Abstract Of Artificial Intelligence

Abstract Of Artificial Intelligence
Abstract Artificial Intelligence otherwise known as AI, it is the development and the theory of some computer systems which are able to undertake
certain tasks which will normally need the intelligence of humans. The tasks that are normally in need of the human intelligence are the likes of
translation of languages, making decisions recognition of speech among others. Good examples of these technologies that fall under the AI are;
augmented reality, Virtual Assistants, and robots. On the other hand, employee productivity can also be called workforce productivity. Productivity is
evaluated in terms of the output of employees within a given time. A Lot of US multinational have embraced the use of this technology as it has been
touted as leading to some financial benefits(Bobrow,2005). My research is limited to American multinational corporations like Amazon and Google.
American Multinational Corporations using AI Technology Many of tech companies and organization are putting into use the AI technologies that are
made up of the robots, augmented reality and even the virtual assistants. Recently there was a report that was undertaken by Accenture in 12 nations
and it showed that AI that is able to sense the environment has the ability to know what is happening to lead to the taking of an action. All these
would, later on, lead to the rise in the levels of productivity to sales of 40% in the year 2035.The report goes on to show that once AI is well utilized,
there will
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Future Of Life : Thanks With Artificial Intelligence
Future of Life: Thanks to Artificial Intelligence Artificial Intelligence is soon to be a massively important and relevant part of our future. I have no
doubt about it, and knowing this... I began my research simply wanting to know more about AI and it's current and speculative uses and capabilities.
I wanted to know how we planned to accommodate for the biggest change our species has ever experienced, I wanted to find out how legislature
would adapt, how research would spring up, how production would occur and by whom and how it would eventually be implemented and used by
our society. The potential benefits and uses of artificial intelligence really excited me and the endless possibilities of what we could accomplish really
propelled... Show more content on Helpwriting.net ...
However, the long–term goal of many researchers is to create general AI, AGI or strong AI. While narrow AI may outperform humans at whatever
its specific task is, like playing chess or solving equations, AGI would outperform humans at nearly every cognitive task. There is no limit as to
what it can learn, the human brain can only learn and do so much at once, but a computer has the ability to do hundreds and thousands of tasks,
operations, calculations, research all at once, and when it is given the ability to think and to learn there is endless possibilities as to what can be
accomplished. Artificial intelligence has the potential to be the most beneficial invention of our generation. But it isn't without it's dangers. Just as the
advancement of fire led to destruction and the agricultural invention led to conflict, and the internet led to invasion of privacy and much more...
Artificial intelligence will also have it's drawbacks. So will it be the most dangerous thing this world has ever experienced or will the benevolent,
good side of humanity prevail? The answer to that remains undecided and completely up to us. Artificial Intelligence has the capability to propel us to
advancements in so many fields. However AI also carries the capability to change everything for the worse, and potentially destroy all of us; its future
is entirely dependant on us as a species. As artificial intelligence gets

Persuasive Essay On Technology-Based Education
John Dewey said, "If we teach today's students as we taught yesterday's, we rob them of tomorrow." This sparks the very controversial debate on if
schools should initiate technology–based education throughout their campus or stick with the traditional textbooks. The idea of change has always
caused great controversy throughout the world and education is no exception. To have a better understanding on this issue there are many facts on both
sides to look into. One of the major reasons people side with the continuation of textbooks is the many health concerns that come with technology.
Technology is known for causing many eye, neck, and back issues. New York Daily News reported that a condition called CVS,computer vision
syndrome, is common with people who use technology on a day–to–day basis (1). The AOA, American Optometric Association, stated that symptoms
of this condition include: eyestrain, headaches, blurred vision, and dry eyes ( NYDN, 1). This is caused by continuous time spent in front of an
electronic screen, such as a tablet or computer. The article also reports of neck strain and pain in the shoulders, hands, or arms (1). Because of these
many health risks that are caused by using technology, many people are concerned with technology–based education.
Another major reason many people give for not switching to technology–based education is the cost. Lee Wilson states, "It will cost a school 552%
more to implement iPad textbooks than it does to deploy books"

The Use Of Automatic Real Time Tracking And Augmented 3d...
The interest to detect and track the endovascular devices during X–ray guided interventional procedures spans over a decade. The recent developments
in real–time detection, tracking, visualization over an augmented reality with multi modality fusion has transformed the surgical environment.
However, it's quite challenging to combine robustness of automatic real–time tracking and augmented 3D visualization. In addition, various
endovascular procedures use different devices and tracking requirements (tip or whole catheter).
The important parameters that affect the detection and tracking can be classified in to 3 broad categories; endovascular devices, projection geometry
and motion. At first, the detection of various shapes and radio–opaque devices is a challenge. Secondly, the detection is challenged by image
magnification and image geometry by various projections (Cranio–caudal tilt). Thirdly, and importantly real–time tracking challenged by the patient
motion and the deformation of the vessel.
Over the years, many of these challenges were addressed with success limited to devices. However, the potential clinical benefits; to deploy the
fenestrated stent grafts accurately without blocking branches of abdominal Aorta (renal), to reduce contrast medium injected that might reduce the risk
of renal pathologies, to reduce the radiation exposure to the patient, personal and the environmental, sustains the research interest. In addition, a
successfully registered 3D

