Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Content Based Image Retrieval : Classification Using Neural Networksijma
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
An implementation of novel genetic based clustering algorithm for color image...TELKOMNIKA JOURNAL
The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.
RFID Based Warehouse Management of Perishable ProductsIOSR Journals
Abstract: The changing global market scenario, high level of competition, and faster obsolescence of
perishable products due to short self life and changing customer demand are among the key challenges faced by
perishable supply chains. Supply chains need to compete with growing variety of products, short delivery time,
higher cycle service level, high quality and lower cost. Perishability imposes an intense pressure on managers
as it adds an additional cost of disposal of outdated items, leading to out-of-stock situations and also loss of
company faith. These problems are arising mostly due to the lack of information at every stage of the supply
chains. The inadequate information about product quality, quantity, demand variability, product availability
and lead times creates the bullwhip effect (BWE) at all stages and chains leads to the mismanagement.
RFID technologies, with the appropriate IT infrastructure, would help major distributors and manufacturers, as
well as health-care system, defence industries, and global supply chains in which products and product
shipments must be traced and identified in a non-contact, wireless fashion using a computer network. This
paper presents the impact of RFID in warehouse management of perishable products. It provides mathematical
framework to asses the benefits of RFID in warehouse management. It helps management in the variety of ways
including improvement in receiving and shipping processes, reduction in cycle counting efforts, reduction in
stock outs/excess inventory, decreased counterfeiting, decreased returns, and reduction in inventory loss due to
shrinkage and obsolescence. A sensitivity analysis has been presented at the end of paper which shows the
compound effect of RFID, reduction in lead time and lead time variability. In all scenarios inventory level is
reduced by certain percentage by incorporating RFID.
Keywords— Supply chain management, Perishable product, Inventory, RFID, Warehouse.
Application of Langmuir-Hinshelwood Model to Bioregeneration of Activated Car...IOSR Journals
Environmental pollution, high cost and high energy consumption associated with thermal regeneration of activated carbon polluted with hydrocarbon necessitated the search for a better way of regenerating activated carbon, bioregeneration. Spent granular activated carbon was regenerated having been initially characterized using cultured Pseudomonas Putida. The rate of bioregeneration was studied by varying the volume of bacteria from 10ml, 20ml, 30ml and 40ml. The regeneration temperature was also varied from 25oC to ambient temperature of 27oC, 35oC and further at 40 and 45oC over a period of 21 days. The experimental results showed clear correlation when validated using the Langmuir-Hinshelwood kinetic model. The experiment at ambient temperature showed a negative correlation due to the fluctuation in the ambient temperature unlike all other experiment where temperature was controlled in an autoclave machine.
Role of Virtual Machine Live Migration in Cloud Load BalancingIOSR Journals
Abstract: Cloud computing has touched almost every field of the life. Hence number of cloud application
consumers is increasing every day and so as the number of application request to the cloud provider. This leads
increment of workload in many of the cloud nodes. The motive to use load balancing concepts in cloud
environment is to efficiently utilize available resources keeping in mind that no any single system is heavily
loaded or not a single system is idle during the active phase of the request completion. Even though cloud
computing being a software facility most often, how does it actually performs well in heavily loaded
environment at processor level, is discussed in the paper. This paper aims to throw some light on what is cloud
load balancing and what is the role of Virtual machine migration in improving it.
Keywords: Cloud load balancing, Live Migration, Migration, Virtualization, Virtual machine.
Chitosan capped Silver nanoparticles used as Pressure sensorsIOSR Journals
In the present work, we report the synthesis and characterization of silver nanoparticles, capped with chitosan (biopolymer ). The majority of the particles produced in this way had sizes around 18 nm. Composite films of capped silver nanoparticles and chitosan polymer were studied to understand the charge transport under different pressure. Films of different compositions were prepared to measure current voltage curves across the film thickness. The results reveal that these materials exhibit electrical conductivity as predicted by the “classical theory of percolation”. Pressure dependent electrical conductivity and these composites can be explored to develop low cost pressure sensors.
COLOUR BASED IMAGE SEGMENTATION USING HYBRID KMEANS WITH WATERSHED SEGMENTATIONIAEME Publication
Image processing, arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. The objective of segmentation is partitioning an image into distinct regions containing each pixels with similar attributes. Image segmentation can be done using thresholding, color space segmentation, k-means clustering.
Segmentation is the low-level operation concerned with partitioning images by determining disjoint and homogeneous regions or, equivalently, by finding edges or boundaries. The homogeneous regions, or the edges, are supposed to correspond, actual objects, or parts of them, within the images. Thus, in a large number of applications in image processing and computer vision, segmentation plays a fundamental role as the first step before applying to images higher-level operations such as recognition, semantic interpretation, and representation. Until very recently, attention has been focused on segmentation of gray-level images since these have been the only kind of visual information that acquisition devices were able to take the computer resources to handle. Nowadays, color image has definitely displaced monochromatic information and computation power is no longer a limitation in processing large volumes of data. In this paper proposed hybrid k-means with watershed segmentation algorithm is used segment the images. Filtering techniques is used as noise filtration method to improve the results and PSNR, MSE performance parameters has been calculated and shows the level of accuracy
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Review on Image Segmentation using Clustering and Swarm Optimization Techni...IJSRD
The process of dividing an image into multiple regions (set of pixels) is known as Image segmentation. It will make an image easy and smooth to evaluate. Image segmentation objective is to generate image more simple and meaningful. In this paper present a survey on image segmentation general segmentation techniques, clustering algorithms and optimization methods. Also a study of different research also been presented. The latest research in each of image segmentation methods is presented in this study. This paper presents the recent research in biologically inspired swarm optimization techniques, including ant colony optimization algorithm, particle swarm optimization algorithm, artificial bee colony algorithm and their hybridizations, which are applied in several fields.
