This offline lesson plan teaches students about computer graphics through a pixel art activity. The five-part lesson covers computer programs, pixels, and how images are created using grids of colored pixels. Students will practice selecting pixels using row and column coordinates. They will color pixel grids to draw simple images, like spelling words, according to given coding instructions. Finally, students will create their own pixel art by writing pixel coordinate and color codes.
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.
This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
After doing the comparative analysis by processing all the known images, our software finally tells the result of test image by displaying its name.
We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
When, we isolated the faces in our image. But now we have to deal with the problem that faces turned different directions look totally different to a computer:
To account for this, we will try to warp each picture so that the eyes and lips are always in the sample place in the image. To do this, we are going to use an algorithm called face landmark estimation .
The basic idea is we will come up with 68 specific points (called landmarks) that exist on every face — the top of the chin, the outside edge of each eye, the inner edge of each eyebrow, etc. Then we will train a machine learning algorithm to be able to find these 68 specific points on any face.
It has successfully run the alg
Computer Graphics and its applications, Elements of a Graphics, Graphics Systems: Video Display Devices, Raster Scan Systems, Random Scan Systems, Input devices.
: Introduction of Rendering, Raytracing, Antialiasing, Fractals
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.
This last step is actually the easiest step in the whole process. All we have to do is find the person in our database of known people who has the closest measurements to our test image that we show in front of the web cam.
After doing the comparative analysis by processing all the known images, our software finally tells the result of test image by displaying its name.
We did it by using any basic machine learning classification algorithm. No fancy deep learning tricks are needed.
When, we isolated the faces in our image. But now we have to deal with the problem that faces turned different directions look totally different to a computer:
To account for this, we will try to warp each picture so that the eyes and lips are always in the sample place in the image. To do this, we are going to use an algorithm called face landmark estimation .
The basic idea is we will come up with 68 specific points (called landmarks) that exist on every face — the top of the chin, the outside edge of each eye, the inner edge of each eyebrow, etc. Then we will train a machine learning algorithm to be able to find these 68 specific points on any face.
It has successfully run the alg
Computer Graphics and its applications, Elements of a Graphics, Graphics Systems: Video Display Devices, Raster Scan Systems, Random Scan Systems, Input devices.
: Introduction of Rendering, Raytracing, Antialiasing, Fractals
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Introduction, graphics primitives :Pixel, resolution, aspect ratio, a frame buffer. Display devices, and applications of computer graphics.
Scan conversion - Line drawing algorithms: Digital Differential Analyzer (DDA), Bresenham’s Circle drawing algorithms: DDA, Bresenham’s, and Midpoint.
YCIS_Forensic PArt 1 Digital Image Processing.pptxSharmilaMore5
Basics of Digital Image Processing
Use of DIP in Society
Digital Image Processing Process
Why do we process images?
Image Enhancement and Edge detection
Python
How are we using Python in DIP
Introduction to Digital Image Processing Using MATLABRay Phan
This was a 3 hour presentation given to undergraduate and graduate students at Ryerson University in Toronto, Ontario, Canada on an introduction to Digital Image Processing using the MATLAB programming environment. This should provide the basics of performing the most common image processing tasks, as well as providing an introduction to how digital images work and how they're formed.
You can access the images and code that I created and used here: https://www.dropbox.com/sh/s7trtj4xngy3cpq/AAAoAK7Lf-aDRCDFOzYQW64ka?dl=0
Introduction, graphics primitives :Pixel, resolution, aspect ratio, a frame buffer. Display devices, and applications of computer graphics.
Scan conversion - Line drawing algorithms: Digital Differential Analyzer (DDA), Bresenham’s Circle drawing algorithms: DDA, Bresenham’s, and Midpoint.
YCIS_Forensic PArt 1 Digital Image Processing.pptxSharmilaMore5
Basics of Digital Image Processing
Use of DIP in Society
Digital Image Processing Process
Why do we process images?
Image Enhancement and Edge detection
Python
How are we using Python in DIP
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...
Coding - Pixel Art
1. This offline lesson plan covers the basics of computer
graphics. After learning about how graphics work,
students will create their own Color by Pixel programs.
The lesson plan consists of four parts, each covering a
specific topic. The entire five-part Color by Pixel lesson
is designed to run approximately one hour.
Copies of the Color by Pixel template sheets
(included in the Resources section).
Colored pencils, crayons, markers, or other
supplies for coloring.
WHAT YOU’LL NEED
2. Computers and computer graphics go hand in
hand. In this lesson, we’ll learn how computers
draw images using pixels.
PART 1: Programming and Pixels 10 min
TOPIC
- What is a computer program?
- What is a pixel?
- How does a computer program use
pixels to display an image?
QUESTIONS
- Code: Code is the name for the
instructions you write to a computer in a
program.
- Computer: A person or device that makes
calculations, stores data, and executes
instructions according to a program.
- Computer program: A list of code
instructions a computer follows in order to
perform a task.
- Computing: Executing instructions,
calculating, or using a computer.
- Pixel: A single point or dot of color in a
larger image. Large images are made up of
thousands of small pixels.