Self Driving Research Paper
I see self–driving cars being phased in incrementally over the next few decades, and want to contribute to making them a reality. Nearly every aspect
of self–driving cars needs improvement, and is a potential treasure trove for research. I think my choice is complex enough to be useful, yet still
narrow enough to be completed in approximately two years.
One of the most frustratingly difficult problems for autonomous vehicles is obstacle detection and avoidance. For cars especially, the diversity of the
types of obstacles they may come across is what really breaks down even the most complicated detection methods. A very common example is a
machine learning system which has been trained on large sets of obstacle images; the first time it runs ... Show more content on Helpwriting.net ...
The first way is fusing the standard camera data stream with that of a forward looking infrared (FLIR) system. FLIR systems have been used for years
by the military to detect enemy combatants and vehicles via their heat signatures. The major advantage of this approach is that it works just as well, if
not better, during the night. While a standard camera produces a large amount of low–light noise, infrared cameras can produce high resolution images
which can be used for accurate obstacle detection. I plan to use the fused data for feature extraction with a suite of image processing methods, such as
Canny edge detection. Finally, I plan to use the extracted features to classify them with common methods such as neural networks or SVMs. I expect
this phase of my research to take one to one and a half years, because it relies on proven technology being used in a new

Artificial Intelligence In Traditions By La Maquina
Artificial Intelligence is the simulation of human intelligence processed by machines, especially computer systems in which can ran as robots. AI
systems are programmed of acquisition of information and rules for using the information to reach approximate or definite conclusions. There are
various applications of AI expert systems, some include speech recognition and machine vision. In the story of Traditions, we are introduced with
La Maquina, a AI robot who lives with a Mexican household family who serves as a maid essentially. "Y Mira, you're interested in cooking, in being
a curandero, in learning her ways" (Gonzales, Kindle). The writing style in the story is engaging because of Gonzales focus on Mexican culture and
traditions in this futuristic world. I believe in cases like these this benefits the family in functional aspect of living. Having a robot/assistant perform
tasks around the house can be very helpful. Such as cleaning, babysitting, cooking this can allow for us humans to focus on more important things such
as trying to live a healthy lifestyle and focusing on school or work. In the prologue to this story, the author talks about incorporating their tradition to
today's world. There is a rising conflict between Mictan and her grandmother. Grandma wants Mictan to live a certain life but does not agree to what
she ultimately thinks about it. The robot seems to have a high–performance technology because of her communications and abilities we notice right
away,

Studying Real Time Application On College Student For...
In this paper, we are going to develop real time application on college student for automatic detection and recognition of student during academics,
followed by display of personal information of students. This application makes proper use of CCTV camera for real time face detection of students
of particular college. The proposed application can be divided into four major steps. In first step, each person in the image is detected. In the second
step, a face detection algorithm detects faces of each person. In third step, we use a face recognition algorithm to match the faces of persons in the
captured image with the database of students' faces which also stores personal as well as academic information of each student. In final step, the face of
student along with his/her personnel information will be displayed on screen to the user when the image captured by CCTV camera contains any
student image of present college. The college administrator as well as faculty members can use this application to identify students and also to
distinguish students from outsiders.
Keywords– Real time face detection, face recognition, denoising
I.INTRODUCTION
Now–a–days identification of students in college campus is very necessary to identify outsiders from college campus. So we decided to make an
automatic device which identifies students of college. Also the identification of each student through automatic device will help faculty as well as
administrator to make record of entered

Modern Science And Arts University
Image blurring and restoration Abdishakur Abdinasir Hersi Faculty of electrical communication and electronics systems Modern science and Arts
University Six of October city, Egypt Abdi_shakur23@hotmail.com Abdallah Nasser Faculty of electrical communication and electronics systems
Modern science and Arts University Six of October city, Egypt nasserw1995@gmail.com Mariam Monier Faculty of electrical communication and
electronics systems Modern science and Arts University Six of October city, Egypt Abstract–Development of blur detection algorithms has attracted
many attentions in recent years. Blur detection algorithms are very helpful in real life applications and it used for many purposes like image
restoration, image blurring and also for image enhancement. The root cause of blur can be extracted in many applications. Blur can be classified into
several main categories either it could be blur due to motion or blur due image defocus, Primarily our research focuses on how to restore a motion
blur image by using several techniques like edge sharp analysis, low depth of field image segmentation, lowest directional high frequency energy (for
motion blur), and wavelet–based histogram and support vector machine. Then we will conclude our paper by implementing our mention methods using
mat lab that will show the output of our design algorithms and its effectiveness. Keywords–blurdetection, algorithms, image restoration, image
sharpening,matlab I.INTRODUCTION Most of the

A Short Note On Camera Mouse And A Computer Vision Based...
CE301 Individual Project
Initial Report
Camera Mouse and a Computer Vision Based Email System
Student Name: Cosmin Buzea
Supervisor: Klaus McDonald–Maier
Second Assessor: Francisco Sepulveda
Contents
Background Reading3
Introduction3
Camera Mouse3
Computer Vision3
Face Detection3
Face Tracking4
OpenCV4
Computer Vision Based E–mail System4
Project Goals5
Project details5
Functionality and Design6
Hardware and Software required7
Methodology7
Project Planning7
Reference9
Background Reading

Introduction
A large number of handicapped persons that have limited mobility have had problems doing activities without the help of others. The same problem is
for them when using a computer because it usually ... Show more content on Helpwriting.net ...
An inexpensive solution was to use the web camera as a mouse alternative. [2]
Camera Mouse
By using the web camera the computer input devices (mouse and keyboard) could be replaced by the web camera to move the mouse pointer and a
virtual keyboard instead of the typical keyboard. [2]
The web camera will take video input of the user's head and it will send it to a face–detection system that will recognize the face features. After
recognizing the face features a tracking system will start moving the mouse pointer based on the movement of a body part of the user. [2]
There are four types of controlling the mouse pointer: one is by using the eye movement, the second is by using head movement, the third is by using
hand movement and the fourth is by using a laser that is mounted on the user head. [2]
Computer Vision
Computer Vision is a field that deals with describing the world that we see in the same way as any human can by using algorithms and methods that
can acquire, process, analyze and understand images. [3]
In computer vision we can track a human's movement, we can create a 3D model of an area using large number of photographs, face detection and
recognition and large number of other applications. [3]
Face Detection
For creating a camera mouse a developer needs to make a face–detection system that can recognize the face features. This system must be use minimal
computation time and achieve high detection

