Detection versus recognition ... Investigate Face++ at Feel free to.pdf
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Detection versus recognition ... Investigate Face++ at Feel free to browse to other similar sites Explain the difference between face detection and face recognition. Give an example use of each in IT. Solution solution: Face detection is a program that determines the locations of human faces in a digital image. Face recognition is a program that identifies a person in a digital image. There are several notions that should be distinguished. Classification = partition of a set of observable objects into disjoint similarity classes (maybe, constituting a hierarchical structure). Detection = in a large set of objects finding out all those belonging to a certain similarity class. Recognition = for a given object answering, to what of (a priori defined) similarity classes it belongs. Identification = a particular case of recognition: proving that a given object really belongs to a similarity class being declared. Face detection aims at the detection/location of face in an image while Face recognition aims at identifying the face with some known faces. The best example for face detection is our Digital Cameras, you see a square over the faces, when taking photos. Face recognition is usually used in forensics to identify fugitives from street cameras... or something like that. face detection is an attempt to detect faces in an image (or video, I guess). Face detection software will typically provide the location and face size/orientation of each region of the image it feels is probably a face. In my experience, the results can be quite noisy. Depending on how the detection parameters are set, you may get many non-faces and will likely miss some/many faces (especially if they are partially obscured). Face recognition, on the other hand, attempts to identify a face in an image that is known (or thought to) contain a single face. Face detection may be applied first to extract image segments containing faces. A database of face data for individuals is needed, and the face recognition software will attempt to associate the provided image with one or more records in the database, typically with a probability that the faces match. Eigenfaces & Fisherfaces Those familiar with linear algebra will remember that every vector space has an orthogonal basis. By combining elements of this basis we can compose every vector in this vector space. And vice versa, every vector in the vector space can be decomposed to the elements of the basis. Images (grayscale) are nothing more than a series of numbers, each number corresponding to some intensity level. So why not treat images as vectors? Say, for example, we have a collection of face images of size 150 by 150 pixels; each of these images can be thought of as a vector of size 22,500 (150*150). We can now talk about the vector space in which these vectors reside. By treating the images as samples of data, we can perform a Principal Components Analysis and obtain the eigenvectors which make up the basis of the vector spa.
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Any camera in the last 10 years, probably has face detection in action:
Face detection is a great feature for cameras. When the camera can automatically pick out
faces, it can make sure that all the faces are in focus before it takes the picture.
But this concept uses it for a different purpose — finding the areas of the image that has to be passed to the next step of the procedure.
To find faces in an image, the image has to be in the black and white format as colour data
is not necessary.
Now we are to the meat of the problem — actually telling faces apart. The solution is to train a Deep Convolutional Neural Network. But instead of training the network to recognize pictures objects like we did last time, we are going to train it to generate 128 measurements for each face.
The training process works by looking at 3 face images at a time:
Load a training face image of a known person.
Load another picture of the same known person.
Load a picture of a totally different person.
Then the algorithm looks at the measurements it is currently generating for each of those three images. It then tweaks the neural network slightly so that it makes sure the measurements it generates for #1 and #2 are slightly closer while making sure the measurements for #2 and #3 are slightly further apart.
After repeating this step millions of times for millions of images of thousands of different people, the neural network learns to reliably generate 128 measurements for each person. Any ten different pictures of the same person should give roughly the same measurements.
Machine learning people call the 128 measurements of each face an embedding. The idea of reducing complicated raw data like a picture into a list of computer-generated numbers comes up a lot in machine learning.
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In class, we talked about all sorts of experiments. I would like to s.pdf
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Solution
a) Young\'s Double-Slit Experiment
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than particles. The principle behind this phenomenon is diffraction of light due to this any point
source of light spreads and behaves like a light source.
In the early 19th century, English scientist Thomas Young carried out the famous double-slit
experiment (also known as Young\'s experiment), which demonstrated that a beam of light, when
split into two beams and then recombined, will show interference effects that can only be
explained by assuming that light is a wavelike disturbance. If light consisted strictly of ordinary
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shape of the slit. However, when this single-slit experiment is actually performed, the pattern on
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the angle of spread.Similarly, if light consisted strictly of classical particles and we illuminated
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slit patterns. In actuality, however, the pattern changes to one with a series of alternating light
and dark bands. When Thomas Young first demonstrated this phenomenon, it indicated that light
consists of waves, as the distribution of brightness can be explained by the alternately additive
and subtractive interference of wavefronts.
b) Photoelectric effect Experiment
When light is shined on clean sodium metal in vacuum then ejection of electrons was observed.
