Content-Based Image Retrieval Case Study

Content-Based Image Retrieval Case Study
INTRODUCTION
Pertaining to the tremendous growth of digitalization in the past decade in areas of healthcare,
administration, art & commerce and academia, large collections of digital images have been created.
Many of these collections are the product of digitizing existing collections of analog photographs,
diagrams, drawings, paintings, and prints with which the problem of managing large databases and
its repossession based on user specifications came into the picture. Due to the incredible rate, at
which the size of image and video collection is growing, it is eminent to skip the subjective task of
manual keyword indexing and to pave the way for the ambitious and challenging idea of the
contend–based description of imagery.
Many ... Show more content on Helpwriting.net ...
In this paper, we will be looking at different methods for comparative study of the state of the art
image processing techniques stated below (K means clustering, wavelet transforms and DiVI
approach) which consider attributes like color, shape and texture for image retrieval which helps us
in solving the problem of managing image databases easier.
Figure 1: Traditional Content–Based Image Retrieval System
LITERATURE SURVEY–
DiVI– Diversity and Visually–Interactive Method
Aimed at reducing the semantic gap in CBIR systems, the Diversity and Visually–Interactive (DiVI)
method [2] combines diversity and visual data mining techniques to improve retrieval efficiency. It
includes the user into the processing path, to interactively distort the search space in the image
description process, forcing the elements that he/she considers more similar to be closer and
elements considered less similar to be farther in the search space. Thus, DiVI allows inducing in the
space the intuitive perception of similarity lacking in the numeric evaluation of the distance
function. It also allows the user to express his/her diversity preference for a query, reducing the
effort to analyze the result when too many similar images are returned.
Figure 2: Pipeline of DiVI processing embedded in a CBIR–based tool.
Processing of
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Taking a Look at Image Processing
Image Processing is a technique to enhance raw images received from cameras/sensors placed on
satellites, space probes and aircrafts or pictures taken in normal day–today life for various
applications. Various techniques have been developed in Image Processing during the last four to
five decades. Most of the techniques are developed for enhancing images obtained from unmanned
spacecrafts, space probes and military reconnaissance flights. Image Processing systems are
becoming popular due to easy availability of powerful personnel computers, large size memory
devices, graphics software's etc. The common steps in image processing are image scanning,
storing, enhancing and interpretation.
Image Processing is used in various applications such as,
Remote Sensing
Medical Imaging
Non–destructive Evaluation
Forensic Studies
Textiles
Material Science.
Military
Film industry
Document processing
Graphic arts
Printing Industry
1.1. METHODS OF IMAGE PROCESSING There are two methods available in Image Processing.
(1)Analog image processing
(2)Digital image processing
1.1.1. ANALOG IMAGE PROCESSING Analog Image Processing refers to the alteration of image
through electrical means. The most common example is the television image. The television

Image Processing And Image Enhancement
Abstract
Image enhancement is to process an image, in order to make the result more suitable than original
image for specific application. i.e. the image is enhanced.For that many image enhancement
techniques are used. Appropriate choice of such techniques is very important.Image Enhancement is
simple and it's the area based on digital image processing techniques. It improves the quality of the
images by working with the existing data.
Keywords:
Image processing, Image enhancement
1. Introduction
Image processing is the input image which is converted from one form to another. Digital image
processing plays a vital role in real world applications. Before processing an image, it must be
converted into a digital form.
One of part of the image processing is the image enhancement. The main objective of image
enhancement is to modify attributes of an image to make it more suitable for a given task. Here, one
or more attributes of the image get modified. The main purpose of image enhancement is to bring
out details which are hidden in an image, or to increase the contrast in a low contrast image. It
produces an output image that is better than the original image by changing the pixel's intensity of
the input image. Image enhancement is applied in many fields. For example, medical image
analysis, analysis of images from satellites, Aerial imaging, Satellite imaging, Digital camera
applications, Remote sensing etc.
2.Enhancement Techniques
[1]The enhancement methods are mainly

Essay On Feature Extraction
Feature plays a very important role in the area of image processing. Different feature extraction
techniques are applied on different types of images to get features that will be useful in classifying
and recognition of images. Features describes the important information of images that helps to
classify images correctly and remarkably reduce the dimension of the images. In pattern recognition
and image processing, feature extraction is a special form of dimensionality reduction. The main
goal of feature extraction is to obtain the most relevant information from the original data and
represent that information in a lower dimensional space. Effective feature extraction from various
intensity or color in images have been an important topic ... Show more content on Helpwriting.net
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The histogram gives the feature vector for entire window. Example of LBP feature extraction is
given in the Figure 2.1
Figure 2.1: Finding decimal value for central pixel using LBP
LBP has some limitations that reduces its application fields. LBP is not rotation invariant and the
size of the features increases exponentially with the number of neighbors which leads to an increase
of computational complexity in terms of time and space.
2.2.1 Noise Adaptive Binary Pattern (NABP)
Noise adaptive binary pattern [12] is a modification of local binary pattern. Though LPB is powerful
in extraction local features, it has a lack of discriminative power and sensitive to noise. LBP may
produce same pattern for big difference and same difference of the central pixel with neighboring
pixel. LBP is also affected by noise. So, a modification is proposed on LBP to face fluctuation of
intensity and noise in image. They proposed a threshold (square root of central pixel + central pixel).
If neighboring pixel value is greater than the pixel then the pattern value is 1 otherwise 0. Figure 2.2
illustrates calculation of NABP.
Figure 2.2: Finding decimal value for central pixel using NABP
2.2.3 Completed Local Binary Pattern (CLBP)
CLBP [1] is also very similar to LBP. Main