Chromebook Advantages And Disadvantages
We've all heard about electronics in schools, whether you are a parent who has been called repeatedly about your kid being glued to it in class or
one of said students, it has been present. The Chromebook has been regarded as a way to revolutionize learning, but it also has a slew of
consequences. So we are left with the everlasting question of whether or not it is a viable method of learning.
It can be seen that there are obvious disadvantages in the system, but some other effects, whether it has to do with health, the environment, or costs, go
unnoticed by the general populous. In a study done by the American Optometric Association, it is shown that using computers in school increases the
odds of a problem called Computer Vision Syndrome, which causes problems such as headaches, eyestrain, dry eyes, and blurred vision. Another
medical problem has been given the lovely name "Text Neck". It is a condition that is caused by exerting more pressure on the upper vertebrae of
the spine, which causes wear and tear on the spine and could result in mass amounts of pain or required surgery. Also to be noted, it takes 79 gallons
of water, 33 pounds of miscellaneous minerals, and 100 kilowatt hours of energy, while printing a textbook requires about 2 kilowatt hours of energy
and two thirds of a pound of materials. While we're on the subject, let's talk about costs. While textbooks may seem expensive, they require very little
for upkeep, not to mention things like networking and

The Benefits Of Artificial Intelligence
Many wonder if Artificial Intelligence (AI) could be beneficial to humanity. AI has been around for only a few years. Voice Assistants such as Bixby
and Siri help people maneuver through screen selection, apps and much more on their cell phones easier and faster. AI is slowly getting better, with
things such as self–driving cars and robotic relations. There are two types of AI, weak AI and strong AI. Weak AI are things we have today, such as
those listed above (Siri and Bixby), and the strong AI is what researchers are currently working towards. There are many ways that AI could be
useful in our lives, it can help us to move forward in technology that we have yet to make many advances in, it can also make everyday
complications and annoyances much easier for humans. With the help of AI, technology will be better and faster than ever because the AI will not
make repeated mistakes like people would. First, AI is created to learn as they go. AI is able to make fast advances by being given set goals. In the
article, Benefits and Risks of AI, the author wrote, "Machines can obviously have goals in the narrow sense of exhibiting goal–oriented behavior: the
behavior of a heat–seeking missile is most economically explained as a goal to hit a target" (Tegmark). Researchers are able to set a goal for the AI
to reach so it will not get off task, which will make the research process faster without common distractions. This allows the AI to work through the
problems quickly

Why The Driverless Cars Are The Future Of Travel
There has been much controversy over the claim that driverless cars are the future of travel. Many people are uncertain of the new advances in the
technology. This is why the idea that we could possibly one day be riding in self–driving with the assurance that everything is under control is amazing
(Weber.) In recent years developments have been underway to make driverless a reality, but there are still many obstacles to try and overcome. The
invention of anti–lock brakes to cruise control has greatly relieved the stress of driving for many travelers ("How a Driverless Car Will Benefit You.")
The progress of technology in modern times has greatly aided drivers. The stability of automobiles has come a long way since the days of Guido da
Vigevano... Show more content on Helpwriting.net ...
Telegraph Media Group, 11 Feb. 2015.Web. 10 Oct. 2016. .
Bottorff, William W. "What Was The First Car? A Quick History of theAutomobile for YoungPeople." The First Car. N.p., n.d. Web. 10 Oct. 2016..
Buchanan, Bruce G. "AI MAGAZINE." A (Very) Brief History of Artificial Intelligence.Association for the Advancement of Artificial Intelligence,
2016. Web. 11 Oct. 2016..
Dr_E Aug 24, 2011 Edited Aug 21, 2015 3 7 Tweet. "Advantages and Disadvantages forArtificial Intelligence." Advantages and Disadvantages for
Artificial Intelligence.InfoBarrel, 24 Aug. 2011. Web. 11 Oct. 2016. .
Johnson, Nicole Blake. "The Pros and Cons of Driverless Cars [#Infographic]." StateTech.StateTech, 10 Sept. 2014. Web. 12 Oct. 2016. .
Robin. "Artificial Intelligence." RSS. N.p., 24 Nov. 2009. Web. 11 Oct. 2016..
Weber, Marc. "Where To? A History of Autonomous Vehicles." CHM Blog Where to AHistory of Autonomous Vehicles Comments. Computer History
Museum, 8 May 2014comput. Web. 10 Oct. 2016. <http://www.computerhistory.org/atchm/where
–to–a

Number Plate Extraction
III. NUMBER PLATE EXTRACTION
The captured input image has number plate covered by vehicle body, so by this step only number plate area is detected and extracted from whole
body of vehicle. The number plate extraction phase influence the accuracy of ANPR system because all further step depend on the accurate extraction
of number plate area. The input to this stage is vehicle image and output is a portion of image containing the exact number plate. Number plate can be
distinguished by its features. Instead of processing every pixel, the system processes only the pixels that have these features. The features are derived
from number plate format and the characters constituting the plate. Number plate color, rectangular shape of number plate boundary, the color change
between the plate background and characters on it etc. can be used for detection and extraction of number plate area. The extraction of Indian number
plate is difficult as compared to the foreign number plate because in India there is no standard followed for the aspect ratio of plate. This factor makes
the detection and extraction of number plate very difficult. The various methods used for number plate extraction are as follows:–
A. Number Plate Extraction using Boundary/Edge information
Normally the number plate has a rectangular shape with aspect ratio, so number plate can be extracted by finding all possible rectangles from the input
vehicle image. Variousedge detection methods commonly used to find these