An Automatic Color Feature Vector Classification Based on Clustering MethodRSIS International
In computer vision application, visual features such as
shape, color and texture are extracted to characterize images.
Each of the features is represented using one or more feature
descriptors. One of the important requirements in image
retrieval, indexing, classification, clustering, etc. is extracting
efficient features from images. The color feature is one of the
most widely used visual features. Use of color histogram is the
most common way for representing color feature. One of
disadvantage of the color histogram is that it does not take the
color spatial distribution into consideration. In this paper an
automatic color feature vector classification based on clustering
approach is presented, which effectively describes the spatial
information of color features. The image retrieval results are
compare to improved color feature vector show the acceptable
efficiency of this approach. It propose an automatic color feature
vector classification of satellite images using clustering approach.
The intention is to study cluster a set of satellite images in several
categories on the color similarity basis. The images are processed
using LAB color space in the feature extraction stage. The
resulted color-based feature vectors are clustered using an
automatic unsupervised classification algorithm. Some
experiments based on the proposed recognition technique have
also been performed. More research, however, is needed to
identify and reduce uncertainties in the image processing chain
to improve classification accuracy. The mathematical training
and prediction analysis of a general familiarity with satellite
classifications meet typical map accuracy standards.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
An implementation of novel genetic based clustering algorithm for color image...TELKOMNIKA JOURNAL
The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.
RFID Based Warehouse Management of Perishable ProductsIOSR Journals
Abstract: The changing global market scenario, high level of competition, and faster obsolescence of
perishable products due to short self life and changing customer demand are among the key challenges faced by
perishable supply chains. Supply chains need to compete with growing variety of products, short delivery time,
higher cycle service level, high quality and lower cost. Perishability imposes an intense pressure on managers
as it adds an additional cost of disposal of outdated items, leading to out-of-stock situations and also loss of
company faith. These problems are arising mostly due to the lack of information at every stage of the supply
chains. The inadequate information about product quality, quantity, demand variability, product availability
and lead times creates the bullwhip effect (BWE) at all stages and chains leads to the mismanagement.
RFID technologies, with the appropriate IT infrastructure, would help major distributors and manufacturers, as
well as health-care system, defence industries, and global supply chains in which products and product
shipments must be traced and identified in a non-contact, wireless fashion using a computer network. This
paper presents the impact of RFID in warehouse management of perishable products. It provides mathematical
framework to asses the benefits of RFID in warehouse management. It helps management in the variety of ways
including improvement in receiving and shipping processes, reduction in cycle counting efforts, reduction in
stock outs/excess inventory, decreased counterfeiting, decreased returns, and reduction in inventory loss due to
shrinkage and obsolescence. A sensitivity analysis has been presented at the end of paper which shows the
compound effect of RFID, reduction in lead time and lead time variability. In all scenarios inventory level is
reduced by certain percentage by incorporating RFID.
Keywords— Supply chain management, Perishable product, Inventory, RFID, Warehouse.
Application of Langmuir-Hinshelwood Model to Bioregeneration of Activated Car...IOSR Journals
Environmental pollution, high cost and high energy consumption associated with thermal regeneration of activated carbon polluted with hydrocarbon necessitated the search for a better way of regenerating activated carbon, bioregeneration. Spent granular activated carbon was regenerated having been initially characterized using cultured Pseudomonas Putida. The rate of bioregeneration was studied by varying the volume of bacteria from 10ml, 20ml, 30ml and 40ml. The regeneration temperature was also varied from 25oC to ambient temperature of 27oC, 35oC and further at 40 and 45oC over a period of 21 days. The experimental results showed clear correlation when validated using the Langmuir-Hinshelwood kinetic model. The experiment at ambient temperature showed a negative correlation due to the fluctuation in the ambient temperature unlike all other experiment where temperature was controlled in an autoclave machine.
Role of Virtual Machine Live Migration in Cloud Load BalancingIOSR Journals
Abstract: Cloud computing has touched almost every field of the life. Hence number of cloud application
consumers is increasing every day and so as the number of application request to the cloud provider. This leads
increment of workload in many of the cloud nodes. The motive to use load balancing concepts in cloud
environment is to efficiently utilize available resources keeping in mind that no any single system is heavily
loaded or not a single system is idle during the active phase of the request completion. Even though cloud
computing being a software facility most often, how does it actually performs well in heavily loaded
environment at processor level, is discussed in the paper. This paper aims to throw some light on what is cloud
load balancing and what is the role of Virtual machine migration in improving it.
Keywords: Cloud load balancing, Live Migration, Migration, Virtualization, Virtual machine.
Chitosan capped Silver nanoparticles used as Pressure sensorsIOSR Journals
In the present work, we report the synthesis and characterization of silver nanoparticles, capped with chitosan (biopolymer ). The majority of the particles produced in this way had sizes around 18 nm. Composite films of capped silver nanoparticles and chitosan polymer were studied to understand the charge transport under different pressure. Films of different compositions were prepared to measure current voltage curves across the film thickness. The results reveal that these materials exhibit electrical conductivity as predicted by the “classical theory of percolation”. Pressure dependent electrical conductivity and these composites can be explored to develop low cost pressure sensors.
Some properties of two-fuzzy Nor med spacesIOSR Journals
The study sheds light on the two-fuzzy normed space concentrating on some of their properties like convergence, continuity and the in order to study the relationship between these spaces
On Hadamard Product of P-Valent Functions with Alternating TypeIOSR Journals
Padmanabhan and Ganeshan have obtained some results on Hadamard product of univalent functions with negative coefficients of the type f (z) z a z , a 0. 2n n 1 2n 2n In this paper we have obtained coefficient bounds and convolution results of p-valent function
Clustering Algorithm Based On Correlation Preserving IndexingIOSR Journals
Abstract: Fast retrieval of the relevant information from the databases has always been a significant issue.