The Image Processing Techniques For Breast Cancer
Abstract– In recent years the image processing techniques are used commonly in various medical
areas for improving earlier detection and treatment stages, in which the time span or elapse is very
important to discover the disease in the patient as possible as fast, especially in many tumours such
as the lung cancer, breast cancer. This system generally first segments the area of interest (lung) and
then analyses the separately obtained area for nodule detection in order to examine the disease. Even
with several lung tumour segmentations have been presented, enhancing tumour segmentation
methods are still interesting because lung tumour CT images has some complex characteristics, such
as large difference in tumour appearance and uncertain tumour boundaries. To address this problem,
tumour segmentation method for CT Images which separates non–enhancing lung tumours from
healthy tissues has been carried out by clustering method. The proposed method uses pre–processing
technique that remove unwanted artifacts using median and wiener filters. Initially, the segmentation
of the CT images has been carried out by using K– Means clustering method. To the clustered result,
EK–Mean clustering is applied . Further the features like entrpy, Contrast, Correlation,Homogenity
and the area are extracted from the tumorous part of Fuzzy Ek– Means segmented Image. For
feature extraction, statistic method called Gray Level Co–occurrence Matrix (GLCM). Classification
is done by using the

Hidden Reasons for Kodak's Digital Revolution Essay
Kodak and the Digital Revolution: Case Analysis
Since the early 1880's, Kodak had proven themselves to be great innovators and had worked on
building their brand on a domestic and international front. They invested heavily in marketing to
establish their image and realized early on that their profits would come from consumables rather
than hardware. They sold their equipment at low prices in order to fuel their highly profitable film
sales. This use of a razor–blade strategy, coupled with strong relationships with retailers positioned
Kodak as an industry leader. Additionally, their heavy investment in R&D allowed Kodak to
grow organically, proving fruitful with the advent of color film. Thus, Kodak's expertise in color
film ... Show more content on Helpwriting.net ...
In traditional imaging, the image chain was as follows: Image Capture > Roll of Film >
Printing > Storage.b This was a change from the digital imaging chain which was: Image Capture
> Digitization > Storage > Retrieval, Transmission, Printing, Manipulation, and
Projection.a See custom attachment for graphical representations of traditional imaging chain and
figure A taken from page 9 of Kodak and the Digital Revolution case. Kodak's response to Sony's
introduction of the Mavica in 1981 was one of trepidation as well as acceptance. Kodak clearly
realized that the Mavica had the potential to greatly alter the landscape of its industry. Kodak
acknowledged this occurrence as a major paradigm shift; however, due to the escalating
commitment and its deep roots in traditional photography, Kodak failed to react accordingly.
Kodak's CEO at the time, Colby Chandler, outwardly recognized the public's affinity for color prints
– the product that made Kodak a household name. Yet, others at Kodak went as far as to make
doomsday predictions. Some managers within Kodak felt that the inception of the Mavica would be
the death of traditional photography. It is apparent that Kodak should have invested in research and
development as traditional film was reaching its natural limit, thus causing the referenced paradigm
shift. Without Kodak's willingness to outwardly adapt to the change, whether it be through
R&D or other channels, Kodak's ability to

Content Based Image Compression Using Dct And Dwt Technique
CONTENT BASED IMAGE COMPRESSION USING DCT AND DWT TECHNIQUE
Abstract: Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are the most
known methods used in digital image compression. Wavelet transform has better efficiency
compared to Fourier transform because it describe any type of signals both in time and frequency
domain simultaneously. In this paper, we will discuss the use of Discrete Cosine Transform (DCT)
and Discrete wavelet transformation (DWT) based Image compression Algorithmand compare the
efficiency of both methods. We do the numerical experiment by considering various types of images
and by applying DCT and DWT–SPIHT to compress an image. We found that DWT yields better
result as compared to DCT.
In this paper, we will do comparison with discrete cosine transform (DCT) which is heart of JPEG
(Joint Photographic Experts Group) standard and widely used wavelet based image compression
algorithm set partitioning in hierarchical tree based on different performance measure such as Peak
to Noise Ratio (PSNR), Mean Square Error (MSE) and CR.
Keywords – Discrete Cosine Transform, Discrete Wavelet Transform, filters, Image Compression.
Introduction:
1.1 Image Processing
A digital image which is portrayed in a[m,n] which is described as a 2D discrete space is received
from a simple image a(x,y) in a constant space using sampling process which is known as a
digitalization. The 2D steady image a(x,y) can be separated into M rows and N

Digital Image Processing : A Multi Dimensional Visual...
ABSTRACT:
Face is a analyzable multi–dimensional visual model and processing a process model for face
recognition is challenging. This paper presents a methodological analysis for face identification
based on content explanation formulation of coding and decoding the face image. categorization
using the Euclidian distance. The content is to use the system for a particular face and separate from
a large number of stored faces with some real time variations as well. The Eigen face attack uses
particular faces with some real time variation. The Eigen face formulation uses principal
components analysis (PCA) algorithm for the acceptance of the images. It gives us prompt way to
insight the lower dimensional space.
Digital Image processing: ... Show more content on Helpwriting.net ...
The sampling theorem states that for a signal to be completely reconstruct able, it must satisfy the
following equation:
Were Ws=sampling frequency W = frequency of sampled signal
. To explain all of this, first consider the simple sinusoidal function given by f(x) = cos(x). Figure 1
shows a plot of this function and Fig. 2 shows a plot of its Fourier transform.
Figure 3 shows a truncated version of that function, and Fig.4 shows the equivalent Fourier
transform.
Figure 1. Cosine function with amplitude A and frequency of 1 Hz.
Figure 2. Power spectrum of the cosine function with amplitude A and frequency of 1 Hz. Figure 3.
Truncated cosine function. The truncation is in the variable x (e.g., time), not in the amplitude.
Figure 4. The power spectrum of the truncate cosine function is a continuous one, with maximum
values at the same points, like the power spectrum of the continuous cosine function.
This is called as folding. In the above fig4 shows that lower frequencies of signal contains most of
signal's powers. A standard analog filter transfer function may be given as
Where the damping factor of the filter and w is is its natural frequency. By cascading first and
second order filters, one of them will get higher order systems which have higher performances.
Bessel filters are used for high performance applications, this is because of two factors.
1) The damping factors