A Brief Note On Pedestrian Detection Of Surveillance
Pedestrian Detection in Surveillance
Abstract
Pedestrian detection is fragmented as there are numerous algorithms used in different research. This research aims to provide an overview to
understand the current state of pedestrian detection as well as analyse the challenges, effectiveness, accuracy and cost benefit of different approaches
used in surveillance and how current challenges are being tackled.
Introduction
Pedestrian detection is a key problem in computer vision. There are a number of ways that makes it difficult to achieve without faults due to the
differences in colour, motion, orientation and poses of individual human beings but also segregation of the background which may or may not be
cluttered as well as other moving objects that just might get in the way.
Even though it is a difficult matter it is still a major research areas as there a great implications of perfecting the system. Key application could
include in the field of surveillance, robotics as well as a tool in vehicular safety. It can also be used in a commercial apects as businesses would be
able to count the number of pedestrians creating a flow statistics for optimizing their business. For it to be effective it should be able to handle
emerging complex scenarios such as speed of detection for vehicles to enable them to avoid or stop immediately and the accuracy in detecting
pedestrian through a crowd for businesses. These applications can also then be forwarded to surveillance as factors

Artificial Intelligence Dangers
What are the dangers and possible benefits of Artificial Intelligence? Throughout the course of history, technological advances have proven to not only
influence, but significantly impact one's quality of life. Within society, there are those who strongly believe that these advancements have saved lives
and benefitted future generations, while others argue against it, firmly articulating that the progression of technology will prove to work against the
overall betterment of humanity. Despite this, one cannot deny society's obsession and thirst for the next 'big' technological advancement. Human's,
never content with what they have, are extremely vulnerable and susceptible to consumeristic tendencies. Regardless of what is actually attainable, we
always focus on the technology of the future. Perhaps the definition of futuristic advancement can be best understood as Artificial Intelligence?
Surprisingly the days of Artificial Intelligence are approaching; however, with this revelation come challenging questions. What can be gained through
Artificial Intelligence –– how will humanity be impacted by this technology? Are there potential dangers associated with it? Will Artificial Intelligence
be a universal advancement? Only through further exploration and analysis of these questions will one be able to fully grasp the notion of Artificial
Intelligence Studies have demonstrated that there are many diverse ways that Artificial Intelligence can make one's

The Appliance Of Computer Vision Techniques
Abstract
Nowadays retail video analytics has moved out ahead of the traditional domain of loss prevention and security by providing retailers understanding
business intelligence for instance queue data and store traffic statistics. Such information allows on behalf of optimized store performance, enhanced
customer experience, ultimately higher reduced operational costs and profitability. This paper gives an overview of various camera–based applications
in retail. It also presents a number of the promising technical guidelines for survey in retail video analytics.
Introduction
The appliance of computer vision techniques in retail using more than a decade.1 in recent times, due to advancement in machine learning, computer
vision, and data analysis, retail video analytics can offer retailers with much further insightful business intelligence. Therefore it promises much
privileged business value, ahead of the traditional domain of authentication, security and loss prevention. Examples include queue data, analysis of
store traffic, purchase decision making and shoppers' behaviors, between others. The retail atmosphere, in addition to its own distinctive business and
technical challenges, is also well thought–out a practical test bed for novel computer vision approaches. For these reasons, retail video analytics and
it's a variety of applications have become of enormous interest to both retailers as well as the computer vision society.
EXISTING TECHNOLOGY
2.1 Loss

Technology And Technology Essay
Technology is unavoidable. No matter where you turn there is sure to be some sort of screen right in your face full of information. The Information Age
has transformed society in ways no one could have imagined. But when do the advancements become too much? Machines and artificial intelligence
have become an integral part of our society used by the majority of the population in their everyday lives. Artificial intelligence has woven its way deep
into our society and begun controlling our lives, without most people even batting an eye. Although technological advances are good, continuing to let
AI grow at this alarming pace will dangerously inhibit the capability of the human brain while also putting generations of people out of work. The
technological advancement that is most prevalent in today's society is without a doubt, the smartphone. People now have access to any information
they want at their fingertips through these smartphones. However, this has caused negative effects within our brains as described in Carolyn
Gregoire's article, "How Technology Is Warping Your Memory". With such easy access to information, people are now putting less effort into
memorizing the information they take in. People have become so dependent on their phones that they are not even bothering to store information in
their memory because they know they will have their phone if they ever need to access it later on. With the internet literally at our fingertips, humans
constantly move from one