Different techniques have been developed for this purpose; one of them is Data Clustering. In this paper Data
Clustering is discussed along with the applications of Data Clustering and Correlation Preserving Indexing. We
proposed a CPI (Correlation Preserving Indexing) algorithm and relate it to structural differences between the
data sets.
Keywords: Data Clustering, Data Mining, Clustering techniques, Correlation Preserving Indexing.
“An analytical study on medal tally of top ten countries in 2012 Olympic Game...IOSR Journals
The origin of the ancient Olympic Games is lost the midst of pre-history, but for many centuries they
were only a festival of the Greek people. The Games were first held in honour of the Greek God, Zeus in 776
BC in the plain of kingdom of Elis, nestled in lush valley between the Alpheus River and Mount Kronion, 15
km from the Ionian Sea. The Olympiad celebrated that year was considered as the first and was used to date
subsequent historic events.
Particulate Sintering of Iron Ore and Empirical Analysis of Sintering Time Ba...IOSR Journals
Particulate sintering of iron ore has been carried out using the necessary ingredients. Empirical
analysis of the sintering time based on the coke breeze input concentration and ignition temperature were also
successfully obtained through first principle application of a derived model which functioned as a evaluative
tool. The derived model;
S = (√T)0.95 + 0.0012α
indicates that amongst ignition temperature and coke breeze input, sintering time is more significantly affected
by the coke breeze input concentration. This is based on the higher correlation it makes with sintering time
compared to applied ignition temperature, all other process parameters being constant. The validity of the
model was rooted in the core expression S – Kα ≈ (√T )N where both sides of the expression are correspondingly
approximately almost equal. Sintering time per unit rise in the operated ignition temperature as obtained from
experiment, derived model and regression model were evaluated as 0.0169, 0.0128 and 0.0159 mins. / 0C
respectively. Similarly, sintering time per unit coke breeze input concentration as obtained from experiment,
derived model and regression model were evaluated as 4.0, 3.0183 and 3.7537 mins./ % respectively indicating a
significant proximate agreement and validity of the model. The standard error (STEYX) incurred in predicting
sintering time for each value of the ignition temperature and coke breeze input concentration considered, as
obtained from the experiment, derived model and regression model are 1.6646, 0.7678 and 2.98 x10-5 % as well
as 2.2128, 1.0264 and 1.2379% respectively. The maximum deviation of mode-predicted results from the
corresponding experimental values was less than 11%.
Synthesis of Optimized Asymmetrical Sum Patterns Using Conventional MethodIOSR Journals
Abstract: Sum patterns find applications in Radar for searching and ranging of the targets. A sum pattern with low sidelobe level is a desirable feature in these applications, in order to reduce EMI problems. Sum patterns with Asymmetrical sidelobe topography are considered, in applications where only certain angular regions of pattern are required to have low sidelobe level. Asymmetrical pattern characteristics can have lower beam widths for given design specifications as compared to symmetrical patterns. In view of this, a conventional method of synthesis is carried out in this paper, to produce asymmetrical sidelobe level patterns using discrete arrays. The effect of beam scanning on the pattern behavior is also analyzed for the above synthesized patterns. Keywords: sum pattern, asymmetrical sidelobe level, beam width, complex excitation weights, and discrete array.
Design of Ball Screw Mechanism for Retro Fit of External Grinding MachineIOSR Journals
Abstract: To convert the existing grinding machine into a good working machine, a ball screw mechanism is
designed and incorporated in the existing grinding machine by retrofitting process. Grinding machine removes
material from the work piece by abrasion, which can generate substantial amount of heat. Therefore a coolant
System is incorporated to cool the work piece, so that it does not overheat and go out of its tolerance. Grinding
practice is a large and diverse area of manufacturing and tool making. It can also rough out large volume of
metal quite rapidly. It is usually better suited to the machining of hard materials. Cylindrical grinding is also
called center type grinding. It is used in the removing the cylindrical surface and shoulders of the work piece.
The five type of cylindrical grinding are outside diameter (OD) grinding. Inside diameter (ID) grinding, plunge
grinding, creep feed grinding and center less grinding.It is used in the industries for grinding the nozzle body.
The grinding machine can be changed to automatic machine according to one of the latest technologies called
PLC’s controller. The manually operated grinding machine has some of the in-accuracies and disadvantages
compared to new modern CNC grinding machines.Based on the case study of both manual and CNC grinding
machine, the manual machine is converted into automatic machine for the better accuracy and efficiency. The
main replacement of the machine parts are hydraulic cylinder and stepper motor by ball screw mechanisms and
servo motor.
A Novel Approach for Examination of Visually Challenged Candidates by E-Evalu...IOSR Journals
ABSTRACT : The evaluation of physically challenged is always a challenging task as any evaluation of them
is compared with that of normal candidates. The case of visually challenged is still more difficult as vision is a
nunerouno sensor in the field of study and knowledge enhancement and evaluation of them on par with other
candidate is very difficult. The aim of this paper is to present an approach for E-evaluation model for the
visually challenged students/candidates for the screening tests conducted by the different examination
authorities. The major attempt is made to use the personal computer and avoid the use of a scriber by the
candidate so that candidate can take the exam independently. A portion of the PC keyboard is slightly modified
in its software functionality to help them in undergoing the test. Also described is the functioning of this model
of E-Evaluation and its relative advantages.
Keywords: E-evaluation, PC keyboard, visually challenged candidate
A Novel Interface to a Web Crawler using VB.NET TechnologyIOSR Journals
Abstract : The number of web pages is increasing into millions and trillions around the world. To make
searching much easier for users, web search engines came into existence. Web Search engines are used to find
specific information on the World Wide Web. Without search engines, it would be almost impossible to locate
anything on the Web unless or until a specific URL address is known. This information is provided to search by
a web crawler which is a computer program or software. Web crawler is an essential component of search
engines, data mining and other Internet applications. Scheduling Web pages to be downloaded is an important
aspect of crawling. Previous research on Web crawl focused on optimizing either crawl speed or quality of the
Web pages downloaded. While both metrics are important, scheduling using one of them alone is insufficient
and can bias or hurt overall crawl process. This paper is all about design a new Web Crawler using VB.NET
Technology.