Character Recognition By Machines, An Innovative Way By...
Abstract–Character Recognition by machines is an innovative way by which the dependence on
manpower is reduced. Character recognition provides a reliable alternative of converting manual
text into digitized format. Now–a–days, as technology becomes integral part of human life, many
applications have enabled the incorporation of English OCR for real time inputs. The advantages
that the English alphabet has is its simplicity offered by less number of letters i.e. 26 and easier
classification due to the concept of lowercase and uppercase. If we consider Devnagari script in this
scenario, we will come across myriad hurdles because this script lacks the simplicity of English. The
concept of fused letters, modifiers, shirorekha and spitting similarities in some letters make
recognition difficult. Also, character recognition for handwritten text is far more complex than that
for machine printed characters. This is because of the versatility and different writing techniques
adopted by people. The direction of strokes, pressure applied on writing equipments, quality of
writing equipment and the mentality of the writer itself highly affects the written text. These
problems when combined with the intricate details of Devnagari script, the complications in
constructing a HCR of this script are increased. The proposed system focuses on these two issues by
adopting Hough transform for detecting features from lines and curves. Further, for classification,
SVM is used. These two methods

Digital Image And Its Effect On The Quality Of Image
Abstract: In image processing, noise reduction and restoration of image is expected to improve the
qualitative inspection of an image and the performance criteria of quantitative image analysis
techniques Digital image is inclined to a variety of noise which affects the quality of image. The
main purpose of de–noising the image is to restore the detail of original image as much as possible.
The criteria of the noise removal problem depends on the noise type by which the image is
corrupting .In the field of reducing the image noise several type of linear and non linear filtering
techniques have been proposed . Different approaches for reduction of noise and image
enhancement have been considered, each of which has their own limitation and advantages.
Index Terms– Digital Image Processing, Images Types, Image Noise Model, Filters
INTRODUCTION
Digital Image process could be a part of digital signal process .The area of digital image process
refers to handling digital pictures by means of a computing device. Digital image process has many
merits on analog image process; it permits a significantly wider assortment of algorithms to be apply
to input file and may keep from issues for instance the build–up of noise and signal deformation
throughout processing. Digital Image process involves the modification of digital information for
improving the image qualities with the help of system. The process helps in maximize the clarity,
sharpness of image and details of options of

Evaluation Of Proposed Design And Necessary Corrective Action
Assignment No: 1
Title :
Review of proposed design and necessary corrective action is taking to consider and submit
publication/presentation details with review report.
Objectives :
1. Constructing a semantic taxonomy for the land–cover classification of satellite images. 2.
Classifying satellite images according to their types such as vegetation, building, water etc.
3. Implementing MapReduce for processing large amount of data (Satellite Images).
Introduction :
Satellite images play a major role in today's world in real–time event detection. These events may
vary from changing landforms, depleting glaciers to catastrophic events like earthquakes, tsunamis
and sand storms. The drastic changes after such events need to be monitored and capturing satellite
images for such event detection can be helpful. The idea behind this project is to detect the changing
landforms across different vegetations, store this data, classify it on the basis of certain specified
parameters and retrieve the classified data using well defined mechanisms. Segmentation and event
detection is highly scalable in satellite images. With the increasing need to have real–time, classified
data for specific applications there is an increasing need to store this chunk of data in a distributed
environment to have better access. The basic idea to is to capture the satellite images and store them
in a distributed environment. The environment to be chosen is Hadoop Distributed Environment.
Hadoop makes it

Digital Image Of A Optical Signature Recognition
3.4.1 Offline Signature Recognition
In this type of recognition, the text is not recognized at the same time as it is produced but after the
user has finished writing. In this case, the text is originally written on a surface such as paper and
from there on it is recognized by the computer by scanning the surface. In the scanned Signature is
first stored digitally in grey scale format. bitmap image, and then further processing is done on it to
have a good recognition accuracy.
Features for recognition are enhanced and extracted from the stored bitmap image by using digital
image processing. Offline signature recognition is known as Optical Signature Recognition (OCR),
because the image of writing is converted into bit pattern by an optically digitizing device such as
optical camera or scanner. The recognition is done on this bit pattern data for machine–printed or
hand–written text [3]. Recognition of machine printed signatures is also a part of Optical Signature
Recognition. In offline, methods are less suitable for man–machine communication because no real
time interactivity is present. It is suitable for automatic conversion of paper documents to electric
documents, which then may be interpreted by computers. Some applications of the off–line
recognition are large–scale data processing such as postal address reading; check sorting, office
automation for text entry automatic inspection and identification [11].
3.4.2 Online Signature Recognition
In contrast to the offline

Image And Image Of Image Enhancement
INTRODUCTION
Image processing refers to the construction of an image for further analysis and use. Image taken by
a camera or same techniques are not actual in a form that can be used by image analysis process.
The technique involves in image enhancement need to be simplified, enhanced, filtered, altered,
segmented or need improvement to reducing noise, etc. Image processing is the collection of
techniques in which implementation is done for industrial applications to resolve various issues that
alter, improve, enhance or simplify an image. Image enhancement is one of the important parts of
digital image processing where image undergo for visual inspection or for machine analysis without
knowledge of its source of degradation. The processes involve in enhancement techniques to bring
out specific application of an image so that the result is satisfactory which more visible as compare
to original image. Image can be enhanced in various ways such as contrast enhancement, intensity,
density slicing, edge enhancement, removal of noise, and saturation transformation.[1]
Over several past years, contrast image enhancement has generated across many applications like
robot sensing, electronic products, fault detection, medical image analysis, etc. Thus, increasing in
popularity of contrast enhancement of images has forces researchers to study their enhancement
techniques and their effectiveness for the interpretability or perception of human viewers. Contrast
enhancement is a

A Literature Study Of Robust Color Image Watermarking...
A LITERATURE STUDY OF ROBUST COLOR IMAGE WATERMARKING ALGORITHM
PANKAJ SONI 1, VANDANA TRIPATI2, RITESH PANDEY3
1. Dept of ECE, ME student, G.N.C.S.G.I., JABALPUR, M.P., INDIA,
2–Dept of ECE, Asst. Prof., G.N.C.S.G.I., JABALPUR, M.P., INDIA,
2–Dept of ECE, Asst. Prof., G.N.C.S.G.I., JABALPUR, M.P., INDIA,
ABSTRACT: Digital Watermarking is a technology which is used to identify the owner, distributor
of a given image. In recent years, digital watermarking plays a vital role in providing the appropriate
solution and various researches have been carried out. In this paper, an extensive review of the
literature related to the color image watermarking is presented together with compression by
utilizing an assortment of techniques. The proposed method should provide better security while
transferring the data or messages from one end to the other end. The main objective of the paper is
to hide the message or a secret data into an image which acts as a carrier file having secret data and
to transmit to the intention securely. The watermark can be extracted with minimum error. In terms
of PSNR, the visual quality of the watermarked image is exceptional. The proposed algorithm is
robust to many image attacks and suitable for copyright protection applications.
KEYWORDS: Watermarking, Discrete wavelet transform, Discrete Cosine Transform, PSNR, MSE.
I. INTRODUCTION
DIGITAL image watermarking has become a necessity in many applications such as data
authentication, broadcast

