6. Explain about image sampling and quantization process.

6. Explain about image sampling and quantization process.

   March 11, 2013       legend

---

>Table of contents


Image Sampling and Quantization:

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.

Basic Concepts in Sampling and Quantization:

The basic idea behind sampling and quantization is illustrated in Fig.6.1. Figure 6.1(a) shows a
continuous image, f(x, y), that we want to convert to digital form. An image may be continuous
with respect to the x- and y-coordinates, and also in amplitude. To convert it to digital form, we
have to sample the function in both coordinates and in amplitude. Digitizing the coordinate
values is called sampling. Digitizing the amplitude values is called quantization.

The one-dimensional function shown in Fig.6.1 (b) is a plot of amplitude (gray level) values of
the continuous image along the line segment AB in Fig. 6.1(a).The random variations are due to
image noise. To sample this function, we take equally spaced samples along line AB, as shown
in Fig.6.1 (c).The location of each sample is given by a vertical tick mark in the bottom part of
the figure. The samples are shown as small white squares superimposed on the function. The set
of these discrete locations gives the sampled function. However, the values of the samples still
span (vertically) a continuous range of gray-level values. In order to form a digital function, the
gray-level values also must be converted (quantized) into discrete quantities. The right side of
Fig. 6.1 (c) shows the gray-level scale divided into eight discrete levels, ranging from black to
white. The vertical tick marks indicate the specific value assigned to each of the eight gray
levels. The continuous gray levels are quantized simply by assigning one of the eight discrete
gray levels to each sample. The assignment is made depending on the vertical proximity of a
sample to a vertical tick mark. The digital samples resulting from both sampling and quantization
are shown in Fig.6.1 (d). Starting at the top of the image and carrying out this procedure line by
line produces a two-dimensional digital image.

Sampling in the manner just described assumes that we have a continuous image in both
coordinate directions as well as in amplitude. In practice, the method of sampling is determined
by the sensor arrangement used to generate the image. When an image is generated by a single sensing element combined with mechanical motion, as in Fig. 2.13, the output of the sensor is
quantized in the manner described above. However, sampling is accomplished by selecting the
number of individual mechanical increments at which we activate the sensor to collect data.
Mechanical motion can be made very exact so, in principle; there is almost no limit as to how
fine we can sample an image. However, practical limits are established by imperfections in the
optics used to focus on the sensor an illumination spot that is inconsistent with the fine resolution achievable with mechanical displacements. When a sensing strip is used for image acquisition, the number of sensors in the strip establishes the sampling limitations in one image direction. Mechanical
motion in the other direction can be controlled more accurately, but it makes little sense to try to
achieve sampling density in one direction that exceeds the sampling limits established by the
number of sensors in the other. Quantization of the sensor outputs completes the process of
generating a digital image.

Fig.6.1. Generating a digital image (a) Continuous image (b) A scan line from A to Bin the
continuous image, used to illustrate the concepts of sampling and quantization (c) Sampling
and quantization. (d) Digital scan line

When a sensing array is used for image acquisition, there is no motion and the number of sensors
in the array establishes the limits of sampling in both directions. Figure 6.2 illustrates this
concept. Figure 6.2 (a) shows a continuous image projected onto the plane of an array sensor.
Figure 6.2 (b) shows the image after sampling and quantization. Clearly, the quality of a digital
image is determined to a large degree by the number of samples and discrete gray levels used in
sampling and quantization.

Fig.6.2. (a) Continuos image projected onto a sensor array (b) Result of image
sampling and quantization.

>Table of contents
 

Read More

5. Explain the process of image acquisition.

5. Explain the process of image acquisition.

   March 05, 2013       legend

---

>Table of contents

Image Sensing and Acquisition:

The types of images in which we are interested are generated by the combination of an
“illumination” source and the reflection or absorption of energy from that source by the elements
of the “scene” being imaged. We enclose illumination and scene in quotes to emphasize the fact
that they are considerably more general than the familiar situation in which a visible light source
illuminates a common everyday 3-D (three-dimensional) scene. For example, the illumination
may originate from a source of electromagnetic energy such as radar, infrared, or X-ray energy.
But, as noted earlier, it could originate from less traditional sources, such as ultrasound or even a
computer-generated illumination pattern.