Keywords: Web Crawler, Visual Basic Technology, Crawler Interface, Uniform Resource Locator.
A Study on Mid-Career Blues with refers to Hyundai Motor India Limited, Irung...IOSR Journals
This research paper was attempted to determine the human resource management which focused on
all issues related to „people‟ in the organization. They are undoubtedly the most important assets. Therefore the
special care must be exercised for managing them. Human resource management is concerned with activities
involved in acquisition, development, motivation and maintenance of people. These are the important to achieve
the organizational goals. The aim of this research was to examine the blue formation in HMIL. The significance
of this research intended to help HMIL to know about the employee career prospects & also analyze this factor
which influence the organization productivity.100 samples were collected from employees (Middle level
employee) of different departments [such as “HR – Recruitment, Industrial & Employee Relation, Training &
Development”, “Body Shop”, “Paint Shop” & “Press”] in HMIL.
There is increased use of nuclear energy after the Second World War which results in increase in artificial radioactivity on our planet. The objective of this article is to show the estimated amount of artificial radioactivity on earth surface and its effect by comparing it with the radioactive decay which took place in Hiroshima bombing by ‘little boy’. There is estimated amount of 100 trillion curie of radioactivity on earth surface for human use. This man made radioactivity on earth surface has capacity to change temperature of the earth by 0.97oC if heat is evenly distributed and unfortunately Hiroshima bombing did not stop but continued at rate of 39 ‘little boy’ bombing of the earth per second. Every second 39 atomic bombs of ‘little boy’ size are dropped. One can see the large amount of bombing taking place on the earth by artificial radioactivity and the bombing should be stopped and further analysis of artificial radioactivity should also be done
Experimental and Analytical Investigation of Drilling of Sandwich Composites:...IOSR Journals
A composite material is made out of a mixture or a combination of two or more distinctly differing
materials which are insoluble in each other and differ in form or chemical composition. The technological and
commercial interest in composite material lies in their superior properties of strength-to-weight, stiffness-toweight,
fatigue and thermal expansion compared to metals. Extensive use of composite in application such as
rockets, satellites, missiles, light combat aircraft, advanced light helicopter and trainer air craft has shown that
India is on par with the advanced countries in the development and use of composites in this area.
Drilling is probably the most important conventional mechanical process and it is the most widely used
machining operation. Prediction of cutting forces for any set of cutting parameters is essential in optimal design
and manufacturing of products. It has been predicted that most of the problem associated with hole making
operation, such as drilling, can be attributed to the force generated during cutting operation. Many
developments and experiments are going on drilling of Sandwich composite for damage free drilling along with
the quality of the hole and the effect of tool geometry and tool material.
This paper aims at the comprehensive analytical and experimental investigation work done on the
composites material. The conclusion of the paper discusses the development and outlines the trends for the
research in this field.
Exploring Values and Value Streams by BPM method solved by Lean Management toolIOSR Journals
This paper suggests a continuous improvement plan that can satisfy customer’s value and eliminate
waste in the enterprise business process. In order to explore the applicability of lean management principles in
the enterprise business process, the five fundamental concepts (specify value, identify the value stream, flow,
pull, and perfection) of lean management are being used as a stable and proved approach. In addition, Business
Process Management is applied as a new method to constantly improve the elimination of waste in the
enterprise business process. This can be accomplished by the lean management concept. Moreover business
process problems, such as overlapping work, redoing work, communication gaps, inflexible processes, and
obscure processes, have the possibility of being solved by lean management.
Lean management has traditionally been adopted by manufacturing industries to improve operations through
the identification and elimination of all forms of waste basically. The construction industry has also adopted this
philosophy, primarily in the field of projects. In order to increase an organization’s competitiveness and
productivity, lean management is needed in the any business process as well as in the field. The intent of this
work is to explore a method of introducing lean management which continuously improves any business
processes. The five fundamental concepts (specify value, identify the value stream, flow, pull, and perfection) of
lean management as an approach are being adapted in this project to improve quality of the processes. Hence
the main objective of this paper to apply the tool of lean and six sigma management to improve the elimination
of waste in the enterprises business process. Followed by a literature review which provides a brief summary of
lean thinking and six sigma along with challenges might face while implementing. A case study follows that
demonstrates how; Business Process Management is applied as a tool, method to constantly improve the
business process.
Uniform particle distribution by a newer method in composite metal of Al/SiCIOSR Journals
Abstract: Preparation of composites of metal with ceramic particle reinforced through the casting process
is not uniform because of poor wet ability. The major difficulty is to get a uniform distribution of
reinforcement especially in higher volume fractions. An innovative method of producing cast composites is
tried in present study to overcome this problem we need homogeneity of matrix. The method involves multi
axis rotation of liquid aluminum and silicon carbide particulates packed in a steel pipe inside a rotating drum.
Up to 65 % volume of the metal (aluminum)is incorporated by SIC by this technique. Physical Properties
like hardness, micro hardness, densities and microstructures have been studied. The distribution of
particles as well the mechanical properties are better as compared to that of stir cast composites with similar
volume fraction of silicon carbide reinforcement. The composite with 65-volume percentage of silicon carbide
of particulates showed a Rockwell Hardness value of 67Rb.In few locations the microstructure showed a
non-uniform distribution which can be neglected . There were segregation of silicon carbide particles at a
particular location and the hardness obtained there was much higher. The particle distribution is a result
of the combined influence of random mixing of particles and liquid aluminum and the solidification pattern
obtained.