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electrons was proportional to the frequency of the illuminating light. This showed that whatever
was knocking the electrons out had an energy proportional to light frequency. The remarkable
fact that the ejection energy was independent of the total energy of illumination showed that the
interaction must be like that of a particle which gave all of its energy to the electron!
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blocked and some passes through the slits, depending on which ones are open.
Ba.
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Face detection is a great feature for cameras. When the camera can automatically pick out
faces, it can make sure that all the faces are in focus before it takes the picture.
But this concept uses it for a different purpose — finding the areas of the image that has to be passed to the next step of the procedure.
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is not necessary.
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Load a training face image of a known person.
Load another picture of the same known person.
Load a picture of a totally different person.
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In class, we talked about all sorts of experiments. I would like to s.pdf
In class, we talked about all sorts of experiments. I would like to sum them up here. For each of
the following, name and briefly describe an experiment that... (a)...demonstrates that light
behaves like a wave. (b)...demonstrates that light behaves like particles. (c)...demonstrates that
electrons behave like particles. (d)...demonstrates that electrons behave like a wave.
Solution
a) Young\'s Double-Slit Experiment
A definitive experiment was Young\'s double slit experiment, which demonstrated that light
shined at two slits in a screen show an interference pattern characteristic of waves of light, rather
than particles. The principle behind this phenomenon is diffraction of light due to this any point
source of light spreads and behaves like a light source.
In the early 19th century, English scientist Thomas Young carried out the famous double-slit
experiment (also known as Young\'s experiment), which demonstrated that a beam of light, when
split into two beams and then recombined, will show interference effects that can only be
explained by assuming that light is a wavelike disturbance. If light consisted strictly of ordinary
or classical particles, and these particles were fired in a straight line through a slit and allowed to
strike a screen on the other side, we would expect to see a pattern corresponding to the size and
shape of the slit. However, when this single-slit experiment is actually performed, the pattern on
the screen is a diffraction pattern in which the light is spread out. The smaller the slit, the greater
the angle of spread.Similarly, if light consisted strictly of classical particles and we illuminated
two parallel slits, the expected pattern on the screen would simply be the sum of the two single-
slit patterns. In actuality, however, the pattern changes to one with a series of alternating light
and dark bands. When Thomas Young first demonstrated this phenomenon, it indicated that light
consists of waves, as the distribution of brightness can be explained by the alternately additive
and subtractive interference of wavefronts.
b) Photoelectric effect Experiment
When light is shined on clean sodium metal in vacuum then ejection of electrons was observed.
Analysis of data from the photoelectric experiment showed that the energy of the ejected
electrons was proportional to the frequency of the illuminating light. This showed that whatever
was knocking the electrons out had an energy proportional to light frequency. The remarkable
fact that the ejection energy was independent of the total energy of illumination showed that the
interaction must be like that of a particle which gave all of its energy to the electron!
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Ba.
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silica exhibits very high material absorption in this spectral region. Afterwards, Kosolapov et al.
extended the transmission window in a similar structured HC-NCF to 10.6 m for CO2 laser by
using chalcogenide glass instead of silica as the fabrication material. In 2012 during the course of
this thesis I designed, fabricated and characterised a silica HC-NCF with low loss transmission in
the mid-infrared spectral region from 3 µm to 4 µm, with minimum attenuation of 34 dB/km at a
wavele.
Can we create an object of type Interface Explain your answer.So.pdf
Can we create an object of type Interface? Explain your answer.
Solution
The answer is w.r.t JAVA
You can\'t instantiate an interface directly, but you can instantiate a class that implements that
interface:
This is basically the same as what you\'re doing inline. The brackets after new Runnable() will
contain your implementation inline..
Cancel QuestionSolutionSince Descriptive statistics uses the d.pdf
Cancel Question
Solution
Since Descriptive statistics uses the data to provide descriptions of the population, either through
numerical calculations or graphs or tables. Inferential statistics makes inferences and predictions
about a population based on a sample of data taken from the population in question.
So a) Is Descriptive
b) is Descriptive
c) is Inferential
d) is Inferential
e) is Descriptive
f) is Descriptive.
