Waymo Essay

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
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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

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

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) ≈ g(h(x1), . . . , h(xn)), (1)
In the above equation, f : 2RN → R, h : RN → RK and g : RK × · · · × RK
→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

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 Digital Image
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.

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

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

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

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

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

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 Reading 3
Introduction 3
Camera Mouse 3
Computer Vision 3
Face Detection 3
Face Tracking 4
OpenCV 4
Computer Vision Based E–mail System 4
Project Goals 5
Project details 5
Functionality and Design 6
Hardware and Software required 7
Methodology 7
Project Planning 7
Reference 9
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

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 for corner 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 first defined by Noble [12] from which a slightly different 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, defines 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 affine 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

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

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

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,

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

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

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

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

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

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,

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

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
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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 (Intel® 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

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. Various edge 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

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 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

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

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

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

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

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

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 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

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

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 the Automobile for Young
People." 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 for Artificial
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 A History of
Autonomous Vehicles Comments. Computer History Museum, 8 May 2014comput. Web. 10 Oct.
2016. <http://www.computerhistory.org/atchm/where–to–a

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