Benefits Of Artificial Intelligence
Beneficial AI Many wonder if Artificial Intelligence (AI) could be beneficial to humanity. AI has been around for only a few years. Voice Assistants
such as Bixby and Siri help people maneuver through screen selection, apps and much more on their cell phones easier and faster. AI is slowly getting
better, with things such as self–driving cars and robotic relations. There are two types of AI, weak AI and strong AI. Weak AI are things we have
today such as those listed above (Siri and Bixby), and the strong AI are what researchers are currently working towards. There are many ways that
AI could be useful in our lives, it can help us to move forward in technology that we have yet to make many advances in, it can also make everyday
complications and annoyances much easier for humans. With the help of AI, technology will be better and faster than ever because AI will not
make repeated mistakes like people would. First, AI are created to learn as they go. AI are able to make fast advances by being given set goals. In the
article, Benefits and Risks of AI, the author wrote, "Machines can obviously have goals in the narrow sense of exhibiting goal–oriented behavior: the
behavior of a heat–seeking missile is most economically explained as a goal to hit a target" (Tegmark). Researchers are able to set a goal for the AI
to reach so it will not get off task, which will make the research process faster without common distractions. This allows the AI to work through
problems

A Method Of Using Mrf Model
A method of using MRF model in image segmentation
Abstract
Machine vision is a high–speed developing field in AI, and image identification is becoming more and more important with higher and higher requires
of AI. As a big part of image identification, it is abviously necessary to develop better solution in image segmentation, thus machine could identity
objects easier. The division technique for pictures based on Markov Random Field (MRF) demonstrate ready to combine the contextual information
from label pictures and statistical properties of pictures to be segmented.
In this paper, we start from MRF theory, described the principle and methods of MRF model, and discussed the advantages of using MRF model in
image processing, also concluded researches and experiences all of the world. And we summarized some types of iterative algorithms of MRF models,
also the features of these algorithms are analyzed and compared. We also did some simulations of some typical algorithms and the results were
compared with traditional methods which demonstrated that using MRF model could have better performance in segmentation.
Key words: Markov random field, image segmentation, adaptive neighborhood, prior information Projection description
What is the problem to be addressed?
The specific problem to be addressed is one modified segmentation method using MRF model, the neighborhood of pixels, this is a little improving in
neighborhood model choosing which is really efficient. As there are

Image Denoising Essay
3.1 IMAGE DENOISING
Denoising of image means, suppressing the effect of noise to an extent that the resultant image becomes acceptable. The spatial domain or transform
(frequency) domain filtering can be used for this purpose. There is one to one correspondence between linear spatial filters and filters in the frequency
domain. However, spatial filters offer considerably more versatility because they can also be used for non linear filtering, something we cannot do in
the frequency domain. Recently wavelet transform is also being used to remove the impulse noise from noisy images. Historically, in early days filters
were used uniformly on the entire image without discriminating between the noisy and noise–free pixels. mean filter such as ... Show more content on
Helpwriting.net ...
Researchers published different ways to compute the parameters for the thresholding of wavelet coefficients. Data adaptive thresholds were introduced
to achieve optimum value of threshold. Later efforts found that substantial improvements in perceptual quality could be obtained by translation
invariant methods based on thresholding of an Undecimated Wavelet Transform . These thresholding techniques were applied to the nonorthogonal
wavelet coefficients to reduce artifacts. Multiwavelets were also used to achieve similar results. Probabilistic models using the statistical properties of
the wavelet coefficient seemed to outperform the thresholding techniques and gained ground. Recently, much effort has been devoted to Bayesian
denoising in Wavelet domain. Hidden Markov Models and Gaussian Scale Mixtures have also become popular and more research continues to be
published. Tree Structures ordering the wavelet coefficients based on their magnitude, scale and spatial location have been researched. Data adaptive
transforms such as Independent Component Analysis (ICA) have been explored for sparse shrinkage. The trend continues to focus on using different
statistical models to model the statistical properties of the wavelet coefficients and its neighbors. Future trend will be towards finding more accurate
probabilistic models for the distribution of non–orthogonal wavelet

Annotated Bibliography On Digital Libraries
I.INTRODUCTION The rapid increase in the volume of digital libraries due to cell phones, web cameras and digital cameras etc, needs and expert
system to have the effective retrieval of similar images for the given query image [1]. CBIR system is one of such experts systems that highly rely on
appropriate extraction of features and similarity measures used for retrieval [10]. The area has gained wide range of attention from researchers to
investigate various adopted methodologies, their drawbacks, research scope, etc [2–5, 14–18]. This domain became complex because of the
diversification of the image contents and also made interesting. [10]. The recent development ensures the popularity of CBIR, since it has been
applied in many real world applications such as life sciences, environmental and health care, digital libraries and social media such as facebook,
youtube, etc. CBIR understands and analyzes the visual content of the images [20]. It represents an image using the renowned visual information such
as color, texture, shape, etc [11, 12]. These are often referred as basic features of the image, which undergoes lot of variations according to the need
and specifications of the image [7–9]. Since the image acquisition varies with respect to illumination, angle of acquisition, depth, etc, it is a challenging
task to define a best limited set of features to describe the entire image library. Similarity measure is another processing stage that defines the
performance of

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

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

Interest Point Detectors
Interest Point Detectors
Introduction:
How it started
Our original idea was to implement the beginning stages of a paper on measuring the driver fatigue detection from a sequence of images []. Majority
of accidents reported are due to driver fatigue. One of the important parameter to be detected to measure the driver's alertness is to track his/her eyes
in the given image sequence. Eye detection is an active research area in computer vision. Applications can range from face detection to biometrics.
Canthus (Eye corners) is a very stable feature that can be constantly detected from a facial image irrespective of the eye status or gaze direction.
Canthus is initially defined and treated as a corner (interest point). Interest point detectors like Harris and SUSAN are being studied and discussed in
this report. These corner detectors are evaluated to find which one suits best for this application. Interest Points
An interest point is a relatively important kind of localized two–dimensional image structure with a mathematical description. It can be defined as the
intersection of two edges or as a point with two dominant and different edge directions. "Corners", "T", "Y" and "X" junctions have the same definition
and fall under this category. Although the interest points constitute a very small portion in the image, the information on shape is concentrated at these
points.
Image corner detection is an important task in various computer vision and image understanding

