pixel

Intro

Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultrasound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications.

Why images?

Images are the most common and convenient means of conveying or

transmitting information. An image is worth a thousand words. Images concisely

convey information about positions, sizes and inter-relationships between

objects. Human beings are good at deriving information from such images,

because of our innate visual and mental abilities. About 75% of the information

received by human is in pictorial form.

Human visual system (HVS) receives an input image as a collection of

spatially distributed light energy; this is form is called an optical image. Optical

images are the type we deal with every day, cameras captures them, monitors

display them, and we see them. These optical images are represented as video

information in the form of analog electrical signals then they are sampled to

generate the digital image.

What is the Digital Image?

A digital image a[m,n] described in a 2D

discrete space is derived from an analog image

a(x,y) in a 2D continuous space through a sampling process that is

frequently referred to as digitization.

Binary Image

Gray Scale Image

The 2D continuous image a(x,y) is divided into N rows and M columns. The

intersection of a row and a column is termed a pixel. Every pixel has

intensity .The value assigned to the integer coordinates [m,n] is a[m,n]. In fact,

in most cases a(x,y) is actually a function of many variables including depth (z),

color (λ), and time (t).

What are the different types of images?

1- Binary Images:

Binary images are the simplest type of images and can take on two values,

typically black and white, or ‘0’ and ‘1’. A binary image is referred to as a

1 bit/pixel image because it takes only 1 binary digit to represent each pixel.

These types of images are most frequently in computer vision application where

the only information required for the task is general shapes, or outlines

information. For example, to position a robotics gripper to grasp an object or in

optical character recognition (OCR).

Binary images are often created from gray-scale images via a threshold value

is, those values above it are turned white (‘1’), and those below it are turned

black (‘0’).

2- Gray Scale Image:

Gray Scale Images are referred to as monochrome, or one-color image. They

contain brightness information only , no color information. The typical image

contains 8 bit/ pixel (data, which allows us to have (0-255) different brightness

(gray) levels). The 8 bit representation is typically due to the fact that the byte,

which corresponds to 8-bit of data, is the standard small unit in the world of

digital computer.

Color Image

Multispectral Image

Raster vs. Vector

3- Color Image:

Color image can be modeled as three band monochrome image data, where

each band of the data corresponds to a different color. Typical color images are

represented as red, green , and blue or RGB images .using the 8-bit monochrome

standard as a model , the corresponding color image would have 24 bit/pixel – 8

bit for each color bands (red, green and blue ). The following figure we see a

representation of a typical RGB color image.

4- Multispectral Image:

A multispectral image is one that captures image data at specific frequencies

across the electromagnetic spectrum. Multispectral images typically contain

information outside the normal human perceptual range. This may include

infrared, ultraviolet, X-ray, acoustic or radar data. Source of these types of

images include satellite systems, underwater sonar systems and medical

diagnostics imaging systems.

In computer graphics, types of image data are divided into two primarily

categories:

1. Bitmap image (or raster image). can represented by our image model a(x,y).

2. Vector images.

(Raster graphics are composed of pixels, and it is an array of pixels of various

colors while vector graphics are composed of paths , and it is made of

mathematical calculations that form objects and lines.).

Note: Because vector graphics are not made of pixels, the images can be scaled

to be very large without losing quality. Raster graphics, on the other hand,

become "blocky," since each pixel increases in size as the image is made larger.

What is the Digital Image Processing (DIP)?

Image processing refers to the procedure of manipulating images. Commonly image processing is carried out in digital domain by means of computers. Digital image processing covers a wide range of different techniques to change the properties or appearance or to extract some information from an image.

Digital image processing systems are using at many application principally automatic control, robots …..

Why we need image processing?

Computer Imaging Systems

Computer imaging systems are comprised of two primary components

types, hardware and software. The hardware components can be divided

into image acquiring system (computer, scanner, and camera) and

display devices (monitor, printer).The software allows us to manipulate the

image and perform any desired processing on the image data.

Purpose of Image processing:

The purpose of image processing is divided into 5 groups. They are:

1. Visualization - Observe the objects that are not visible.

2. Image sharpening and restoration - To create a better image.

3. Image retrieval - Seek for the image of interest.

4. Measurement of pattern – Measures various objects in an image.

5. Image Recognition – Distinguish the objects in an image.

How can get an image? “ Image Acquisition “

The physical device that is sensitive to the energy radiated by the object we wish to image (Sensor).

Today, most digital still cameras use either a CCD image sensor or a CMOS sensor. Both types of sensor accomplish the same task of capturing light and converting it into electrical signals.

The image that we get it is a physical image, How can we convert it into a digital image? Let’s see….

There are numerous ways to acquire images, but our objective in all is the same: to generate digital images from sensed data. The output of most sensors is a continuous voltage waveform whose amplitude and spatial behavior are related to the physical phenomenon being sensed. To create a digital image, we need to convert the continuous sensed data into digital form. This involves two processes: sampling and quantization.

Digital Image Processing and Other Fields:

There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vision, start. Sometimes a distinction is made by defining image processing as a

discipline in which both the input and output of a process are images . We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision , including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision. There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high-level processes. Low-level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both

its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A mid-level process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher-level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.

Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book(Digital Image Processing, Rafael C. Gonzalez) digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects.

The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense”.

Low Level Process

Input: ImageOutput: Image

Examples: Noise removal, image sharpening

Mid Level Process

Input: Image Output: Attributes

Examples: Object recognition, segmentation

High Level Process

Input: Attributes Output: Understanding

Examples: Scene understanding, autonomous navigation