VOCABULARY
Would you enjoy playing games on a computer
that didn’t have a screen with graphics? Probably
not. Computer graphics and animation have
become an important part of how we interact with
computers today.
Computer programs are like lists of instructions
that we give to a computer. Computers are not
very capable on their own -- they need good
instructions to tell them what to do and how to do
it. Programmers can create instructions for
computers by writing computer code that tells the
computer exactly what to do.
But how do programs draw pictures onto the
screen? Computers break each picture down into
tiny blocks of color, called pixels, that are
organized in a grid across the screen. Each pixel
contains one single color. By combining
thousands and thousands of these tiny pixels,
programs can draw complicated pictures onto the
screen.
DISCUSSION
3. A demonstration of how to use pixels to make a
simple image.
PART 2: How Do They Work? 15 min
TOPIC
- What format will we use to select and
color a pixel for this exercise?
- It’s important to not mix up rows and
columns. Which direction do rows run?
And which way are columns?
- What number do the rows and columns
start from?
QUESTIONS
- Row: Rows run horizontally (side to side)
- Column: Columns run vertically (up and
down)
VOCABULARY
The image below is a very simple example of
using pixels to draw a basic image. Each box in
the grid is a single pixel. In this case, some of the
pixels have been colored to make a smiley face.
We can label the rows and columns of the image
to make it easier to pick out specific pixels:
Now that we have row and column numbers, we
can select individual pixels. For example, the
smiley face’s right eye is located in column 3, row
1 (notice that we start counting at zero). We can
communicate this more quickly by writing all the
pixel coordinates in this format: (column, row).
We’ll write the color of the pixel rightly after the
column and row position. So to color the right
eye, we can write: (3, 1, green).
Using these coordinates, we can paint the
smiley face above with the following program:
1. (1, 1, green)
2. (3, 1, green)
3. (0, 3, green)
4. (4, 3, green)
5. (1, 4, green)
6. (2, 4, green)
7. (3, 4, green)
DISCUSSION
Page 2
Columns
0 1 2 3 4
0
1
2
3
4
4. Now that students have learned the basics of
how images are drawn with pixels, it’s time to
practice drawing some images.
The first activity has students practice drawing
out a simple message on the screen. The
second activity is slightly harder, but results in
a cool heart image.
1. YOUR FIRST PIXEL PROGRAM
CODE
1. (0, 0, yellow)
2. (2, 0, yellow)
3. (4, 0, yellow)
4. (0, 1, yellow)
5. (2, 1, yellow)
6. (4, 1, yellow)
7. (0, 2, yellow)
8. (1, 2, yellow)
9. (2, 2, yellow)
10. (4, 2, yellow)
11. (0, 3, yellow)
12. (2, 3, yellow)
13. (4, 3, yellow)
14. (0, 4, yellow)
15. (2, 4, yellow)
16. (4, 4, yellow)
PART 3: Activity - Color by Pixel 20 min
TOPIC
Give each student a “computer screen” grid
and instructions sheet. By following the
program next to the grids, students will be
“human computers” who will draw out images
onto the screen.
SETUP
Page 3
Solution
0 1 2 3 4
0
1
2
3
4
6. Now students can write code to make their very
own pixel image!
PART 4: Create Your Own!
TOPIC
Give each student a copy of the blank “My Pixel
Image” page. Students can write their code in the
area provided. Note that it might be a good idea
to make a rough draft image first to help with
writing the code -- having extra copies of the My
Pixel Image handout would be beneficial.
To extend this activity, have each student pair up
with another student after they have finished
writing their code. Using Student A’s code instruc-
tions, Student B can be the human computer and
draw out the image. The students can then switch
roles and Student A can draw out Student B’s
program.
DISCUSSION
Page 5
20 min
7. Page 6
Computer graphics are all around us, from the phones in our pockets to the movies we watch. In
order to display images, computers follow basic lists of instructions called programs. Each image is
made up of individual pixels. Each pixel only displays one color, so computers combine thousands of
pixels in a grid in order to display complex images. Displaying an image is like giving the computer a
list of instructions of which pixels need to be colored in a certain color.
To continue learning, visit www.CodeHS.com
PART 5: Conclusion
TOPIC
Part 1
- What is a computer program?
- What is a pixel?
- How does a computer program use
pixels to display an image?
Part 2
- What format will we use to select and
color a pixel for this exercise?
- It’s important to not mix up rows and
columns. Which direction do rows run?
And which way are columns?
- What number do the rows and columns
start from?
DISCUSSION QUESTIONS
Part 1
- Code: Code is the name for the instructions you
write to a computer in a program.
- Computer: A person or device that makes
calculations, stores data, and executes
instructions according to a program.
- Computer Program: A list of code instructions a
computer follows in order to perform a task.
- Computing: Executing instructions, calculating,
or using a computer.
- Pixel: A single point or dot of color in a larger
image. Large images are made up of thousands
of small pixels.
Part 2
- Row: Rows run horizontally (side to side)
- Columns: Columns run vertically (up and down)
VOCABULARY
20 min
8. -Blank “code editor” and “computer screen” sheet for the Your First Pixel Program and Computer
Science Love exercises.
-Blank “My Pixel Image” page for the Create Your Own activity
Page 7
RESOURCES