Key word: Multi axis rotation, microstructure, MMC, Al- SIC matrix
Heat Capacity of BN and GaN binary semiconductor under high Pressure-Temperat...IOSR Journals
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Review of Image Segmentation Techniques based on Region Merging ApproachEditor IJMTER
Image segmentation is an important task in computer vision and object recognition. Since
fully automatic image segmentation is usually very hard for natural images, interactive schemes with a
few simple user inputs are good solutions. In image segmentation the image is dividing into various
segments for processing images. The complexity of image content is a bigger challenge for carrying out
automatic image segmentation. On regions based scheme, the images are merged based on the similarity
criteria depending upon comparing the mean values of both the regions to be merged. So, the similar
regions are then merged and the dissimilar regions are merged together.
Feature Extraction for Image Classification and Analysis with Ant Colony Opti...sipij
The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.
Survey on Brain MRI Segmentation TechniquesEditor IJMTER
Image segmentation is aimed at cutting out, a ROI (Region of Interest) from an image. For
medical images, segmentation is done for: studying the anatomical structure, identifying ROI ie tumor
or any other abnormalities, identifying the increase in tissue volume in a region, treatment planning.
Currently there are many different algorithms available for image segmentation. This paper lists and
compares some of them. Each has their own advantages and limitations.
A Survey On: Content Based Image Retrieval Systems Using Clustering Technique...IJMIT JOURNAL
Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a days the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divide and conquer K Means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.
A Survey of Image Processing and Identification Techniquesvivatechijri
Image processing is always an interesting field as it gives enhanced visual data for human
simplification and processing of image data for transmission and illustration for machine preception. Digital
images are processed to give better solution using image processing. Techniques such as Gray scale
conversion, Image segmentation, Edge detection, Feature Extraction, Classification are used in image
processing.
In this paper studies of different image processing techniques and its methods has been conducted.
Image segmentation is the initial step in many image processing functions like Pattern recognition and image
analysis which convert an image into binary form and divide it into different regions. The technique used for
segmentation is Otsu’s method, K-means Clustering etc. For feature extraction feature vector in visual image is
texture, shape and color. Edge detector with morphological operator enhances the clarity of image and noise
free images. This paper also gives information about algorithm like Artificial Neural Network and Support
Vector Mechanism used for image classification. The image is categorized into the receptive class by an ANN
and SVM is used to compile all the categorized result. Overall the paper gives detail knowledge about the
techniques used for image processing and identification.
A comparative study on content based image retrieval methodsIJLT EMAS
Content-based image retrieval (CBIR) is a method of
finding images from a huge image database according to persons’
interests. Content-based here means that the search involves
analysis the actual content present in the image. As database of
images is growing daybyday, researchers/scholars are searching
for better techniques for retrieval of images maintaining good
efficiency. This paper presents the visual features and various
ways for image retrieval from the huge image database.
International Journal of Computer Science, Engineering and Information Techno...ijcseit
Object segmentation plays an important role in human visual perception, medical image processing and
content based image retrieval. It provides information for recognition and interpretation. This paper uses
mathematical morphology for image segmentation. Object segmentation is difficult because one usually
does not know a priori what type of object exists in an image, how many different shapes are there and
what regions the image has. To carryout discrimination and segmentation several innovative segmentation
methods, based on morphology are proposed. The present study proposes segmentation method based on
multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment
simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.
The method is tested on various datasets and results shows that it can be used for both interactive and
automatic segmentation.
OBJECT SEGMENTATION USING MULTISCALE MORPHOLOGICAL OPERATIONSijcseit
Object segmentation plays an important role in human visual perception, medical image processing and content based image retrieval. It provides information for recognition and interpretation. This paper uses mathematical morphology for image segmentation. Object segmentation is difficult because one usually does not know a priori what type of object exists in an image, how many different shapes are there and what regions the image has. To carryout discrimination and segmentation several innovative segmentation methods, based on morphology are proposed. The present study proposes segmentation method based on multiscale morphological reconstructions. Various sizes of structuring elements have been used to segment simple and complex shapes. It enhances local boundaries that may lead to improve segmentation accuracy.The method is tested on various datasets and results shows that it can be used for both interactive and automatic segmentation.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Semi-Supervised Method of Multiple Object Segmentation with a Region Labeling...sipij
Efficient and efficient multiple object segmentation is an important task in computer vision and object recognition. In this work; we address a method to effectively discover a user’s concept when multiple objects of interest are involved in content based image retrieval. The proposed method incorporate a framework for multiple object retrieval using semi-supervised method of similar region merging and flood fill which models the spatial and appearance relations among image pixels. To improve the effectiveness of similarity based region merging we propose a new similarity based object retrieval. The users only need to roughly indicate the after which steps desired objects contour is obtained during the automatic merging of similar regions. A novel similarity based region merging mechanism is proposed to guide the merging process with the help of mean shift technique and objects detection using region labeling and flood fill. A region R is merged with its adjacent regions Q if Q has highest similarity with Q (using Bhattacharyya descriptor) among all Q’s adjacent regions. The proposed method automatically merges the regions that are initially segmented through mean shift technique, and then effectively extracts the object contour by merging all similar regions. Extensive experiments are performed on 12 object classes (224 images total) show promising results.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
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An Unsupervised Cluster-based Image Retrieval Algorithm using Relevance FeedbackIJMIT JOURNAL
Content-based image retrieval (CBIR) systems utilize low level query image feature as identifying similarity between a query image and the image database. Image contents are plays significant role for image retrieval. There are three fundamental bases for content-based image retrieval, i.e. visual feature extraction, multidimensional indexing, and retrieval system design. Each image has three contents such as: color, texture and shape features. Color and texture both plays important image visual features used in Content-Based Image Retrieval to improve results. Color histogram and texture features have potential to retrieve similar images on the basis of their properties. As the feature extracted from a query is low level, it is extremely difficult for user to provide an appropriate example in based query. To overcome these problems and reach higher accuracy in CBIR system, providing user with relevance feedback is famous for provide promising solutio
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A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 3 (Nov. - Dec. 2013), PP 71-78
www.iosrjournals.org
www.iosrjournals.org 71 | Page
A Survey of Image Segmentation based on Artificial Intelligence
and Evolutionary Approach
Varshali Jaiswal1,
Aruna Tiwari2
1 Department of Computer Science and Engineering SGSITS, Indore, MP, India
2 Department of Computer Science and Engineering IIT Indore, Indore, MP, India
Abstract : In image analysis, segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
types of images and the quality of output of a particular algorithm is difficult to measure quantitatively due to
the fact that there may be much correct segmentation for a single image. Image segmentation denotes a process
by which a raw input image is partitioned into nonoverlapping regions such that each region is homogeneous
and the union of any two adjacent regions is heterogeneous. A segmented image is considered to be the highest
domain-independent abstraction of an input image. Image segmentation is an important processing step in many
image, video and computer vision applications. Extensive research has been done in creating many different
approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm
produces more accurate segmentations than another, whether it be for a particular image or set of images, or
more generally, for a whole class of images.