General Review of Algorithms Presented for Image Segmentation
Image segmentation commonly known as partitioning of an image is one of the intrinsic parts of any
image processing technique. In this image pre processing step, the digital image of choice is
segregated into sets of pixels on the basis of some predefined and preselected measures or standards.
There have been presented many algorithms for segmenting a digital image. This paper presents a
general review of algorithms that have been presented for the purpose of image segmentation.
Segmenting or dividing a digital image into region of interests or meaningful structures in general
plays a momentous role in quite a few image processing tasks. Image analysis, image visualization,
object representation are some of them. The prime objective of segmenting a digital image is to
change its representation so that it looks more expressive for image analysis. During the course of
action in image segmentation, each and every pixel of the image segmentation is assigned a label or
value. The pixels that share the same value also share homogeneous traits. The examples can include
color, texture, intensity or some other features. Image segmentation can be defined as the technique
to divide the an image f (x, y) into a non empty subset f1, f2, ...., fn which is continuous and
disconnected. This step contributes in feature extraction. There are quite a few applications where
image segmentation plays a pivotal role. These applications vary from image filtering, face
recognition, medical imaging

Definition Of Image Quality Of Digital Imaging
CHAPTER 1: INTRODUCTION
1.1. Background
1.1.1. Limits to image quality
Digital imaging systems have a lot of applications including digital photography for recreational and
commercial purposes, electronic surveillance, satellite imaging and ground based geographic
information systems, medical imaging systems like computed tomography (CT) and magnetic
resonance imaging (MRI), forensics and even particle physics.
In many applications of digital imaging, a high quality image is required to allow human
interpretation or machine perception. Image quality is defined in terms of spatial resolution, pixel
resolution, temporal resolution and spectral resolution. For our application, we are interested in
spatial resolution.
Spatial resolution is measured in terms of pixel density and refers to the number of pixels used per
unit area to construct the image. It defines the minimum separation distance for 2 features in the
original scene for them to be distinguishable. Spatial resolution is determined by the density of
imaging sensors. Imaging sensors are charge coupled devices (CCD) or CMOS active pixel sensors,
arranged in a two dimensional array. The higher the sensor density, the higher the spatial resolution.
Higher sensor density can be achieved either by reducing the sensor size or increasing the size of the
chip carrying the sensors. Increasing the pixel density is limited by:
1. Reducing the size of sensors results in less light falling on the sensors, thus generating shot

Using Image Acquisition Is The Input Text Document
1. INPUT TEXT DOCUMENT Image acquisition is the input text document. Acquire image of any
document with the help of camera or scanner. Image acquisition is used to Acquire/obtain the image
of document in color, gray level or binary format. 2. PRE–PROCESSING These are the pre–
processing steps often performed in OCR 1. Binarization The simplest way to use image
binarization is to choose a threshold value, and classify all pixels with values above this threshold as
white, and all other pixels as black. Selecting proper threshold is very important task. In many cases,
finding one threshold compatible to the entire image is very difficult, and in many cases even
impossible. Therefore, adaptive image binarization is needed where an optimal threshold is chosen
for each image area. Binarization is processing of converting color image in to binary image. In
binarization, first we are converting color image in to Gray scale image using following formula.
[2]There are various Binerization methods and in that various different algorithm used are as
follows. Color image is converted into gray image and following algorithms are applied on gray
scale image for converting it in to binary image. Niblack Algorithm It is local thresholding
algorithm. Local thresholding algorithms give good results for document because it calculate
different threshold for different part of the image, considering pixel value. Niblack's algorithm
calculates a pixel–wise threshold by sliding a

Statistical Analysis Of Early Detection Of Liver Cirrhosis
Statistical Analysis Of Early Detection Of Liver Cirrhosis Through Medical Image Processing
Megha Bahdauria1,Chetna Garg1, Dr. Saurabh Mukherjee2, K.F. Rahman2
1.Mtech Scholar, Department of Computer Science, Banasthali University, Rajasthan, India
2. Associate Professor, Department of Computer Science, Banasthali University, Rajasthan, India
Abstract:
Statistical operations provide the means of principle of solving the many type of problems which
require the uncertain information in cirrhosis. This paper discusses the statistical operations.
Computed Tomography, Magnetic Resonance Imaging, Ultrasound etc has been proved very helpful
in diagnosing liver cirrhosis. Cirrhosis is an endemic disease across the world that leads to observed
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To let the liver function properly it is important to detect cirrhosis in early stage. Now a days several
noninvasive imaging techniques have been developed recently for detection of liver cirrhosis such
as CT, USG, MRI. In this paper we have used CT scan images of liver cirrhosis and applied some
statistical operations on those CT images such as mean, median, standard deviation and mode.
II. Methodology: CT scans are challenging because of the different image characteristics that must
be considered. Here we will be considering the statistical features of a CT scan of liver which is
having liver cirrhosis as a disease. The methodology followed is given below:
Fig.1 Flow Chart of Methodology Used
(1).Image Acquisition : To get an image of which you want to extract some features.
(2).Image Preprocessing : It is common practice to perform preprocessing on acquired CT scan
images before extracting the features of images. Here we have applied the statistical operation on
the preprocessed images After acquiring the image various preprocessing methods can be apply. The
aim of this step is to improve the quality of the image that suppress unwanted distortion and enhance
the image features which is important for further processing. Such as increase or decrease
brightness, shape, contrast, remove the noise from the image.
(3).Statistical analysis : Image analysis