Similarly, the scene elements could be familiar objects, but they can just as easily be molecules,
buried rock formations, or a human brain. We could even image a source, such as acquiring
images of the sun. Depending on the nature of the source, illumination energy is reflected from,
or transmitted through, objects. An example in the first category is light reflected from a planar
surface. An example in the second category is when X-rays pass through a patient’s body for the
purpose of generating a diagnostic X-ray film. In some applications, the reflected or transmitted
energy is focused onto a photo converter (e.g., a phosphor screen), which converts the energy
into visible light. Electron microscopy and some applications of gamma imaging use this
approach.

Figure 5.1 shows the three principal sensor arrangements used to transform illumination energy
into digital images. The idea is simple: Incoming energy is transformed into a voltage by the
combination of input electrical power and sensor material that is responsive to the particular type
of energy being detected. The output voltage waveform is the response of the sensor(s), and a
digital quantity is obtained from each sensor by digitizing its response.

 

Fig.5.1 (a) Single imaging Sensor (b) Line sensor (c) Array sensor

 (1)Image Acquisition Using a Single Sensor:

Figure 5.1 (a) shows the components of a single sensor. Perhaps the most familiar sensor of this

type is the photodiode, which is constructed of silicon materials and whose output voltage
waveform is proportional to light. The use of a filter in front of a sensor improves selectivity. For
example, a green (pass) filter in front of a light sensor favors light in the green band of the color spectrum. As a consequence, the sensor output will be stronger for green light than for other
components in the visible spectrum

In order to generate a 2-D image using a single sensor, there has to be relative displacements in

both the x- and y-directions between the sensor and the area to be imaged. Figure 5.2 shows an
arrangement used in high-precision scanning, where a film negative is mounted onto a drum
whose mechanical rotation provides displacement in one dimension. The single sensor is
mounted on a lead screw that provides motion in the perpendicular direction. Since mechanical
motion can be controlled with high precision, this method is an inexpensive (but slow) way to
obtain high-resolution images. Other similar mechanical arrangements use a flat bed, with the
sensor moving in two linear directions. These types of mechanical digitizers sometimes are
referred to as microdensitometers.

 Fig.5.2. Combining a single sensor with motion to generate a 2-D image

 (2) Image Acquisition Using Sensor Strips:

 A geometry that is used much more frequently than single sensors consists of an in-line

arrangement of sensors in the form of a sensor strip, as Fig. 5.1 (b) shows. The strip provides
imaging elements in one direction. Motion perpendicular to the strip provides imaging in the
other direction, as shown in Fig. 5.3 (a).This is the type of arrangement used in most flat bed
scanners. Sensing devices with 4000 or more in-line sensors are possible. In-line sensors are used routinely in airborne imaging applications, in which the imaging system is mounted on an
aircraft that flies at a constant altitude and speed over the geographical area to be imaged. Onedimensional imaging sensor strips that respond to various bands of the electromagnetic spectrum are mounted perpendicular to the direction of flight. The imaging strip gives one line of an image

 at a time, and the motion of the strip completes the other dimension of a two-dimensional image.

Lenses or other focusing schemes are used to project the area to be scanned onto the sensors.

 Sensor strips mounted in a ring configuration are used in medical and industrial imaging to

obtain cross-sectional (“slice”) images of 3-D objects, as Fig. 5.3 (b) shows. A rotating X-ray
source provides illumination and the portion of the sensors opposite the source collect the X-ray
energy that pass through the object (the sensors obviously have to be sensitive to X-ray
energy).This is the basis for medical and industrial computerized axial tomography (CAT). It is
important to note that the output of the sensors must be processed by reconstruction algorithms
whose objective is to transform the sensed data into meaningful cross-sectional images.

 In other words, images are not obtained directly from the sensors by motion alone; they require

extensive processing. A 3-D digital volume consisting of stacked images is generated as the
object is moved in a direction perpendicular to the sensor ring. Other modalities of imaging
based on the CAT principle include magnetic resonance imaging (MRI) and positron emission
tomography (PET).The illumination sources, sensors, and types of images are different, but
conceptually they are very similar to the basic imaging approach shown in Fig. 5.3 (b).