According to the cladogram, what derived trait is shared by primates .pdf
According to the cladogram, what derived trait is shared by primates and rodents/rabbits? What
is the correct term for the area labeled with a circle? What does it represent? What is the correct
term for the earliest branch pictured in the cladogram (the one with the shark)? What does it
represent?
Solution
PART1)According to the cladogram,hair trait is shared by primates and rodden rabbis.........
PART2)IT represents they are reptiles....
PART3)IT represents it is the oldest organism...........
A male Flagus fly with the Barkus phenotype is crossed with a female.pdf
A male Flagus fly with the Barkus phenotype is crossed with a female who has the wild-type
phenotype. A second cross is performed in which the female Flagus fly has the Barkus
phenotype and the male has the wild-type phenotype. This second cross is known as a:
A. inter-cross.
B. recessive cross.
C. motor-cross.
D. dominant cross.
E. reciprocal cross.
Solution
E. Reciprocal Cross.
2015Matthew and Michael Goode (cousins) decide to form a partners.pdf
2015:
Matthew and Michael Goode (cousins) decide to form a partnership (M&M) on April 1, 2015.
Each contributed personally-owned assets to start the new company. Matthew Goode contributed
$100,250 in cash and property with book value of $57,750 and appraised value of $78,750.
Michael Goode contributed cash of $99,750; computer equipment with book value of $52,500
and appraised value of $21,000; a software library with book value of $63,000 and appraised
value of $23,500; and office equipment with book value of $57,750 and appraised value of
$26,250. M&M operates on a calendar year.
The Articles of Partnership stipulate that no goodwill is to be recognized and that the two will
start with capital balances equal to their contributions. Monthly cash draws of $1,350 each are
allowed, beginning June 1, 2015. Net income is to be allocated as follows:
(a)Matthew is to receive a compensation allowance of $2,625 per month for managing the day-
to-day operations of the partnership.
(b)Interest of 12.25% to be paid on ending balances of capital accounts, PLUS 15% interest on
ending capital balances in excess of $262,500 (all interest to be calculated without current
income allocation).
(c)Each partner is to receive a bonus equal to 15% of net income, after the compensation
allowance, interest, and bonuses. Negative bonuses are not allowed.
(d)Remainder of profit to be allocated in a 55:45 ratio, Matthew:Michael.
(e)Remainder of loss to be allocated in a 50:50 ratio.
Net partnership income for 2015 was $210,000.
2016:
June 1, 2016, Maxwell Goode (Matthew’s twin brother) joins the partnership (ThreeM). He
purchases 1/3 of Matthew’s ownership interest for cash of $78,750 and 1/4 of Michael’s
ownership interest for cash of $63,000, paid directly to the partners.
The Articles of Partnership are amended as follows for allocation of net income:
(a)In addition to Matthew’s compensation allowance, Michael will receive a compensation
allowance of $1,575 per month, and Maxwell $1,260 per month.
(b)Unchanged.
(c)Matthew, Michael, and Maxwell are to receive bonuses of 12.5%, 12.5% and 10%,
respectively, of net income after the compensation allowances, interest, and bonuses. Negative
bonuses are not allowed.
(d)Remainder of profit to be allocated in a 40:35:25 ratio, Matthew:Michael:Maxwell.
(e)Remainder of loss to be allocated equally among the three partners.
Net partnership income for 2016 was $262,500.
2017:
Morgan (Michael’s younger brother) is admitted to the partnership (MM MM Goode) on January
2, 2017. He purchases a 32% interest for a cash payment of $260,000, paid directly to the
partnership.
The Articles of Partnership are amended as follows for allocation of net income:
(a)Partners will receive the following compensation allowances per month: Matthew, $3,150;
Michael, $2,625; Maxwell, $2.200; and Morgan, $2,000.
(b)Unchanged.
(c)Matthew, Michael, Maxwell, and Morgan are to receive bonuses of 12%, 12%, 10%, and
10%, respectively, of net income after the co.
1. Jose is a Latino client who presents with occupational difficulti.pdf
1. Jose is a Latino client who presents with occupational difficulties. He was laid off from a
construction job 8 months ago and has been unable to find work. Ever since he lost his job, he
has been drinking alcohol heavily throughout the day. He also reports “occasional” marijuana
use. Jose recently was arrested for driving under the influence of alcohol and has to appear in
court next month. His wife is angry with him because she depends on him to pick up their 3
children from school, and now he may lose his driver’s license. Jose has always been a hard
worker, but he admits that there were times that he often drank alcohol while working on
construction jobs to eliminate his stress. He reports having intermittent episodes of anxiety since
adolescence. Based on the information provided, Jose’s principal diagnosis would likely be: A.