Analysis : Automated Tissue Image Analysis
Topic1: image analysis
JIAN GAO 13050902
This report is about automated tissue image analysis, there are 5 parts in this article:
1. Introduction of image analysis
2. How image analysis be used in slide image of histology
3. What can be obtained from slide of diagnostic use
4. Discuss the advantages and disadvantages of image analysis
5. Conclusion
1. What is the image analysis
Histology is a microscopic study of organic tissue, is an important tool to diagnosis of cancer and
other diseases. The traditional method is artificial test, which needs to make a tissue slide and
obtaining under a microscope by naked eyes, for this method, the processing of analysis is a
monotonous and long work, and there are unavoidable artificial errors. So develop an automated
tissue image analysis is a very important study.
The history of development of automated image analysis technology: scientists has done the study
since 1920, start for application on 1960, the range of application expanded rapidly after 1970, and
nowadays: the application of image analysis technology in almost every fields of nature science. Of
course, Image analysis also can be used in medical science for histology tissue study().
Image analysis system is a digital technique, which consist of two parts: hardware and computer
software: the hardware includes are input device

Analysis Of Underwater Image For Future Requirement Using
Analysis of underwater image for future requirement using
Wavelet Transform analysis
Abstract:
Optical information is transmitted in the form of digital images is becoming a large method of
communication in the modern age but still the images reach after transmission is often depraved
with noises so the received images demand processing before it can be used in application. Our
motive is that to eliminate the noise from images that is underwater images also improve the image ,
underwater images consist of different kinds of noises like random noise, speckle noise, Gaussian
noise, salt and pepper noise, Brownian noise etc. Image De–noising is involved manipulation of
images data to produce a visually high quality, images processing of improving the quality of
images by enhancing its features. The underwater image processing area has accepted appreciable
attention within the last decades so using some proper kind of filter it is possible. The filter we will
employ is a bilateral filter for smoothing the images. It is required because of a lot researchers like
forensic department, argeologiest geologist, and underwater marine lab and underwater inside hydro
lab and so on, for their research activity. The underwater images have poor image condition. First it
uses some preprocessing methodology which is to be complete before wavelet threshold de–nosing.
Then it will use CLAHE method for image enhancement along with wavelet transform then we get
some adaptive output and the images

Essay On Homomorphic Filter
Abstract
In spite of the significant research conducted on multiplicative noise removal using homomorphic
filter, the development of efficient de–noising methods is still one of the most important tasks. Noise
effects badly on the signal. In many times signals are consolidated in a complicated way. Sending
visual digital images is one of the main problems that we face in modern data communication
network. Sometimes the image may not be received from the source by the receiver and it may get
interrupted with noise. To get high quality image we must reduce the noise in image which involves
the manipulation of the image data. For noise reduction we have various solutions are available. We
need to design a filter that will handle most of the ... Show more content on Helpwriting.net ...
Content
List of figures................................................................................................
Abstract.........................................................................................................
Introduction...................................................................................................
Operation......................................................................................................
Results...........................................................................................................
Conclusion.....................................................................................................
References.....................................................................................................
Introduction
Chapter 1:
Image processing:
Image processing is a signal processing where it's input signal is image. In image Processing system
we treat the images as 2D signals. We have two types of image processing which is digital and
analog. Analogue image processing used in hard copies while digital image processing use
computers for the manipulation of the digital images. Digital image processing have many types like
binary, RGB and grayscale.
Chapter 2:
Noise:
Noise is a random signal which affects badly on the wanted signal. Due to noise the signal may not

Image Processing Essay
Abstract: – A Measurement is must before going to the further calculations in various fields of work
or study. In order to find out something we definitely need some calculations. In different sectors,
determining exact size and shape are progressively becoming an issue and based on that the latency
is going up. As we cannot measure everything with a scale or a tape, we use some optical methods
of Image Processing. In this paper, we present an approach that can be used to determine the lengths
and some other degrees of measurements like diameter, spline, Caliper(perpendicular angle) etc. We
used mostly the Image Processing techniques because all the measurements are done on an Image.
We also use some other techniques like Euclidean ... Show more content on Helpwriting.net ...
The image can be enhanced to mark down the accurate end points. It actually can mark the end of a
single pixel which is almost invisible as a single pixel to the naked eye. A set of operations need to
be carried out respectively to achieve this. Initially the image need to be acquired and smoothened to
mark the pixel actually need to be. Then the neighborhood pixels collision should be eliminated
followed by the image segmentation. Finally, using the Euclidean algorithm the exact length can be
found.
II. IMAGE AQUSITION AND SMOOTHING: –
In Image Processing mostly the initial step will be the Image acquisition and smoothing. As the
input for the tool of any Image Processing technique is an image, the input image should be taken
and enhanced in all the ways possible. Enhancement involves smoothing the image, grey scaling,
removing the unwanted blur, differentiating the subject from background and so on. In this project,
for enhancing or smoothing the image we use the median filter. The median filter is non–linear
digital filtering technique where the noise reduction is the pre–processing step before going to the
further processing. Because the signal is big in the case of images, we chose median filter as it can
handle the larger signal and the run–time is literally less. The major advantage of the median filter is
the edge preservation. It processes each signal individually and replaces the edges of the pixel with

Types of Image Compression for Medical Imaging Essay
Medical imaging, as we all know, is the process of taking images of various parts of the human body
for diagnostic and surgical purposes. Some of the popular medical imaging modalities are X–ray
radiography, Magnetic resonance imaging, Medical ultrasound, Computed tomography etc. Since,
these images contain clinical data of extreme importance for treatment follow–ups and are acquired
at cost of radiation exposure, infrastructure, money and time involved. Thus, once acquired, the
medical imaging data should not be disposed off casually, instead it should be retained so that it can
be utilized for various medical applications and the chances of repeated testing can be minimized.
Also, maintaining electronic health records of patients serves ... Show more content on
Helpwriting.net ...
In other words an optimal compression ratio should be chosen so as to suit the needs of medical
examination, without compromising with its diagnostic value [2].
1.2 Types of Compression
Image compression can be classified into two types viz. lossless and lossy compression.
Lossless compression is the technique of reducing the size of an image without any virtual loss of
information. It is also known as reversible form of image compression since the image obtained
after compression and then decompression resembles the original one. Typical compression ratios
that can be achieved ranges from 1.5 to 3.6 [3].
Conversely, lossy or irreversible form of compression techniques are those in which some or the
other information is always lost. Though, lossy compression algorithms are capable of compressing
images at ratios much higher than that achieved from lossless compression thus, ensuring faster
rates of transmission and lesser storage space. However, the regenerated image is not guaranteed to
be an exact replica of the original image, as some data is lost permanently, which will cause error
during decompression. Typical compression ratios achieved may range from 5 to 50.
Though lossy data compression is often acceptable but the game is not that easy when it comes to
medical images. The data from medical imaging examination should possess certain requirements
for fidelity [3].
1.3 Barriers to image compression
Lossy compression:

Review On Fruit Disease Detection Using Color, Texture...
Review on Fruit Disease Detection Using Color, Texture Analysis and ANN with E–nose
Shalaka Koske Minal Bhalgat
Computer Engineering Computer Engineering
DYPSOE, Pune, DYPSOE, Pune,
Maharashtra, India. Maharashtra, India.
Pratiksha Kale Neha Mundokar
Computer Engineering Computer Engineering
DYPSOE, Pune , DYPSOE, Pune ,
Maharashtra, India. Maharashtra, India.
Prof. Yogesh A Thorat
Assistant Professor,
DYPSOE, Pune,
Maharashtra, India.
Abstract:
In agricultural industry, along with vegetables, fruit production also plays a vital role. For better
yield of fruit, detection of fruit diseases at early stage is necessary for taking preventive measures,
so as to reduce the loss of farmer. For detecting the disease an earlier approach was to hire an expert
which was time consuming for large farms, hence to reduce human efforts and to improve the yield
of fruits we are proposing a system which includes smart farming technique .In the proposed system
image processing is used for getting the required output, we are using Open Cv library which is an
image processing software. Images are classified and mapped to respective diseases on basis of
following features: color, texture, morphology, structure of hole and odour. E–NOSE is used which
is a

Incidence Rate Of Skin Cancer
Abstract: Incidence rate of skin cancer are increasing day by day. Skin cancer is one of the deadliest
forms of cancer but detected earlier can save the life time of the human being. An automated
screening system is introduced to identify the presence of skin cancer in advance. In this paper,
texture distinctiveness lesion segmentation algorithm is used. Experience and training–based
characteristics of back propagation neural network is used with texture distinctiveness lesion
segmentation algorithm, for identifying the normal and abnormal portions of skin .The most
commonly occurring skin cancers are Melanoma, Basal and squamous cell carcinoma and actinic
keratosis. The proposed system is to diagnose the presence of these skin cancers with high
segmentation accuracy.
Keywords: Melanoma, segmentation, skin cancer, texture, neural network.
1. INTRODUCTION
Cancer is a life threatening disease caused primarily by genetic instability and accumulation of
multiple molecular alternations [1] [2].Present diagnostic and prognostic classifications are
insufficient to make prediction for successful treatment and patient outcome [3] [4].Among many
types of cancer, Skin cancers are the most common form of cancers in human [5]. The common
types of skin cancers are melanoma, basal and squamous cell carcinoma, and Actinic Keratosis
[6].Digital Dermoscopy is widely considered as one of the most cost effective method to identify
and classify skin–cancer. The rate of detection of melanoma

Chapter 1: Camera Modeling And Computer Video
CHAPTER (5)
CAMERA MODELING AND COMPUTER VISION Introduction
As mentioned before the computer vision role in this study is to identify and locate the desired parts
on the system's conveyor. Fig. (5.1) shows the block diagram for this process. Fig. (5.1) computer
vision block diagram
The camera streaming a real time video to the vision algorithm. MATLAB/SIMULINK of
MathWorks–Company is used to analysis the video streaming and detect the parts position in pixels.
The camera model and camera calibration equations then transform the pixel positions to a real
world (x, y) position related to the robot reference coordinate (home position). Robot inverse
kinematic equations take the (x, y) positions and convert them to a number of steps to ... Show more
content on Helpwriting.net ...
The field of digital image processing refers to processing digital images by means of a digital
computer [11]. Image coordinates
Assume that an image f(x,y) is sampled so that the resulting image has M rows and N columns so,
the image size is M x N. The values of the coordinates are discrete quantities. The image origin is
usually defined to be at (x, y) = (0, 0). The next coordinate values along the first row of the image
are (x, y) = (0, 1). The notation (0, 1) is used to signify the second sample along the first row. It does
not mean that these are the actual values of physical coordinates when the image was sampled. Fig.
(5.3) shows this coordinate convention, where x ranges from 0 to (M–1) and y from 0 to (N–1) in
integer increments [11].
Equation (5.1) represents the digital image with respect to the image coordinate system discussed
above [11]. f(x,y)=[■(■(f(0,0)@f(1,0))&■(f(0,1)@f(1,1))&■(f(0,N–1)@f(0,N–1))@⋯&⋯&⋯
@f(M–1,0)&f(M–1,1)&f(M–1,N–1))] (5.1)
Fig. (5.3) Digital image coordinate conventions [11].
Both sides of this equation are equivalent ways of expressing a digital image quantitatively. The
right side is a matrix of real numbers. Each element of this matrix is called an image element,
picture element, or pixel. The term pixel is used throughout the rest of this study [11]. Camera
Modeling
Introduction
In this section the basic camera model is developed based on [12].as a

The Advantages And Disadvantages Of Digital Radiography
Digital radiography (DR) is a revolutionary invention in radiography. With this technology, no
cassette is needed for an x–ray examination meaning that there is no need to reload films or to erase
imaging plate in every examination. This is a distinctive feature which conventional radiography
and computed radiography (CR) do not have. DR was first introduced in 1996 (Carroll, 2011).
Miniature electronic x–ray detectors are used as the image receptor. The detectors enable the direct
capture of the x–ray image without conversion steps (like the conversion of x–ray photos into light
photons). This technology is widely used nowadays since it has many advantages and it brings much
convenience to radiographers. One of the main advantages of DR is image post–processing in which
the quality of the film (in terms of contrast and brightness, etc.) can be adjusted to reach the desired
standard. Therefore, the tolerance of the deviation of the exposure factors is greater and the need of
repeating the examination is greatly reduced so the patient dose is reduced. This follows the as low
as reasonably achievable principle for radiation protection and this also improve the final image
quality simultaneously. Besides, many DR systems were installed with preset for numerous
anatomical studies which can improve the post processing. Like CR, the images produced are in
digital format so this provides convenience for radiographers to store and retrieve the image easily.
DR is also capable to work with PACS ... Show more content on Helpwriting.net ...
There are three main components of DR system. They are imaging system, image processing system
and image communication& archiving system.
1) Imaging