In this paper, The Survey of Image Segmentation using Artificial Intelligence and Evolutionary Approach
methods that have been proposed in the literature. The rest of the paper is organized as follows. 1.
Introduction, 2.Literature review, 3.Noteworthy contributions in the field of proposed work, 4.Proposed
Methodology, 5.Expected outcome of the proposed research work, 6.Conclusion.
Keywords: Image Segmentation, Segmentation Algorithm, Artificial Intelligence, Evolutionary Algorithm,
Neural Network, Fuzzy Set, Clustering.
I. Introduction
Digital image processing is one of most important area of research and has opened new research
prospects in this field. Digital image processing refers to processing digital image by means of digital computer.
Image processing [23] is a very profound key that can change the outlook of many designs and proposals.
Fundamental steps in digital image processing are image acquisition, image enhancement, image restoration,
color image processing, compression, image segmentation and recognition. Image segmentation [9] has become
a very important task in today’s scenario. An importance of segmentation is, segmentation is generally the first
stage in any attempt to analyze or interpret an image automatically. Segmentation provides bridges the gap
between low-level image processing and high-level image processing. The any application involving the
detection, recognition, and measurement of objects in images make use of segmentation techniques like.
Application area of Segmentation is include, Industrial inspection, Optical character recognition (OCR),
Tracking of objects in a sequence of images, Classification of terrains visible in satellite image, Detection and
measurement of bone, tissue, etc., in medical images.
Image segmentation is part of image processing. The task of Image segmentation [1 4] is to group pixels
in homogeneous regions by using common feature approach. Features can be represented by the space of colour,
texture and gray levels, each exploring similarities between pixels of a region. Segmentation [5] refers to the
process of partitioning a digital image into multiple regions. The goal of segmentation is to simplify and change
the representation of an image into something that is more meaningful and easier to analyze. Image segmentation
is typically used to locate objects and boundaries (lines, curves, etc.) in images. In the present day world
computer vision has become an interdisciplinary field and its applications can be found many like area be it
medical, remote sensing, electronics and so on. Recently image segmentation based on [31][32] rough set and
fuzzy set and genetic algorithm have gained increasing attention. The result of image segmentation is a set of
regions that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels
in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture.
2. A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach
www.iosrjournals.org 72 | Page
Fig. 1 Classification of image
Segmentation is based on the measurements taken from the image based on the attributes like grey level,
colour, texture, depth or motion. Image segmentation techniques are categorized into three classes: Clustering,
edge detection and region growing. Edge [2 26] detection is an essential pre-processing step for image
segmentation which is used to transform the input image to a binary image, which indicates either the presence or
the absence of an edge. Edges are sets of pixels and are an important contour features where distinct intensity
changes or discontinuities in the corresponding image occur. In Digital image [3] brightness characteristics of a
pixel has an influence of background noise hence imaged pre-processing become necessary. Most noise in the
image is discrete noise. Median filter can eliminate the discrete noise, inhibition of salt and pepper noise and
overcome the image blur. The aim of noise [4] reduction is to suppress the noise while preserving the important
fine details and edges which are the boundary tow different regions. Image segmentation has been done based on
the Artificial Intelligent (neural network, fuzzy set, and rough set) [40] and Evolutional approach (ant colony
optimization, PSO) as a filter on both noisy and noise free images.
II. Literature Review
Image segmentation has been a topic of interest for the research as it can be associated with different
research areas like data mining, soft computing etc. Many researchers have published research papers related to
the above mentioned image segmentation related fields. Jan-Marco Bremer, Michael Gertz, [7] where the image
segmentation has been done based on the relative differential box-counting algorithm and the gliding-box
algorithm, which is a novel method for estimating the lacunarity features of greyscale digital images. The
authors have used four nature texture images to test the performance of the novel lacunarity measure. Wang
Lian, David Wai-lok cheung, Nikos Mamoulis, Siu-Ming Yiu, [8] present a novel method to detect plural kinds
of shapes such as lines, circles, ellipses, and parabolas have been proposed. It is based on improved Genetic
Algorithm (GA). .Rahm,Philip A Bernstein, [9] in which author talk about image segmentation result in a set of
segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the
pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or
texture. Due to the importance of image segmentation a number of algorithms have been proposed but based on
the image that is inputted the algorithm should be chosen to get the best results. The paper [9] the author gives a
study of the various algorithms that are available for color images, text and gray scale images.