 Fig.5.3 (a) Image acquisition using a linear sensor strip (b) Image acquisition using a

circular sensor strip.

(3) Image Acquisition Using Sensor Arrays:

Figure 5.1 (c) shows individual sensors arranged in the form of a 2-D array. Numerous
electromagnetic and some ultrasonic sensing devices frequently are arranged in an array format.
This is also the predominant arrangement found in digital cameras. A typical sensor for these
cameras is a CCD array, which can be manufactured with a broad range of sensing properties
and can be packaged in rugged arrays of 4000 * 4000 elements or more. CCD sensors are used
widely in digital cameras and other light sensing instruments. The response of each sensor is
proportional to the integral of the light energy projected onto the surface of the sensor, a property
that is used in astronomical and other applications requiring low noise images. Noise reduction is
achieved by letting the sensor integrate the input light signal over minutes or even hours. Since
the sensor array shown in Fig. 5.4 (c) is two dimensional, its key advantage is that a complete
image can be obtained by focusing the energy pattern onto the surface of the array. The principal
manner in which array sensors are used is shown in Fig.5.4. This figure shows the energy from
an illumination source being reflected from a scene element, but, as mentioned at the beginning
of this section, the energy also could be transmitted through the scene elements. The first
function performed by the imaging system shown in Fig.5.4 (c) is to collect the incoming energy
and focus it onto an image plane. If the illumination is light, the front end of the imaging system
is a lens, which projects the viewed scene onto the lens focal plane, as Fig. 2.15(d) shows. The
sensor array, which is coincident with the focal plane, produces outputs proportional to the
integral of the light received at each sensor. Digital and analog circuitry sweep these outputs and
converts them to a video signal, which is then digitized by another section of the imaging system.
The output is a digital image, as shown diagrammatically in Fig. 5.4 (e).

Fig.5.4 An example of the digital image acquisition process (a) Energy (“illumination”)
source (b) An element of a scene (c) Imaging system (d) Projection of the scene onto the
image plane (e) Digitized image

>Table of contents

Read More

4. Explain about elements of visual perception.

4. Explain about elements of visual perception.

   March 04, 2013       legend

---

>Table of contents

Elements of Visual Perception:

Although the digital image processing field is built on a foundation of mathematical and
probabilistic formulations, human intuition and analysis play a central role in the choice of one
technique versus another, and this choice often is made based on subjective, visual judgments.

(1) Structure of the Human Eye:

Figure 4.1 shows a simplified horizontal cross section of the human eye. The eye is nearly a
sphere, with an average diameter of approximately 20 mm. Three membranes enclose the eye:
the cornea and sclera outer cover; the choroid; and the retina. The cornea is a tough, transparent
tissue that covers the anterior surface of the eye. Continuous with the cornea, the sclera is an
opaque membrane that encloses the remainder of the optic globe. The choroid lies directly below
the sclera. This membrane contains a network of blood vessels that serve as the major source of
nutrition to the eye. Even superficial injury to the choroid, often not deemed serious, can lead to
severe eye damage as a result of inflammation that restricts blood flow. The choroid coat is
heavily pigmented and hence helps to reduce the amount of extraneous light entering the eye and
the backscatter within the optical globe. At its anterior extreme, the choroid is divided into the
ciliary body and the iris diaphragm. The latter contracts or expands to control the amount of light
that enters the eye. The central opening of the iris (the pupil) varies in diameter from
approximately 2 to 8 mm. The front of the iris contains the visible pigment of the eye, whereas
the back contains a black pigment.

The lens is made up of concentric layers of fibrous cells and is suspended by fibers that attach to
the ciliary body. It contains 60 to 70%water, about 6%fat, and more protein than any other tissue
in the eye. The lens is colored by a slightly yellow pigmentation that increases with age. In
extreme cases, excessive clouding of the lens, caused by the affliction commonly referred to as
cataracts, can lead to poor color discrimination and loss of clear vision. The lens absorbs
approximately 8% of the visible light spectrum, with relatively higher absorption at shorter
wavelengths. Both infrared and ultraviolet light are absorbed appreciably by proteins within the
lens structure and, in excessive amounts, can damage the eye.