The Human Face Action Recognition System
Abstract– In this paper we implement the Human Face Action Recognition System in Wireless
Sensor Network. Detecting movements of human is one of the key applications of wireless sensor
networks. Existing technique is detecting movements of a target using face tracking in wireless
sensor network work efficiently but here we implementing face action recognition system by using
image processing and algorithms with sensors nodes. Using sensor node we can collect the
information, data about human facial expressions and movements of human body and comparing old
data captured by sensors to the new capturing data, if data is match then we can say that detecting
human is same as early. Here we create new framework for face tracking and its movements
capturing, achieve tracking ability with high accuracy using Wireless Sensor
Networks. We use the Edge Detection Algorithms, Optimal Selection Algorithm, Image Processing
Technique, Action Recognition, the big data analysis. Using java language, various types of sensors.
Keywords– Mobile Network, Ad–hoc Network, Routing Protocol, Sensor Networks, Surveillance
system, Pattern Recognition.
I. Introduction Face Recognition is a technology to extract facial features by computer and a
technique for authentication according to the characteristics of these features. Face Recognition

Ultrasound Images Of The Patients Suffering From...
Abstract–This paper presents the approach to analyze the ultrasound images of the patients suffering
from Cholelithiasis. The occurrence of Cholelithiasis is the commonest biliary disease to be reported
in India. Our research is aimed to apply the potential of image processing in diagnosing the presence
of gall bladder stones. In this paper we propose a technique, a combination of preprocessing
morphological techniques and Entropy calculation of the pixels representing gallstones in the gall
bladder.
Keywords–Cholelithiasis, entropy calculation, image processing, morphological techniques,
preprocessing
INTRODUCTION
Gallstone diseases are one of the most common biliary diseases, demanding a great progress in
understanding the gallstones. The historical background of Cholelithiasis helps the researchers for
easy classification of Gallstones. According to Japanese, there are two types of Gallstones are
widely discussed: the Cholesterol stone, which is further of three types, the Pure Cholesterol stone,
the Combination stone and the Mixed stone. Second is the Pigment stone, which is further classified
as the Black stone and the Calcium Bilirubinate stone. The division line between Cholesterol and the
pigment stones depends upon the proportion of Cholesterol. If the proportion of cholesterol is equal
to or more than 70% then the stone is a Cholesterol stone; otherwise the stone is a pigment stone
with calcium bilirubinate as its principal constituent. The purpose of this

Image And Image Of Image Enhancement
CHAPTER 1
INTRODUCTION
Image processing refers to the construction of an image for further analysis and use. Image taken by
a camera or same techniques are not actual in a form that can be used by image analysis process.
The technique involves in image enhancement need to be simplified, enhanced, filtered, altered,
segmented or need improvement to reducing noise, etc. Image processing is the collection of
routines and techniques that alter, improve, enhance or simplify an image. Image enhancement is
one of the important parts of digital image processing where image undergo for visual inspection or
for machine analysis without knowledge of its source of degradation. The processes involve to bring
out specific application of an image so that the result is more suitable that the original image. Image
can be enhanced in various ways such as contrast enhancement, intensity, density slicing, edge
enhancement, removal of noise, and saturation transformation.[1]
Over several past years, contrast image enhancement has generated across many applications like
robot sensing, electronic products, fault detection, medical image analysis, etc. Thus, increasing in
popularity of contrast enhancement of images has forces researchers to study their enhancement
techniques and their effectiveness for the interpretability or perception of human viewers. Contrast
enhancement is a vital part of various fields, such as X–ray image analysis, biomedical image
analysis, machine vision where pixel

Blood Count Literature Review
REVIEW ON IMAGE PROCESSING USED IN HAEMOTOLOGY
Abstract– In medical analysis blood cell count plays vital role. Variations in the count of blood cells
cause many diseases in the human body. For overall health assessment and diagnosis of many
disorders complete blood count is required. Abnormal increase or decrease in cell count indicates
that person has indispensable medical condition. The Complete Blood Count (CBC) is a blood test,
extensively used to check various disorders such as infections, allergies, problems with clotting,
anaemia, leukaemia etc. In order to perform CBC test, the blood film is stained and then imaged
with a transmission light microscope. Here the analysis of the blood sample is done manually in
order to count number of blood cells and also to identify disorders in blood samples through a
microscope. But it is a time consuming process and also leads to undesirable human error. In
essence, the goal of this review paper is to find out and validate the necessary image processing
steps and different methods and algorithms used to count blood cells on blood smear slides. This
paper aims to provide: mitigate problems posed by different conditions such as noisy and degraded
images; detect the overlapping cells; to differentiate RBCs ,WBCs and also platelets which are
present in a blood smear slide counting RBCs and WBCs and even platelets and also to detect the
disease related to blood.
INTRODUCTION
In early days microscopists have manually viewed

Optical And Analog Image Processing
In imaging science, image processing is processing of images using mathematical operations by
using any conformation of signal processing for which the input is an image, such as a picture or
video frame, the out turn of image processing may be either an image or a set of features or
parameters corrsponding to the image.Most image–processing techniques implicate treating the
image as a 2D signal and appealing worth signal–processing techniques to it.
Image processing usually refers to digital image processing, but optical and analog image processing
also are possible. This article is about general techniques that apply to all of them. The acquisition of
images (producing the input image in the first place) is referred to as imaging.
Closely related to image processing are computer graphics and computer vision. In computer
graphics, images are manually made from physical models of objects, environments, and lighting,
instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most
animated movies. Computer vision, on the other hand, is often considered high–level image
processing out of which a machine/computer/software intends to decipher the physical contents of
an image or a sequence of images (e.g., videos or 3D full–body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever growing
importance of scientific visualization (of often large–scale complex scientific/experimental