Alcohol use disorder B. Cannabis use disorder C. Adjustment disorder with mixed anxiety and
depressed mood D. Major depressive anxiety disorder
Solution
A. Alcohol use disorder
Heavy drinking leads to \"alcohol use disorder.\" It is characterized by increased tendency to
drink alcohol, inability to quit alcohol, neglecting the family responsibilities due to heavy
drinking, anxiety, depression and alcohol withdrawal symptoms such as difficulty in sleeping,
seizures, high blood pressure, etc..
You have a small business customer who wants to use a voice over IP .pdf
You have a small business customer who wants to use a voice over IP (VoIP) system and
subscribe to this service through his ISP using DSL. What special piece of equipment will need
to be installed to provide analog-to-digital signal conversion between the phones and the digital
network?
A. A cable modem
B. A DSL modem
C. An Integrated access device (IAD)
D. A multimedia terminal adapter (MTA)
Solution
a cable modem is anetwork bridge and modem connects bi-directional data communication radio
frequncy channel on a hybrid fibar co axial and radio frequncy over glass infra structure.
A digital subscriber line (DSL) modem is a device used to connect a computer or router to a
telephone line which provides the digital subscriber line service for connectivity to the Internet.
An Integrated Access Device (or IAD) is a customer premises device that provides access to
wide area networks and the Internet.
A multimedia terminal adapter (MTA), or Home Phone modem, operates much the same as a
cable modem, but an MTA can deliver Home Phone service in addition to High Speed Internet.
so answer is B. A DSL Modem.
Why can some microbial species grow and survive in extreme environ.pdf
Why can some microbial species grow and survive in extreme environments?
Why can some microbial species grow and survive in extreme environments?
Solution
Microbial species grow and survive in extreme environments. These organisms are the
extremophiles. There are different types of extremophiles:
-Thermophiles and hyperthermophiles (organisms growing at high or very excessive
temperatures, respectively)
-psychrophiles (organisms that develop best at low temperatures)
-acidophiles and alkaliphiles (organisms optimally tailored to acidic or fundamental pH values,
respectively)
-barophiles (organisms that develop quality underneath pressure)
-halophiles (organisms that require NaCl for increase).
The different types of extremophiles have exceptional adaptations that allow them to thrive
mainly extremes.
1.Life at High Temperatures. Thermophiles have molecules and cell membranes that preserve
their stability at excessive temperatures. As all cellular additives need to be made heat stable and
their evolutionary function is derived of all recognized lifestyles.
2.Life at Low Temperatures and Extreme pH Levels. The molecules and membranes of
psychrophiles are designed to maximise flexibility. Extremophiles that grow exceptional at pH
extremes maintain a neutral pH inside their cells.
3.Life in a Brine and Other Environments. Halophiles ought to keep a balance between the
solute concentration in their cells and their salty surroundings. Halophiles are able to live in salty
conditions by preventing dehydration of their cytoplasm.a
Radiation-resistant micro organisms adaptedto literally shatter its chromosome into hundreds of
pieces. Some extreme barophiles grow optimally at pressures over 500 atmospheres. The
transmembrane proteins in barophiles likely are modified to still function at high pressures..
Which prokaryotic group maps most closely to the chlorophyll of plan.pdf
Which prokaryotic group maps most closely to the chlorophyll of plants on the phylogenetic
tree? cyanobacteria planctomyces proteobacteria spirochetes
Solution
cyanobacteria maps most close to chlorophyll of plants as cyanobacteria contains chlorophyll
and psycobillins though chlorophyllin them is a little different from that of eukaryotes ..... hope it
helps.
Which of the following static methods allow an ArrayList of strings t.pdf
Which of the following static methods allow an ArrayList of strings to be passed in as the
parameter? public static void methodA(ArrayList param) {\\\\ do something} public static void
methodB(ArrayList param) {\\\\ do something} methodA only methodB only both methodA and
methodB
Solution
Only method B allows an arraylist of strings to be passed in as the parameter as it accepts any
type which extends hasWord.
In the case of methodA, it will be required to create a new ArrayList of the type Object to pass
into the method..
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