                             Fig.4.1 Simplified diagram of a cross section of the human eye.

The innermost membrane of the eye is the retina, which lines the inside of the wall’s entire
posterior portion. When the eye is properly focused, light from an object outside the eye is
imaged on the retina. Pattern vision is afforded by the distribution of discrete light receptors over
the surface of the retina. There are two classes of receptors: cones and rods. The cones in each
eye number between 6 and 7 million. They are located primarily in the central portion of the retina, called the fovea, and are highly sensitive to color. Humans can resolve fine details with
these cones largely because each one is connected to its own nerve end. Muscles controlling the
eye rotate the eyeball until the image of an object of interest falls on the fovea. Cone vision is
called photopic or bright-light vision. The number of rods is much larger: Some 75 to 150
million are distributed over the retinal surface. The larger area of distribution and the fact that
several rods are connected to a single nerve end reduce the amount of detail discernible by these
receptors. Rods serve to give a general, overall picture of the field of view. They are not involved
in color vision and are sensitive to low levels of illumination. For example, objects that appear
brightly colored in daylight when seen by moonlight appear as colorless forms because only the
rods are stimulated. This phenomenon is known as scotopic or dim-light vision.

(2) Image Formation in the Eye:

The principal difference between the lens of the eye and an ordinary optical lens is that the
former is flexible. As illustrated in Fig. 4.1, the radius of curvature of the anterior surface of the
lens is greater than the radius of its posterior surface. The shape of the lens is controlled by
tension in the fibers of the ciliary body. To focus on distant objects, the controlling muscles
cause the lens to be relatively flattened. Similarly, these muscles allow the lens to become
thicker in order to focus on objects near the eye. The distance between the center of the lens and
the retina (called the focal length) varies from approximately 17 mm to about 14 mm, as the
refractive power of the lens increases from its minimum to its maximum. When the eye


Fig.4.2. Graphical representation of the eye looking at a palm tree Point C is the optical
center of the lens.

focuses on an object farther away than about 3 m, the lens exhibits its lowest refractive power.
When the eye focuses on a nearby object, the lens is most strongly refractive. This information
makes it easy to calculate the size of the retinal image of any object. In Fig. 4.2, for example, the
observer is looking at a tree 15 m high at a distance of 100 m. If h is the height in mm of that
object in the retinal image, the geometry of Fig.4.2 yields 15/100=h/17 or h=2.55mm. The retinal
image is reflected primarily in the area of the fovea. Perception then takes place by the relative
excitation of light receptors, which transform radiant energy into electrical impulses that are
ultimately decoded by the brain.

(3)Brightness Adaptation and Discrimination:

Because digital images are displayed as a discrete set of intensities, the eye’s ability to
discriminate between different intensity levels is an important consideration in presenting imageprocessing results. The range of light intensity levels to which the human visual system can adapt is enormous—on the order of 1010—from the scotopic threshold to the glare limit. Experimental evidence indicates that subjective brightness (intensity as perceived by the human visual system) is a logarithmic function of the light intensity incident on the eye. Figure 4.3, a plot of light intensity versus subjective brightness, illustrates this characteristic. The long solid curve
represents the range of intensities to which the visual system can adapt. In photopic vision alone,
the range is about 106. The transition from scotopic to photopic vision is gradual over the
approximate range from 0.001 to 0.1 millilambert (–3 to –1 mL in the log scale), as the double
branches of the adaptation curve in this range show.

Fig.4.3. Range of Subjective brightness sensations showing a particular adaptation level.

The essential point in interpreting the impressive dynamic range depicted in Fig.4.3 is that the
visual system cannot operate over such a range simultaneously. Rather, it accomplishes this large
variation by changes in its overall sensitivity, a phenomenon known as brightness adaptation.
The total range of distinct intensity levels it can discriminate simultaneously is rather small when
compared with the total adaptation range. For any given set of conditions, the current sensitivity
level of the visual system is called the brightness adaptation level, which may correspond, for
example, to brightness Ba in Fig. 4.3. The short intersecting curve represents the range of
subjective brightness that the eye can perceive when adapted to this level. This range is rather
restricted, having a level Bb at and below which all stimuli are perceived as indistinguishable
blacks. The upper (dashed) portion of the curve is not actually restricted but, if extended too far,
loses its meaning because much higher intensities would simply raise the adaptation level higher
than Ba.

