This document discusses image and speech processing. It provides an overview of image processing techniques including dithering, erosion, dilation, opening, and closing. These techniques are used to manipulate digital images by modifying pixels at image boundaries or within images. The document also discusses using speech recognition to improve human-computer interfaces and synchronization of image and speech processing.

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IMAGE PROCESSING
Mr.J.P.Patil Mr.R.D.Badgujar Mr.M.L.Patel
Lecturer, Lecturer, Lecturer,
RCPIT,Shirpur RCPIT,Shirpur RCPIT,Shirpur
patiljitendra@rediffmail.com ravi_badgujar@rediffmail.com
Mob.:9423193448 Mob.:-9881804224 Mob.:9372092305
ABSTRACT
Image and Speech processing are used to be a single unified field in the early sixties
and seventies. Today , it has expanded and diversified into several branches based on
mathematical tools as well as applications. For instance there are separate topics dealing
with fuzzy IP, morphological IP knowledge based IP etc. Similarly several topics deal with
diverse application specific tools for remote sensing industrial vision and so forth.
Image analysis issue such as segmentation, edge/line detection, feature extraction,
image description and pattern recognition have been covered in great deal and all the state-
of-art concepts have been discussed in many papers.
The main motivation for extracting the content of information is the accessibility
problem. A problem that is even more relevant for dynamic multimedia data, which also have
to be searched and retrieved. While content extraction techniques are reasonably developed
for text, video data still is essentially opaque. Its richness and complexity suggests that there
is a long way to go in extracting video features, and the implementation of more suitable and
effective processing procedures is an important goal to be achieved.
1. INTRODUCTION computer easier. Virtual reality, the
technology of interacting with a computer
Image Processing is development of the art using all of the human senses, will also
and technique of producing images known as contribute to better human and computer
photographs. Photography is so much a part of interfaces. Standards for virtual-reality
life today that the average person may program languages—for example, Virtual
encounter more than 1000 camera images a Reality Modeling language (VRML)—are
day. Photographs preserve personal memories currently in use or are being developed for the
(family snapshots) and inform us of public World Wide Web.
events (news photos). They provide a means
of identification (driver's license photos) and Synchronization of Image and Speech
of glamorization (movie-star portraits); views Processing plays a very important role in this
of far-off places on Earth (travel photographs) fairy world. Other, exotic models of
and in space (astral photographs); as well as computation are being developed, including
microscopic scenes from inside the human biological computing that uses living
body (medical and scientific photos). Many organisms, molecular computing that uses
specialized commercial categories, including molecules with particular properties, and
fashion, product, and architectural computing that uses deoxyribonucleic acid
photography, also fit under the broad umbrella (DNA), the basic unit of heredity, to store data
that defines photography's function in the and carry out operations. These are examples
world today. of possible future computational platforms
that, so far, are limited in abilities or are
Speech Processing improved speech strictly theoretical. Scientists investigate them
recognition will make the operation of a because of the physical limitations of

