Posters to help compsci students select a research lab.

1. Key details
• What is a barcode?
Barcodes are images consisting of a combination of
bars and spaces that represents numbers and letters in
a machine-readable manner. It is read by an optical
recognition device called a bar code scanner.
• What is image recognition?
Image recognition is a technology that allows
computers and machines to identify what is in an
image. In recent years, the accuracy has been further
improved by a method called deep learning. Deep
learning eliminates the need for humans to extract
features from data. It automatically extracts features
from a given image data from start to finish.
AI : Image Recognition
Hook
Do you know how Barcodes work? Bar codes, which began
to be utilized in the 1940s, are considered the oldest image
recognition technology. Many people can easily imagine it
because it appears on the packaging of various products.
Let's learn more about image recognition technology using
such barcodes as an example.
More details
https://www.denso-wave.com/
1. A bar code consists of black and white bars. A bar
code scanner shines light on these bars, captures the
reflected light, and converts the black and white into
a binary digital signal to retrieve data.
2. White areas are highly reflective and black areas are
less reflective. The sensor receives this reflected light
and obtains an analog waveform.
3. The analog signal is converted to a digital signal by an
A/D converter. (Binarization)
4. The code system is determined from the obtained
digital signal and the data is extracted. (decoding
process)
This is the basic barcode system. The basic system requires
a dedicated scanner, but with AI technology, even dark,
remote, or multiple barcodes can be read using a
smartphone camera.

2. Computer vision : Image recognition technology(IRT)
Hook
Have you ever heard of an A I that, upon input of text, generates an image
according to instructions? This is done through image recognition
technology. This technology has begun to spread in our daily lives, such as
face recognition in smartphones and self-driving ads in cars. How is AI
involved in image recognition technology, and what is actually so great
about it that it is becoming a hot topic? Let's deepen our understanding of
how image recognition technology works and its potential.
Key details
・About Computer Vision
Computer vision is an academic field that studies the realization of vision
using computers. It uses certain "image data" and performs some kind of
processing to achieve the same visual processing as human vision. It is a
technology that aims to extract three-dimensional information from two-
dimensional image data taken of the real world for humans to recognize
and process the information in them with their minds.
・About Image recognition technology
Image recognition technology is a technology that uses a computer to
identify objects in image or video data. For example, when a photograph of
a vegetable is loaded, the computer can instantly determine whether it is a
radish, a carrot, or a cabbage. Advances in A I, such as machine learning and
deep learning, have improved the accuracy of image recognition
technology, and it is now being used in a variety of fields.
More details
・Types of Image Recognition
①Object Detection
Technique for recognizing objects in images and videos. When a human viewer looks at an image or video, he
or she can immediately discern information about a person or object.
②Face Recognition
Refers to a technology that extracts prominent features from a facial image. It is a method that uses a
computer system to reproduce the means by which humans normally discriminate between people.
③Character Recognition
Identifying handwritten or printed characters on paper.
Example
『Image Generation AI』
A Sacred and
Wonderful Oil
Painting of a Really
Fat Cat Celebrating
the Coming of a
New Age
This picture is drawing by DALL・E2
Robots composed
of complex
structures
This picture is drawing by Midjourney

3. Deep learning: Face Recognitnion
Hook
Face Recognition Algorithm
Key details
More details
1. This is equal to the straight line distance or shortest distance or displacement between two points (..assume in two
dimensions but it can be in more dimensions).
3. After calculating the Euclidean distance, the algorithm either generates a new personID for the unknown type of person
(if the distance is greater than the key value) or marks the face as classified and matches the personID (if the distance is
less than the key value). In case of FACE01 GRAPHICS, the key value is usually 0.45 as the base value. Through deep
learning algorithm called ResNet, 128 dimensional feature vectors of various face images are trained to minimize the
distance between them for the same person. Therefore, the same person is determined by measuring the distance
between the vectors of each face image and calculating whether the distance is smaller than a key value.
(If you want to Algorithm which can detect mask-on face)
->The ‘CNN’ method is recommended for detecting a group of faces or faces with mask on In this case, a fast CPU and a fast
NVIDIA graphics card are required.
4. It was developed by Kaiming He at Microsoft Research, and is a 152-layer! The neural network was developed by Mr.
Kaiming He, who worked at Microsoft Research. This is the property that if the same number of neurons are to be
trained, the performance will be better if the depth of the hierarchy is deeper than if the width of each layer of the
network (the number of neurons) is wider. (This is a rule of thumb, and it is not well understood why this is the better
way to go theoretically.) But, If too many layers are added, problems such as vanishing or exploding of gradients occur,
and the system cannot learn well.
(The problem when he makes Resnet Algorithm)
-> When learning with Deep Learning, the activation function is differentiated for each layer to obtain the slope. In the
"shallow" layer, which is close to the input layer, the differential operation works well because the difference between the
input and output to that layer (accuracy) is large. However, as the learning progresses to the "deeper" layers, the accuracy of
the input-to-output conversion increases as the learning approaches convergence (as it should), and the difference between
the input and output becomes very small, making it difficult to obtain a gradient. Since propagation from layer to layer is
multiplicative in nature, the decreasing trend of the difference is exponential. In other words, as more layers are added, the
gradient quickly disappears.
*face01 graphics(Library for face detection and recognition in Python and command line. Also, It was trained on a dataset
of approximately 3 million images using deep learning.)
(Back end of software)
-> On the back end, the algorithm identifies a record with "classified = false" and uses a function to generate a 128-dimensional
vector detailing the attributes of this face. The algorithm cross-references this vector with all face entries in the database using
Euclidean distances to discover if this new face matches the face on the record.
This Photo by Unknown Author is
licensed under CC BY-SA
This Photo by Unknown Author
is licensed under CC BY
1. Euclidean distance -> This is equal to
the straight line distance or shortest
distance or displacement between two
points (..assume in two dimensions but
it can be in more dimensions).
2. Triplet training -> The training
consists of a set of three face images
and labeling each of the three images as
Positive if it’s similar to one of the
images and Negative if it’s dissimilar to
one of the images.
3. How to compare face (Algorithm) ->
Compare the learned images with the
detected facial features.
4. Resnet - > The algorithm of face
recognition
This Photo by Unknown Author
is licensed under CC BY-SA
Used like
this