>Table of contents
 

Read More

3. What are the components of an Image Processing System?

3. What are the components of an Image Processing System?

   March 04, 2013       legend

---

>Table of contents
 
Components of an Image Processing System:

As recently as the mid-1980s, numerous models of image processing systems being sold
throughout the world were rather substantial peripheral devices that attached to equally
substantial host computers. Late in the 1980s and early in the 1990s, the market shifted to image
processing hardware in the form of single boards designed to be compatible with industry
standard buses and to fit into engineering workstation cabinets and personal computers. In
addition to lowering costs, this market shift also served as a catalyst for a significant number of
new companies whose specialty is the development of software written specifically for image
processing.

Although large-scale image processing systems still are being sold for massive
imaging applications, such as processing of satellite images, the trend continues toward
miniaturizing and blending of general-purpose small computers with specialized image
processing hardware. Figure 3 shows the basic components comprising a typical general-purpose system used for digital image processing. The function of each component is discussed in the following paragraphs, starting with image sensing.

With reference to sensing, two elements are required to acquire digital images. The first is a
physical device that is sensitive to the energy radiated by the object we wish to image. The
second, called a digitizer, is a device for converting the output of the physical sensing device into
digital form. For instance, in a digital video camera, the sensors produce an electrical output
proportional to light intensity. The digitizer converts these outputs to digital data.

Specialized image processing hardware usually consists of the digitizer just mentioned, plus
hardware that performs other primitive operations, such as an arithmetic logic unit (ALU), which
performs arithmetic and logical operations in parallel on entire images. One example of how an
ALU is used is in averaging images as quickly as they are digitized, for the purpose of noise
reduction. This type of hardware sometimes is called a front-end subsystem, and its most
distinguishing characteristic is speed. In other words, this unit performs functions that require
fast data throughputs (e.g., digitizing and averaging video images at 30 framess) that the typical
main computer cannot handle.

                         Fig.3. Components of a general purpose Image Processing System

The computer in an image processing system is a general-purpose computer and can range from
a PC to a supercomputer. In dedicated applications, some times specially designed computers are
used to achieve a required level of performance, but our interest here is on general-purpose image processing systems. In these systems, almost any well-equipped PC-type machine is
suitable for offline image processing tasks.

Software for image processing consists of specialized modules that perform specific tasks. A
well-designed package also includes the capability for the user to write code that, as a minimum,
utilizes the specialized modules. More sophisticated software packages allow the integration of
those modules and general-purpose software commands from at least one computer language.

Mass storage capability is a must in image processing applications. An image of size 1024*1024
pixels, in which the intensity of each pixel is an 8-bit quantity, requires one megabyte of storage
space if the image is not compressed. When dealing with thousands, or even millions, of images,
providing adequate storage in an image processing system can be a challenge. Digital storage for
image processing applications falls into three principal categories: (1) short-term storage for use
during processing, (2) on-line storage for relatively fast re-call, and (3) archival storage,
characterized by infrequent access. Storage is measured in bytes (eight bits), Kbytes (one
thousand bytes), Mbytes (one million bytes), Gbytes (meaning giga, or one billion, bytes), and
Tbytes (meaning tera, or one trillion, bytes). One method of providing short-term storage is
computer memory. Another is by specialized boards, called frame buffers, that store one or more
images and can be accessed rapidly, usually at video rates (e.g., at 30 complete images per
second).The latter method allows virtually instantaneous image zoom, as well as scroll (vertical
shifts) and pan (horizontal shifts). Frame buffers usually are housed in the specialized image
processing hardware unit shown in Fig.3.Online storage generally takes the form of magnetic
disks or optical-media storage. The key factor characterizing on-line storage is frequent access to
the stored data. Finally, archival storage is characterized by massive storage requirements but
infrequent need for access. Magnetic tapes and optical disks housed in “jukeboxes” are the usual
media for archival applications.