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miniaturizing circuits embedded in silicon. 4.1.2 Erosion :-
There are also limitations related to heat
generated by even the tiniest of transistors. Erosion is the process of eliminating all the
boundary points from an object, leaving the
object smaller in area by one pixel all around
its perimeter. If it narrows to less than three
3. BACKGROUND
pixels thick at any point, it will become
Content of image includes resolution, disconnected (into two objects) at that point. It
color, intensity, and texture. Image resolution is useful for removing from a segmented
is just the size of image in term of display image objects that are too small to be of
pixels. Color is represented using RGB color interest.
model in computer. For each pixel on the
screen, there are three bytes (R,G,B color Shrinking is an special kind of erosion in
component) to represent its color. Each color that single-pixel objects are left intact. This is
component is in the range of 0 to 255. useful when the total object count must be
Intensity is the gray level information of pixels preserved.
represented by one byte. The intensity value is Thinning is another special kind of
in the range of 0 to 255. Texture characterizes erosion. It is implemented in a two-step
local variations of image color or intensity. process. The first step will mark all candidate
Although texture-based methods has been pixels for removal. The second step actually
widely used in computer vision and graphics, removes those candidates that can be removed
there is no single commonly accepted without destroying object connectivity.
definition of texture. Each texture analysis
method defines texture according to its own 4.1.3 Dilation :-
model. We consider texture as a symbol of
local color or intensity variation. Image Dilation is the process of incorporating
regions that are detected to have a similar into the object all the background pixels that
texture have similar pattern of local variation touch it, leaving it larger in area by that
of color or intensity. amount. If two objects are separated by less
than three pixels at any point, they will
4. BASIS IMAGE PROCESSING: become connected (merged into one object) at
4.1 THEORY OF IMAGE PROCESSING that point. It is useful for filling small holes in
Modern digital technology has made it segmented objects.
possible to manipulate multi-dimensional
signals with systems that range from simple Thickening is a special kind of dilation. It
digital circuits to advanced parallel computers. is implemented in a two-step process. The first
The goal of this manipulation can be divided step marks all the candidate pixels for
into three categories: addition. The second step adds those
* Image Processing image in -> image out candidates that can be added without merging
* Image Analysis image in -> measurements objects.
out 4.1.4 Opening :-
* Image Understanding image in -> high-level
description out The process of erosion followed by
Common Image Processing techniques : dilation is called opening. It has the effect of
eliminating small and thin objects, breaking
4.1.1 Dithering :- objects at thin points, and generally smoothing
the boundaries of larger objects without
Dithering is a process of using a pattern of significantly changing their area.
solid dots to simulate shades of gray. Different
shapes and patterns of dots have been 4.1.5 Closing :-
employed in this process, but the effect is the
same. When viewed from a great enough The process of dilation followed by
distance that the dots are not discernible, the erosion is called closing. It has the effect of
pattern appears as a solid shade of gray. filling small and thin holes in objects,
connecting nearby objects, and generally

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smoothing the boundaries of objects without between black and white. To improve the
significantly changing their area. ability to differentiate, special lighting
techniques must often be employed. It should
4.1.6 Filtering :- be pointed out that the above method of using
a histogram is only one of a large number of
Image filtering can be used for noise ways to threshold an image. Such a method is
reduction, image sharpening, and image said to use a global threshold for an entire
smoothing. By applying a low-pass or high- image. When it is not possible to find a single
pass filter to the image, the image can be threshold or an entire image, an approach is to
smoothed or sharpened respectively. Low pass partition the total image into smaller
filter is used to reduce the amplitude of high- rectangular areas and determine the threshold
frequency components. Simple low pass filters or each window being analyzed. Images of a
applies local averaging. The gray level at each weld pool in real time were taken and digitized
pixel is replaced with the average of the gray using thresholding technique. The images
levels in a square or rectangular neighborhood. were thresholded at various threshold values
Gaussian Low pass Filter applies Fourier
and also at the optimum value to show the
transform to the image. High pass filter is used importance of choosing an appropriate
to increase the amplitude of high-frequency threshold.
components
4.2 IMAGE ANALYSIS FEATURE EXTRACTION:
Image techniques are used to enhance, We have seen analysis or any visual
improve, or otherwise alter an image and to pattern reorganization problem, the camera
prepare it for image analysis. takes the picture of scene and passes the
picture to a feature extractor, whose purpose
The various techniques employed in image is data reduction by measuring certain features
processing and analysis are:
or properties that distinguish objects or their
1. Image data reduction parts. Feature extraction usually, is associated
with another method called feature selection.
2. Segmentation The objective of feature selection and
extraction techniques is to reduce this
3. Feature extraction dimensionality.
The objective of feature extraction is to
4. Object recognition represent an object in compact way that
facilities image analysis task in terms of
SEGMENTATION
algorithmic simplicity and computationally
Segmentation is the generic name for the efficiency.
number of different techniques that divide the
image into segments of its constituents. In OBJECT RECOGNITION
segmentation, the objective is to group areas
of an image having similar characteristics or
The most difficult part of image
features into distinct entities representing parts
processing is object recognition. Although
of the image. One of the most important
there are many image segmentation algorithms
techniques which this papers deals with is
that can segment image into regions with some
thresholding.
continuous feature, it is still very difficult to
THRESHOLDING recognize objects from these regions.
Thresholding is a binary conversion There are several reasons for this.
technique in which each pixel is converted into First, image segmentation is an ill-posed task
a binary value either black or white. This is and there is always some degree of uncertainty
accomplished by utilizing a frequency in the segmentation result. Second, an object
histogram of the image and establishing what may contain several regions and how to
intensity (gray level) is to be the border connect different regions is another problem.