Corner Detection Are Useful for Computer Vision...
Corner detection and its parameters: position, model and orientation are useful for many computer vision applications, such as object recognition,
matching, segmentation, 3D reconstruction, motion estimation [2, 3, 4, 34.] indexing, retrieval, robot navigation and in our case edge tracking from
geometry design. This need has driven the development of a large number of corner detectors [1, 5, 6, 7, 8, 9, 10, 11, 12, 13.]. Other methods forcorner
detection are described in [14, 15]. These detectors compete with each other in terms of precision localization, accuracy, speed, and information they
provide. Model classification and orientation are the most interest information needed in process of edge tracking.
For some of these approaches, ... Show more content on Helpwriting.net ...
Corner strength has been п¬Ѓrst deп¬Ѓned by Noble [12] from which a slightly diп¬Ђerent version has been proposed by Harris and Stephen [11]: (1.2)
The role of the parameter k is to remove sensitivity to strong edges.
The Plessey operator uses estimates of the variance of the gradient of an image in a set of overlapping neighborhoods. This detector, which produced
much interest, was extended by including local gray–level invariants based on combinations of Gaussian derivatives [17].
One of the earliest detectors [16], which was based on the Moravec operator, deп¬Ѓnes corners to be local extrema in the determinant of the Hessian
Matix, H=M.
The Kitchen and Rosenfeld operator [5] uses an analysis of the curvature of the grey–level variety of an image. The SUSAN operator [18] uses a form
of grey–level moment that is designed to detect V– corners, and which is applied to other model of corner.
The earlier Forstner [22] algorithm is easily explained in terms of H (Hessian Matrix). For a more recently proposed detector [20], it has been shown
[21] that under afп¬Ѓne motion, it is better to use the smallest eigenvalue of H as the corner strength function.
Recently, George Azzopardi and Nicolai Petkov [36] propose a trainable filter which we call Combination Of Shifted FIlter REsponses (COSFIRE) and
use for keypoint detection and pattern recognition.
2)Contour based methods
These methods extract contours and then

Methods of 3D Image Analysis
Introduction
Since the late 1980s, arguably the most successful method of 3D image analysis has been the model–based segmentation approach. This approach, in
its most mature form, matches 3D image information to models which describe not only the expected shape and appearance of a structure, but also
statistically–based information about common variations in the structure of interest. In this way, the natural deviations in organic structures are
accounted for in the statistical shape model, or SSM.
This literature review seeks to present the present stake of the field in terms of representing structures in this way, through a review of the methods of
representing bones. Search algorithms used to locate and align a model to the image data will then be discussed and described. Techniques and
algorithms used to model the appearance of bones will be analysed and commented on, including the construction from, and correlation of, bones to
their models. Case studies will be mentioned throughout to demonstrate the application of SSMs to image segmentation needs. Finally, some further
applications of these models will be expanded upon.
Representation of Bones
There are a number of methods in which training data for SSMs can be gathered, and all involve some type of volumetric image set to have been
obtained. This is done primarily through the modalities of computed tomography or magnetic resonance imaging, and sometimes both in conjunction
(1). Both binary and fuzzy voxel data can

Rectification Of Cameras Captured Documents Images For...
Rectification of camera captured document images for Camera–based OCR technology Abstract Due to the rapid progress in digital cameras industry,
camera captured documents becomes as another possibility or choice for document capturing and understanding for OCR applications. However, image
quality degradations arising from the image acquisition process have severe effect on these applications. The distortion results from digital camera may
take the shape of skew, perspective distortion or geometric distortion. We propose fully automatic preprocessing techniques to enhance the digital
camera captured images to improve the performance of OCR applications. Our algorithms depend on the features of the text lines and letters and do not
need any especial equipment. Experimental results on a real camera captured images demonstrate 10~15 % enhancement in current commercial OCR
packages. 1.Introduction Textual content in books, newspapers and articles have been traditionally digitized using scanners and read with the help of
optical character recognition (OCR). Recent technical advances in digital cameras have led the OCR community to consider using them instead of
scanners for document capture. Compared with the scanner, the digital camera is quite easier to use, being able to capture images from any viewpoint.
Cameras can be easily integrated with portable computing devices such as PDAs, cell phones, or media players. Together, these factors contribute to
the growing interest

Waymo Essay
Google company, Waymo, an autonomous vehicle division of Alphabet, has achieved an important innovative. Members of Phoenix Arizona Easy
Rider Program are now able to register to go for a ride in the self–driving minivan for free. Anyone interested can register, however riders will be
selected by the type of trips needed and the inclination to use the self–driving vehicle as their primary means of transportation. The self–driving
minivan are being driven without a driver behind the wheel, however, an employee will ride behind the driver's seat. The car is equipped with fully
autonomy; sensors that can see around the world, lasers that can see objects in 3D for up to 300 meters. It also has short range lasers for closer views
and radars for seeing beneath and around vehicles for tracking moving objects. Waymo has teamed up with Flat–Chrysler, Lyft (Andrew Hawkins.
2017). AI in the gaming world is common and widely used in our daily life. When playing against the computer, AI becomes involved by assisting
with determining what moves to make (Mike Fekety 2015). One good thing about AI or computer is that it does not require sleep or breaks. Like the
energizer bunny, it keeps on going and going without stopping (Rounak, 2017). Disadvantages of Artificial Intelligence With AI being so useful, who
is to blame when things go bad? It is hard to know who to blame when things go wrong with AI. Some may want to blame the software, even the
owner, but who is the blame? The way