Image displays in use today are mainly color (preferably flat screen) TV monitors. Monitors are
driven by the outputs of image and graphics display cards that are an integral part of the
computer system. Seldom are there requirements for image display applications that cannot be
met by display cards available commercially as part of the computer system. In some cases, it is
necessary to have stereo displays, and these are implemented in the form of headgear containing
two small displays embedded in goggles worn by the user.

Hardcopy devices for recording images include laser printers, film cameras, heat-sensitive
devices, inkjet units, and digital units, such as optical and CD-ROM disks. Film provides the
highest possible resolution, but paper is the obvious medium of choice for written material. For
presentations, images are displayed on film transparencies or in a digital medium if image
projection equipment is used. The latter approach is gaining acceptance as the standard for image
presentations.

Networking is almost a default function in any computer system in use today. Because of the
large amount of data inherent in image processing applications, the key consideration in image
transmission is bandwidth. In dedicated networks, this typically is not a problem, but
communications with remote sites via the Internet are not always as efficient. Fortunately, this
situation is improving quickly as a result of optical fiber and other broadband technologies.

>Table of contents

Read More

2. What are the fundamental steps in Digital Image Processing?

2. What are the fundamental steps in Digital Image Processing?

   March 04, 2013       legend

---

>Table of contents
 
Fundamental Steps in Digital Image Processing:

Image acquisition is the first process shown in Fig.2. Note that acquisition could be as simple as
being given an image that is already in digital form. Generally, the image acquisition stage
involves preprocessing, such as scaling.

Image enhancement is among the simplest and most appealing areas of digital image processing.
Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or
simply to highlight certain features of interest in an image. A familiar example of enhancement is
when we increase the contrast of an image because “it looks better.” It is important to keep in
mind that enhancement is a very subjective area of image processing.

Image restoration is an area that also deals with improving the appearance of an image.
However, unlike enhancement, which is subjective, image restoration is objective, in the sense
that restoration techniques tend to be based on mathematical or probabilistic models of image
degradation. Enhancement, on the other hand, is based on human subjective preferences
regarding what constitutes a “good” enhancement result.

Color image processing is an area that has been gaining in importance because of the significant
increase in the use of digital images over the Internet.

Wavelets are the foundation for representing images in various degrees of resolution.
Compression, as the name implies, deals with techniques for reducing the storage required to
save an image, or the bandwidth required to transmit it. Although storage technology has
improved significantly over the past decade, the same cannot be said for transmission capacity.
This is true particularly in uses of the Internet, which are characterized by significant pictorial
content. Image compression is familiar (perhaps inadvertently) to most users of computers in the
form of image file extensions, such as the jpg file extension used in the JPEG (Joint
Photographic Experts Group) image compression standard

Morphological processing deals with tools for extracting image components that are useful in the
representation and description of shape.

Segmentation procedures partition an image into its constituent parts or objects. In general,
autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged
segmentation procedure brings the process a long way toward successful solution of imaging
problems that require objects to be identified individually. On the other hand, weak or erratic
segmentation algorithms almost always guarantee eventual failure. In general, the more accurate
the segmentation, the more likely recognition is to succeed.

Representation and description almost always follow the output of a segmentation stage, which
usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels
separating one image region from another) or all the points in the region itself. In either case,
converting the data to a form suitable for computer processing is necessary. The first decision
that must be made is whether the data should be represented as a boundary or as a complete
region. Boundary representation is appropriate when the focus is on external shape
characteristics, such as corners and inflections. Regional representation is appropriate when the
focus is on internal properties, such as texture or skeletal shape. In some applications, these
representations complement each other. Choosing a representation is only part of the solution for
transforming raw data into a form suitable for subsequent computer processing. A method must
also be specified for describing the data so that features of interest are highlighted. Description,
also called feature selection, deals with extracting attributes that result in some quantitative
information of interest or are basic for differentiating one class of objects from another

Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its
descriptors. We conclude our coverage of digital image processing with the development of
methods for recognition of individual objects.


>Table of contents

Read More