They [9] have been put forward different types of segmentations which are, Point-based or pixel-based
segmentation, pixel based segmentation results in a bias of the size of segmented objects when the objects show
variations in their gray values. Darker objects will become too small, brighter objects too large. Edge-Based
Segmentation, Edge-based segmentation is based on the fact that the position of an edge is given by an extreme
of the first-order derivative or a zero crossing in the second-order derivative. Region-based methods focus
attention on an important aspect of the segmentation process missed with point-based techniques. There a pixel
is classified as an object pixel judging solely on its gray value independently of the context. This meant that
isolated points or small areas could be classified as object pixels, disregarding the fact that an important
characteristic of an object is its connectivity. Model-Based Segmentation all segmentation techniques discussed
so far utilizes only local information. The human vision system has the ability to recognize objects even if they
Shape Based
Image
Texture
Based Image
Classification of
Image
Color, Gray
scale image
Boundry
Based Image
Spatial Location
image
Structural
Based Image
Region
Based Image
3. A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach
www.iosrjournals.org 73 | Page
are not completely represented. It is obvious that the information that can be gathered from local neighbourhood
operators is not sufficient to perform this task.
The [10][23] proposed Ant Colony Optimization (ACO) is proposed which is a group of algorithms
inspired by the foraging behaviour of ant colonies in nature. Like their biological counterparts, a colony of
artificial ants is able to adapt to the changes in their environment, such as exhaustion of a food source and
discovery of a new one. In this paper, one of the basic ACO algorithms, the Ant System algorithm, was applied
for edge detection where the edge pixels represent food for the ants. A set of gray scale images obtained by a
nonlinear contrast enhancement technique called Multiscale Adaptive Gain is used to create a variable
environment. As the images change, the ant colony adapts to those changes leaving pheromone trails where the
new edges appear while the pheromone trails that are not reinforced evaporate over time. Although the images
were used to create an environmental setup in which the ants move, the colony’s adaptive behaviour could be
demonstrated on any type of digital habitat.
Hong-Hai Do, Sergey Melnik, Erhard Rahm , [11] paper presented a new approach for image
segmentation by applying k-means algorithm. In image segmentation, clustering algorithms are very popular as
they are intuitive and are also easy to implement. The [22][27] K-means clustering algorithm is one of the most
widely used algorithm in the literature, and many authors successfully compare their new proposal with the
results achieved by the k-Means. Amarintrarak N, R. Sailkean K., Tongsima S. Wiwatwattana N, [12] proposed
a method for automatic detection of root crowns in root images, are designed, implemented and quantitatively
compared. The approach is based on the theory of statistical learning. The root images are preprocessed with
algorithms for intensity normalization, segmentation, edge detection and scale space corner detection. Survey
paper [13 14] proposes a method for automatic 3D segmentation of human brain CT scans using data mining
techniques. The brain scans are processed in 2D and 3D.
III. Noteworthy Contributions In The Field Of Proposed Work
A lot of research work is going on in the field of image segmentation and it has found considerable
interest in both research and practice Many researchers have put forward many aspects of image segmentation.
Radim Burget, Vaclav Uher, Jan Masek [15] presents an innovative algorithm combining theory of artificial
intelligence and knowledge of human eye anatomy. Ping Chen , Zhisheng Zhang, Yanxiang Han , Fang Chen
,Bei Tang [16] proposed based on separating the coke microstructures from coke microscopic image is a crucial
task for automatic recognition by using digital image analysis technology. The author Yang Gui, Xiang Bai,
Zheng Li, Yun Yuan, [17] proposed novel approach is presented for color image segmentation. By incorporating
the advantages of mean shift (MS) segmentation and spectral clustering (SC) method, the proposed approach
provides effective and robust segmentation. Yanan Fu, Wei Zhang, Mrinal Mandal, and Max Q.-H. Meng,
Fellow [18], talk about on Wireless capsule endoscopy (WCE) can directly take digital images in
gastrointestinal (GI) tract of a patient. It has opened a new chapter in small intestine examination. Jamshid
Sourati, Dana H. Brooks, Jennifer G. Dy, Deniz Erdogmus , [19] published by have disuses about Constrained
spectral clustering with affinity propagation in its original form is not practical for large scale problems like
image segmentation. The paper [19] author has employed novelty selection sub-sampling strategy, besides using
efficient numerical Eigen decomposition methods to make this algorithm work efficiently for images. In
addition, entropy-based active learning is also employed to select the queries posed to the user more wisely in an
interactive image segmentation framework. Gajanan K. Choudhary and Sayan Dey, their paper [20] presents
fuzzy logic and artificial neural network based models for accurate crack detection on concrete. Features are
extracted from digital images of concrete surfaces using image processing which incorporates the edge detection
technique. The properties of extracted features are fed into the models for detecting cracks. G.Subha Vennila,
L.Padma Suresh, their paper [21] used image segmentation and classification on dermoscopy which is the
method of examining the skin lesions. It is especially used for Diagnosing melanoma, a type of skin cancer.
Image segmentation and classification are important tools to provide the information about the Dermoscopic
images clinically in terms of its size and shape. Chaloemchai Lowongtrakool and Nualsawat Hiransakolwong
[22], their paper proposes AUCCI, the Design of Image Segmentation Using Automatic Unsupervised
Clustering Computation Intelligence, a novel automatic image clustering algorithm based on Computation
Intelligence. V. Senthil, R. Bhaskaran, [24] talk about analyzes the robustness of watermarking method in still
images using Haar, Daubecheies and Biorthogonal wavelets. The embedding process uses a canny edge
detection method and hides the watermark with the perceptual considerations on different modalities of images.
Rubén Salvador, Andrés Otero, Javier Mora, Eduardo de la Torre, Teresa Riesgo and Lukas Sekanina,, [25]
presents an evolvable hardware system, fully contained in an FPGA, which is capable of autonomously
generating digital processing circuits, implemented on an array of Processing Elements (PE). R. Harinarayan,R.