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At present, no algorithm can segment general
images into objects automatically with high
accuracy. In the case that there is some a prior
knowledge about the foreground objects or
background scene, the accuracy of object
recognition could be pretty good. Usually the
image is first segmented into regions
according to the pattern of color or texture.
Then separate regions will be grouped to form
objects. The grouping process is
important for the success of object recognition.
Full automatically grouping only occurs when
the a prior knowledge about the foreground
objects or background scene exists. In the
other cased, human interaction may be
required to achieve good accuracy of object
recognition
5. DEMOS
This is a demo showing different image
processing techniques.
Here is the ORIGINAL image, taken from the
photo "Robin Jeffers at Ton House" (1927) by
Edward Weston. QUANTIZATION
LOW PASS FILTERING
Here is the image with only
Here is the image filtered
5 grayscale shades; the original
this filter is a 3-by-3 mean filter
has 184 shades. -
notice how it smoothes the
Note how much detail is retained the
texture of the image while
with only 5 shades
blurring out the edges
LOW PASS FILTERING II
EDGE DETECTION
Here is the image filtered
This filter is a 2-dimensional
Notice the difference between the
Laplacian (actually the negative
Images from the two filters?
of the Laplacian) - notice how it brings out
the edges in the image
Here is the image with every 3rd pixel
sampled, and the intermediate pixels filled in
with the sampled values. Note the blocky
appearance of the new image.

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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 image is stored only as a set of
pixels with RGB values in computer. The
computer knows nothing about the meaning of
these pixel values. The content of an image is
quite clear for a person. However, it is not so
easy for a computer. For example, it is a piece
of cake to recognize yourself in an image,
even in a crowd. But this is extremely difficult
for computer. The preprocessing is to help the
EDGE DETECTION II computer to understand the content of image.
This is the Laplacian filter with the original What is the so-called content of image? Here
image added back in – notice how it brings out content means features of image or its objects
the edges in the image while maintaining the such as color, texture, resolution, and motion.
underlying grey scale information. Object can be viewed as a meaningful
component in an image. For example, a
moving car, a flying bird, a person are all
objects. There are a lot of techniques for image
processing. This chapter starts with an
introduction to general image processing
techniques and then talks about video
processing techniques. The reason we want to
introduce image processing first is that image
processing techniques can be used on video if
we treat each picture of a video as a still
image.
7. APPLICATIONS
6. IMAGE PROCESSING VERSUS REAL-TIME MEASUREMENT OF
IMAGE ANALYSIS TRAFFIC QUEUE PARAMETERS BY
USING IMAGE PROCESSING
Image processing relates to the
TECHNIQUES
preparation of an image for latter analysis and
use. Images captured by a camera or a similar
The real-time measurement of traffic
technique (e.g. by a scanner) are not queue parameters are required in many traffic
necessarily in a form that can be used by
situations such as accident and congestion
image analysis routines. Some may need
monitoring and adjusting the timings of the
improvement to reduce noise, others may need
traffic lights. So far the reported image
to be simplified, and still others may need to
processing methods have been targeted for
be enhanced, altered, segmented, filtered, etc.
measuring simple traffic parameters. In this
Image processing is the collection of routines
paper we describe image processing
and techniques that improve, simplify, techniques together with the results to measure
enhance, or otherwise alter an image. Image
the queue traffic parameters in real-time. The
analysis is the collection of processes in which
proposed queue detection algorithm consists of
a captured image that is prepared by image
a motion detection and vehicle detection
processing is analyzed in order to extract
operation, both based on extracting edges of
information about the image and to identify
the scene. The results show that the reposed
objects or facts about the object or its algorithms are able to measure various queue
environment. parameters such as queue detection, length of
In a sophisticated image processing the queue, period of the occurrence of the
system it should be possible to apply specific queue, slope of the queue etc.
image processing operations to selected