Computer Vision Causes And The Effects Of Visual Problems...
Furthermore, another effect is visual problems. Visual problems from computer patient will meet the doctor with symptom blurred vision, double
vision (diplopia), etc. Actually, these problems are not caused by nearsightedness or myopia, farsightedness or hyperopia, astigmatism and refractive
problem, but the effect of these problems are from prolonged computer usage. The study of computer vision syndrome in computer office workers
blurred vision can be found up to 23.8% and this study also found that double vision up to 13.7%. Plus, statistic of the American Optometric
Association was found blurring vision in 30.48%. Visual problems are caused by fatigue of the eye muscles (ciliary muscle) that control the focus of
the image or extraocular muscle that compounds the images from both eyes into a single image (binocular single vision) due to use near vision or
reading vision for a long time. Definition of double vision or diplopia by The Merriam Webster dictionary is a disorder of vision in which two
images of a single object are seen (as from the unequal action of the eye muscles). And the meaning of blurred vision is a loss of visual acuity and
disability to see small details. Which is sometimes related to the inability of the eyes to focus on a computer screen for a significant amount of time.
In addition, visibility may be blurred by constantly changing focus on the keyboard and computer screen. How to avoid the visual problem? There are
many ways to avoid these symptoms,

How Do Driverless Cars Drive Themselves
How do Driverless Cars Drive Themselves? Manufacturers across the globe like Tesla, Mercedes–Benz, BMW, Google, Apple, and Audi are all in a
race to produce the most efficient way of travel. The most efficient seems to be driverless cars, before they were an unachievable dream, but now they
are a reality. Although the most difficult concept of this all is to figure out how they work. Driverless cars are a new technology made from many
older technologies combined, such as the LIDAR system, Computer Vision systems, and Central Hub computer systems. Investments from some
countries like the U.K., who has invested 100 million pounds (130 million U.S.), have driven some of these companies to field test the cars in
everyday environments in places ... Show more content on Helpwriting.net ...
The system is quite similar to sonar and radar, but the only difference would be that LIDAR uses lasers while sonar and radar use sound and radio
waves. What it does is it uses 64 rotating lasers send micro–pulses of light to see where everything is. It uses time of flight which is when it calculates
how long it takes the laser to hit the target and reflect back to the LIDAR system. Millions of data points are taken every second and used to make
a 3D map of its surroundings and used to show where the car can and cannot go. The 3D map made by the LIDAR is processed by a system called
the Computer Vision. It is the only part of the car that can make sense of the 360 degree pictures taken by cameras. What it does is look out for
certain road markers a human would have to look out for such as, traffic lights, road signs, and other cars. It also is constantly on the lookout for other
unimportant things like traffic cones, and the very important things like pedestrians. The last part of the driverless car system is the Central Computer.
The Central Computer reads the data the LIDAR and Computer Vision give and decides where it will guide the car with the steering, acceleration, and
brakes. This computer can determine whether it will hit something directly in front of it or anticipate a possible car crash 200 feet

Document Images Are Acquired By Scanning Journal
Document images are acquired by scanning journal, printed document, degraded document images, handwritten historical document, and book cover
etc. The text may appear in a virtually unlimited number of fonts, style, alignment, size, shapes, colors, etc. Extraction of text from text document
images and from complex color background is difficult due to complexity of the background and mix up of colors of fore–ground text with colors of
background. In this section, we present the main ideas and details of the proposed algorithm. Implementation of any system needs the study of features,
it may be symbolic, numerical or both. An example of a symbolic feature is color; an example of numerical feature is weight. Features may also result
from applying a text extraction algorithm or operator to the input data. The related problems of feature selection and feature extraction must be
addressed at the outset of any text recognition system design. The key is to choose and to extract features that are computationally feasible and reduce
the problem data into a manageable amount of information without discarding valuable information. Different methods used for text extraction from
document images (as shown in fig. 1) include: A.Feature Extraction Feature extraction involves the extracting the meaningful information from the
document image. The features are classified in to Global features and Local features. Features that are extracted from whole image are known as the
global features

Intelligent Traffic Surveillance System Essay
Abstract– Due to the traffic accidents over the last few years; the development of surveillance systems with multifunctional techniques has received
increasing attention. The use of the smart camera is one solution to solve the traffic problems, Smart cameras are cameras that can perform tasks far
beyond simply taking photos and recording videos. Intelligent Traffic Surveillance System (ITSS) is used to monitor the roads in preventing accidents
at the same time finding what causes the accidents. This is done by implementing some image vision protocols as that of Gaussian and Canny. This
paper will discuss about the camera–video–surveillance capabilities of tracking across different and varied road environments including detection of
moving ... Show more content on Helpwriting.net ...
Thus, vehicles detection in cluttered environment is an open problem. The major difficulties lie in:
Vehicles are a non–rigid object. In other words, the shape and size of vehicles vary greatly, and therefore the models of vehicles are much more
complex than that of others objects.
In the case of the clutter background. It does not matter if we are analyzing images from a typical city or from a country traffic environment or
highways; the background formed by trees, wire poles, and billboards is much cluttered. Most of these backgrounds can be taken for vehicles, due to
their similar shapes.
Illumination and weather conditions vary greatly. The on–vehicle vision systems must be able to deal with the different illumination conditions [2].
One of the goals is to develop a system that can be used to Monitor the highways and prevent accidents by implementing an algorithm program in
OpenCV (IntelВ® open–source computer vision library), Real time tracking objects will be achieved by using a smart camera which captures the
frames and sends the image signals to the main server
The system will be a replacement of human for triggering alarms which if something dangerous has happened or potential horrific scenario (potential
accidents) is about to happen. In general this project consists of a smart camera system interconnected with wireless technology for traffic surveillance
which