Pannerselvam,M. Mubarak Ali,Dhirendra Kumar Tripathi, [26] their paper present a FPGA-based architecture
for edge detection algorithms has been proposed. Mei Yeen Choong, Wei Leong Khong, Wei Yeang Kow,
Lorita Angeline, Kenneth Tze Kin Teo, [27] represents a graph-based image segmentation method. Vaishnavi
4. A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach
www.iosrjournals.org 74 | Page
Ganesh, Sandhya Vaidyanathan, Eswer K, [28] talk about digital image segmentation with sensor.-With the
help of sensors and image processing technique giving artificial intelligence another limb to work with. The
applications of such a combined mechanism are limitless and this can be further brought to practical usage if
given the right resources. Ms.K.Kavi Niranjana, Ms.M.Kalpana Devi and Ms.L.Mary Marshaline ,[29] paper
presents a new approach for image segmentation by applying Marker Controlled Watershed Algorithm
(MCWS). The proposed approach [29] for image segmentation by comparing with Pillar - K means algorithm
[30] [33] [35] and involving RGB color space. Amiya Halder , Avijit Dasgupta, their paper [30] describes a
rough set approach for gray scale image segmentation that can automatically segment an image to its
constituents parts. The method mainly consists of spatial segmentation; the spatial segmentation divides each
image into different regions with similar properties. T. J. Ram´irez-Rozo, J.C.Garc´ia,Alvarez,C. G.
Castellanos-Dom´inguez,[33] where the Expectation Maximization Clustering (EM-Clustering) segmentation is
evaluated for IR images, using as reference watershed transform-based segmentation. A major challenge in
segmentation [35] evaluation comes from the fundamental conflict between generality and objectivity. As there
is a glut of image segmentation techniques available today, customer who is the real user of these techniques
may get obfuscated. Wenbin Zou, Kidiyo Kpalma and Joseph Ronsin ,[36] where the used new concept image
segmentation by using region bank. Images are hierarchically segmented leading to region banks. Local features
and high-level descriptors are extracted on each region of the bank. Vasiliy N Vasyukov, Nikolay and Sysoev,
[37] introduce a new image texture segmentation algorithm, based on wavelets and the hidden Markov tree
model Hidden Markov tree model provides a good classifier for distinguishing between textures. Mei Wang,
Hsiung-Cheng Lin, Xiao-Wei Wu, Jian-Ping Wang, [39] proposed a new image segmentation approach using
the proportion of foreground to background method.
IV. Segmentation Algorithm
Segmentation algorithms are based on one of two basic properties of color, gray values, or texture:
discontinuity and similarity. First category is to partition an image based on abrupt changes in intensity, such as
edges in an image. Second category are based on partitioning an image into regions that are similar according to
a predefined criteria. Histogram thresholding approach falls under this category.
Fig. 2 Segmentation
4.1 Thresholding
The simplest method of image segmentation is called the thresholding method. This method is based on a clip-
level (or a threshold value) to turn a gray-scale image into a binary image. The key of this method is to select the
threshold value (or values when multiple-levels are selected).Finding histogram of gray level intensity.
Basic Global Thresholding
Otsu’s Method
Multiple Threshold
Variable Thresholding
5. A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach
www.iosrjournals.org 75 | Page
Fig. 3 Original image with thresolding
4.2 Edge-based segmentation
Using mask to detect edge in image by convolution.
Basic Edge Detection
The Marr-Hildreth edge detector(LoG)
Short response Hilbert transform(SRHLT)
Watersheds
Fig. 4 original image with Edge Based Segmentation
4.3 Region-based segmentation
Finding region, but not finding edge.
Region Growing
Data Clustering (Hierarchical clustering)
Partitional clustering
Cheng-Jin Kuo`s method
Fig. 7 original image with Region Based Segmentation
6. A Survey of Image Segmentation based on Artificial Intelligence and Evolutionary Approach
www.iosrjournals.org 76 | Page
V. Proposed Methodology
The propose work will Develop and implement of A Novel algorithm for image segmentation which used
concepts of Data mining techniques. A investigate the applications and improvements in the field of image
segmentation. The following proposals can be taken forward during the tenure of the research work.
The paper [34] given by author proposed a new method for neural network training patterns using fuzzy
concepts which cannot be applied directly to medical image because medical image as it does not distort
the objects shape and is able to retain the important features. So it is required develop improved matching
algorithm that can be applied directly on the medical images.
The paper [35] compares the performance of a few clustering based image segmentation methods. Fuzzy c-
Means clustering algorithm, particle swarm optimization and Darwinian PSO are discussed. It is required
proposed to work on the performance analysis will be based on some newer optimization techniques as well
as the algorithms and comparison will be extended to wide range of applications.
In paper [9] author has explained and suggested a few application specific segmentation algorithms which
also take into consideration the type of image inputted like color, gray scale and text. So it is required to
calculate performance of new optimization techniques on different types of inputted image.
The propose algorithms criteria are, First of all, we need to be aware of the target image which we would
like to segment out. Second, the background image has to be blurred and the colour of the target image
should be different to that of background image as much as possible. They expect the appendages of the
target image to cross over each other as least as possible. The approach work is to obtain segment of the
target image and boundary extraction separately and simultaneously. The proposed methodology used
concepts artificial intelligence [34 4] (Neural network, fuzzy logic and rough set theory) and evolutionary
approach [2 23] of the pixels of the target image. The propose work use Matlab to extract the segment of
image. Then they will perform final modification and remove the noise. Then these processes applied for
different types of image and calculate the performance of algorithm.
Fig. 8 Flowchart of the proposed algorithm
VI. Expected Outcome Of The Proposed Research Work
Using the proposed methodology following outcomes is expected in due course of research work. They include
after picking a numbers different types of images which fit the criteria from the website, some images are
selected and partially segmented from the original image. The processed images are posted as follows:
Images are blurred can be segmented with nice quality.
Images with clear boundary would be extracted.
Only clear foreground images would be left.
The information of the pixels which reside in the extracted boundaries.
VII. Conclusion
In image analysis, Segmentation is an important pre-processing step in the areas of image analysis and
image compression. It is a critical and essential component of image recognition system and usually determines
the quality of the final result. Segmentation is the partitioning of a digital image into multiple regions (sets of
pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in
literature and there are a wide variety of approaches that are used. Different approaches are suited to different
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