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BAW-Project - Digital Image Processing The signals are usually processed in a
digital representation whereby speech
Aim of this project is to investigate the processing can be seen as the intersection of
effects that lead to structural changes in river digital signal processing and natural language
embankments. Changes on the microscopic processing.
scale can eventually course complete
destabilization of shore fortifications. We Speech processing can be divided in the
study the microscopic movement that occurs at following categories:
boundaries between sediment layers (or
geotextiles) due to hydraulic pressure changes. Speech recognition, which deals with
Towards this end endoscopes are used to analysis of the linguistic content of a speech
gather images from within the sediment. The signal.
images are in turn analyzed by digital image Speaker recognition, where the aim is to
sequence analysis techniques which yield recognize the identity of the speaker.
information on the frequency of motion and
occurring velocity fields. Another aspect of Enhancement of speech signals, e.g. noise
our research is the estimation of flow fields reduction,
through sediment layers which again can be
done using endoscopes in conjunction with Speech coding for compression and
image processing techniques. transmission of speech. See also
telecommunication.
Remote sensing
Natural resources survey and Voice analysis for medical purposes, such
management; estimation related to agriculture, as analysis of vocal loading and dysfunction of
hydrology, forestry, mineralogy; urban the vocal cords.
planning; environment and pollution control;
Speech synthesis: the artificial synthesis of
cartography, registration of satellite images
speech, which usually means computer
with terrain maps; monitoring traffic along
generated speech.
roads, docks, air fields; etc.
Speech compression is important in the
Bio-medical telecommunications area for increasing the
amount of info which can be transferred,
ECG, EEG, EMG analysis; cytological, stored, or heard, for a given set of time and
histological and stereological applications; space constraints.
automated radiology and pathology, X-ray
images analysis; mask screening of medical Speech can be described as an act of
images such as chromosome slides for producing voice through the use of the vocal
detection various diseases mammograms, folds and vocal apparatus to create a linguistic
cancers, smears, CAP, MRI, PET, SPECT, act designed to convey information.
USG and other tomography images.
Various types of linguistic acts where the
Military Applications audience consists of more than one individual,
including public speaking, oration, and
Missile guidance and detection; target quotation.
identification; navigation of pilot less vehicles;
reconnaissance; and range finding; etc. The physical act of speaking, primarily
through the use of vocal cords to produce
voice. See phonology and linguistics for more
detailed information on the physical act of
8. SPEECH PROCESSING
speaking.
Speech processing is the study of speech
However, speech can also take place
signals and the processing methods of these
inside one's head, known as intrapersonal
signals.
communication, for example, when one thinks
or utters sounds of approval or disapproval. At

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a deeper level, one could even consider
subconscious processes, including dreams
where aspects of oneself communicate with
each other (see Sigmund Freud), as part of
intrapersonal communication, even though
most human beings do not seem to have direct
access to such communication.
Speech recognition (in many contexts also
known as 'automatic speech recognition',
computer speech recognition or erroneously as
Voice Recognition) is the process of
converting a speech signal to a sequence of
words, by means of an algorithm implemented
as a computer program. Speech recognition
applications that have emerged over the last
years include voice dialing (e.g., Call home),
call routing (e.g., I would like to make a
collect call), simple data entry (e.g., entering a
credit card number), and preparation of
structured documents (e.g., a radiology report).
Voice recognition or speaker recognition
is a related process that attempts to identify the
person speaking, as opposed to what is being
said.
CONCLUSION
So, these were some of the primitive
processing operations which are applied on
the captured Image. Not all the operations are
necessary; actually it depends on our need.
Speech Processing improved speech
recognition will make the operation of a
computer easier. Virtual reality, the
technology of interacting with a computer
using all of the human senses, will also
contribute to better human and computer
interfaces. Standards for Virtual-reality
program languages
REFERENCES :
1. ACM Transaction on graphics
2. Digital Image Processing and Analysis-
B.Chanda, D.Dutta Maujmder
3. http://www.google.com
4. www.howstuffworks.com
5. http://www.baw.de