Robots Guided through Computer Vision
1.1 Basic introduction
Human life is of prime importance. It is the most beautiful and precious gift of God. Sometimes situations occur where we would have no choice and
we have to put someone's life in danger to save many other lives. At that time we would surely think that it would be better if there is some kind of
replacement for human body which can perform human tasks. Furthermore, there are millions of physically challenged people who can't perform day
to day simple tasks which a normal man can easily perform.
The answer and solution to all these problems is ROBOT. Human life is becoming more advanced and safer day by day. Human efforts are replaced
with machines which can perform work accurately, precisely and safely. The idea of a robot to provide assistance either in the home, office or in more
hostile environments (e.g. bomb–disposal or nuclear reactors) has existed for many years and such systems are available today. Unfortunately, they are
typically expensive and by no means ubiquitous in the way that 1950sand 60s science fiction would have had us believe. The major limitations to
including robots in homes and offices are the infrastructure changes they require.
Computer vision however means that robots can be monitored from just a few inexpensive cameras and the recent availability of wireless network
solutions (IEEE 802.11 and Bluetooth in particular) has decimated the infrastructure they demand. The final step to the package is how humans are to
interact

High Level : A Model Based On Finding Shapes And Objects...
2.5.3 High–Level Feature Extraction High–level feature matching based on finding shapes and objects implies knowledge of a mathematical model or
template of a target shape. This technique is a model–based in which the shape is extracted by searching for the best correlation between a known
model and the pixels of the image. Hough transform represents an efficient implementation of computing the correlation between the template and the
image by extracting simple shapes such as lines, circles, and ellipses. Generally, Hough transform and related techniques relied on model–based idea
are perfectly working on the fixed shape in that it is flexible only in terms of the parameters which characterize the shape or the parameters that define
template's appearance [43]. However, in the cases that the exact shape is unknown or it might be that the degree of non–uniformity of the shape is
impossible to be expected and hence to be parameterized, such model–based techniques fail to extract features. This leads to search for techniques that
can evolve to target solution or adapt their result to the data [43]. This represents the milestone of deformable shape analysis. There are many
techniques used in the manipulation of flexible shapes in images, most applicable techniques will be discussed in the following subsections. 2.5.3.1
Deformable Templates The deformable templates technique is based on the analysis of a flexible image by modeling the image as composed of many

Taking a Look at Image Registration
INTRODUCTION
"If we knew what it was we were doing, it would not be called research, would it?"
– Albert Einstein (1879 – 1955)
An image is a 2 dimensional representation of a 3 dimensional scene. A Digital Image is a graphical representation of an object. Digital Image
Processing abbreviated as DIP is the manipulation of digital image by a processor. Image Registration is one of the techniques used in DigitalImage
Processing. Registration refers to merging and fusion of 2 or more data.
Image Registration is a fundamental step in all image analysis tasks in which we find mapping for each and every pair of points in two or more
different images. The images of a same scene are taken from different viewpoints, from different sensors or sometimes images taken at different times
are also geometrically aligned in this process. Virtually all large systems which evaluate images require the registration of images, or a closely related
operation, as an intermediate step.
Registration can be performed either manually or automatically. In manual method human operators manually select corresponding features in the
images to be registered. In order to get reasonably good registration results, an operator has to choose a considerably large number of feature pairs
across the whole images, which is not only tedious and wearing but also subject to inconsistent and limited accuracy. Thus, there is a natural need to
develop automated techniques that require little or no operator supervision.

Applications Of Cnn In Computer Vision Essay
Applications of CNN in Computer Vision
Applications of CNN in Computer Vision Computer vision is a very broad research area which covers a wide variety of approaches not only to process
images but also to understand their contents. It is an active research field for convolutional neural network applications. The most popular of these
applications include, classification, segmentation, detection and scene understanding. Most CNN architectures have been used for computer vision
problems including, supervised or unsupervised face/object classification (e.g., to identify an object or a person in a given image or to output the class
label of that object), detection (e.g., annotate an image with bounding boxes around each object), segmentation ... Show more content on
Helpwriting.net ...
While this CNN model has significantly reduced the error rate for image classification, we shall discuss below more recent and advanced deep CNN
architectures, which have achieved a very high classification performance.
1.1. PointNet
PointNet [3] is a new type of neural network which directly consumes point clouds and well respects the permutation invariance of the points in the
input image.
PointNet, shown in Fig. 1, provides a unified architecture for applications ranging from object classification, and part segmentation, to scene semantic
parsing from pointclouds. It directly takes point clouds as input and outputs either class labels for the entire input, or per point segment labels for each
point of the input. PointNet has three main modules, which we briefly discuss below.
2
1.1.1. Symmetric Function (Max Pooling Layer – Module A):
The first key module is the max pooling layer, used as a symmetric function to aggregate information from all the points, and to make a model
invariant to input permutations. To achieve this, a general function is defined on a point set by applying a symmetric function on the transformed
elements in the set: f(x1, . . . , xn) в‰€ g(h(x1), . . . , h(xn)), (1)
In the above equation, f : 2RN в†’ R, h : RN в†’ RK and g : RK Г— В· В· В· Г— RK
в†’R is a symmetric function. h is approximated by a multi–layer perceptron (mlp) network and g by a composition of a single variable function and a
max pooling function. Through a collection of

Abstract Of Artificial Intelligence

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