Data Processing : Image Processing
1
1. INTRODUCTION
1.1. Introduction to broad area of research
1.1.1. Image processing: Image processing is a methodology to perform some operations on an
image, so as to urge an enhanced image or to extract some helpful data from it. It is treated as an
area of signal processing where both the input and output signals are images. Images are portrayed
as two dimensional matrix, and we are applying already having signal processing strategies to input
matrix. Images processing finds applications in several fields like photography, satellite imaging,
medical imaging, and image compression, just to name a few. Basically Image processing includes
the following steps:  Reading the image via image acquisition tools like cameras, caners etc. 
Analysing and manipulating the acquired image to have enhanced quality and locate the data of
interest;  Output in which result can be altered image or report that is based on image analysis.
Originally image processing is proposed for space exploration and biomedical field. But later on
with the increase in use of digital images in everybody's lives it considered as powerful tool for
arbitrarily manipulating images to gain useful information. It defined as the means of conversion
between human visual system and digital imaging devices.The main purpose of image processing
are listed below: 1. Visualization – Observe the objects which are not visible. 2. Image sharpening
and restoration – To increase quality of image. 3. Image retrieval –

The Image Of Image Processing
1. INTRODUCTION
1.1. Introduction to broad area of research
Image processing:
Image processing is a methodology to perform some operations on an image, so as to urge an
enhanced image or to extract some helpful data from it. It is treated as an area of signal processing
where both the input and output signals are images. Images are portrayed as two dimensional
matrix, and we are applying already having signal processing strategies to input matrix. Images
processing finds applications in several fields like photography, satellite imaging, medical imaging,
and image compression, just to name a few. Basically Image processing includes the following
steps:
Reading the image via image acquisition tools like cameras, caners etc.
Analysing and manipulating the acquired image to have enhanced quality and locate the data of
interest;
Output in which result can be altered image or report that is based on image analysis.
Originally image processing is proposed for space exploration and biomedical field. But later on
with the increase in use of digital images in everybody's lives it considered as powerful tool for
arbitrarily manipulating images to gain useful information. It defined as the means of conversion
between human visual system and digital imaging devices.The main purpose of image processing
are listed below:
1. Visualization – Observe the objects which are not visible.
2. Image sharpening and restoration – To increase quality of image.
3. Image retrieval – finding

A Literature Study Of Watermarking Techniques On Contrast...
A LITERATURE STUDY OF WATERMARKING TECHNIQUES ON CONTRAST
ENHANCEMENT OF COLOR IMAGES Rajendra Kumar Mehra1, Amit Mishra2 1. Dept of ECE,
M–TECH student, VITS, JABALPUR, M.P., INDIA, 2. Dept of ECE, H.O.D., VITS, JABALPUR,
M.P., INDIA. ABSTRACT: In this paper a watermarking method with contrast enhancement is
presented for digital images. Digital Watermarking is a technology which is used to identify the
owner, distributor of a given image. If the watermarked images is low contrast & poor visual quality
or due to poor illumination in some imaging system, the contrasts of the obtained images are often
needs to be improve. In recent years, digital watermarking plays a vital role in providing the
appropriate solution and various researches have been carried out. In this paper, an extensive review
of the literature related to the color image watermarking is presented together with contrast
enhancement by utilizing an assortment of techniques. This method outperforms other present
algorithm by enhancing the contrast of images well without introducing undesirable artifacts.
KEYWORDS: Watermarking, Histogram equalization, CLAHE, CAHE, PSNR, MSE. I.
INTRODUCTION DIGITAL image watermarking has become a necessity in many applications
such as data authentication, broadcast monitoring on the Internet and ownership identification.
Various watermarking schemes have been proposed to protect the copyright information. There are
three indispensable, yet contrasting requirements for a

A Short Note On Diabetic Retinopathy ( Dr ) Is The...
Abstract– Diabetic Retinopathy (DR) is the deterioration of human eye as a result of increase in the
blood glucose level. Longer the patient has DR, higher the chance to develop purblind. The robust
detection of lesions in digital colour fundus images is an important step in the development of
automated screening system for diabetic retinopathy. In this work a novel method is introduced for
automatic detection of red lesions in the fundus image. A new set of shape features extracted from
the detected red lesion called the dynamic shape features that differentiate between the lesions and
vessel segments. The detected lesion candidates are classified using dynamic shape features based
on the medical values. The simulation analysis indicates that the proposed work is better than the
previous works in terms of accuracy, sensitivity, precision and specificity.
Keywords: Diabetic retinopathy, Fundus, Lesions, Dynamic shape features, Retina
Introduction
Diabetic Retinopathy (DR) affects the diabetic patients. Generally diabetics are of three types Type
I, II and III. The Type I diabetic is due to the genetic predisposition, Type II diabetic which usually
affects the adults. This is owing to over weight of children beyond their age limit and Type III is
seen only in pregnant women. The patients with Type I diabetics will only suffer from DR which
influence the retina. This leads the way to damage of retina and finally blindness.
DR is caused by red lesion which is composed of

Design Of Image Capture, Display, Colour Processing And...
INTRODUCTION
Aim: Throughout this laboratory we aimed to understand the processes used to achieve the
development of image capture, display, colour processing and finally object tracking. In particular,
we aim to learn the I2C protocols to program the registers used to configure the camera, how to
convert a raw image to a full colour image, detect a selected colour and then track it.
Block Diagrams and images for the image processing steps:
The block diagram in Figure 1, illustrates the processing blocks that were created to being the image
processing steps. It also shows the variables created in the code and how they interact to produce the
initial output of display an image from the camera to the screen. The clock for the 640x480 (frame
size 800x525) display image runs at a frequency of 25.2 MHz and the clock for the camera runs at a
frequency of 48.825 MHz to synchronize the display. The I2C setup, involves using I2C protocols to
program registers within the camera. It is a two wire protocol, where one wire acts as the clock to
pass from the FPGA to the device, and the other wire is the data wire which is bidirectional. The
data wire is a top level entity and requires the setup module to have 3 data connections. These are
input data from the camera to the controller, output data from the FPGA controller to the camera and
output enable (tristate control), which determines whether the data is input or output.
Producing the image on the VGA display, involves using

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