point, as shown in gure 1.6. Usually
they take on only integer values, so the image shown in gure 1.6 will have and ranging from 1
to 256 each, and the brightness values also ranging from 0 (black) to 255 (white). A digital image
can be considered as a large array of discrete dots, each of which has a brightness associated with
it. These dots are called picture elements, or more simply pixels. The pixels surrounding a given
pixel constitute its neighbourhood. A neighbourhood can be characterized by its shape in the same
way as a matrix: we can speak of a neighbourhood, or of a neighbourhood. Except in
very special circumstances, neighbourhoods have odd numbers of rows and columns; this ensures
that the current pixel is in the centre of the neighbourhood.
2Digital Image.pdf (Size: 4 MB / Downloads: 101)
Introduction
Images and pictures
As we mentioned in the preface, human beings are predominantly visual creatures: we rely heavily
on our vision to make sense of the world around us. We not only look at things to identify and
classify them, but we can scan for dierences, and obtain an overall rough feeling for a scene with
a quick glance.
Humans have evolved very precise visual skills: we can identify a face in an instant; we can
dierentiate colours; we can process a large amount of visual information very quickly.
However, the world is in constant motion: stare at something for long enough and it will change
in some way. Even a large solid structure, like a building or a mountain, will change its appearance
depending on the time of day (day or night); amount of sunlight (clear or cloudy), or various shadows
falling upon it.
We are concerned with single images: snapshots, if you like, of a visual scene. Although image
processing can deal with changing scenes, we shall not discuss it in any detail in this text.
For our purposes, an image is a single picture which represents something. It may be a picture
of a person, of people or animals, or of an outdoor scene, or a microphotograph of an electronic
component, or the result of medical imaging. Even if the picture is not immediately recognizable,
it will not be just a random blur.
What is image processing?
Image processing involves changing the nature of an image in order to either
1. improve its pictorial information for human interpretation,
2. render it more suitable for autonomous machine perception.
We shall be concerned with digital image processing, which involves using a computer to change the
nature of a digital image (see below). It is necessary to realize that these two aspects represent two
separate but equally important aspects of image processing. A procedure which satises condition
(1)a procedure which makes an image look bettermay be the very worst procedure for satisfying
condition (2). Humans like their images to be sharp, clear and detailed; machines prefer their
images to be simple and uncluttered.
Examples of (1) may include
Images and digital images
Suppose we take an image, a photo, say. For the moment, lets make things easy and suppose the
photo is black and white (that is, lots of shades of grey), so no colour. We may consider this image
as being a two dimensional function, where the function values give the brightness of the image at
any given point, as shown in gure 1.6. Usually
they take on only integer values, so the image shown in gure 1.6 will have and ranging from 1
to 256 each, and the brightness values also ranging from 0 (black) to 255 (white). A digital image
can be considered as a large array of discrete dots, each of which has a brightness associated with
it. These dots are called picture elements, or more simply pixels. The pixels surrounding a given
pixel constitute its neighbourhood. A neighbourhood can be characterized by its shape in the same
way as a matrix: we can speak of a neighbourhood, or of a neighbourhood. Except in
very special circumstances, neighbourhoods have odd numbers of rows and columns; this ensures
that the current pixel is in the centre of the neighbourhood.