Midterms

Question 1

is a type of technology
that enables a computing device to inspect, evaluate
and identify still or moving images.

Answer 1

Machine Vision System (MVS)

Question 2

It is a field in computer vision and is quite similar to
surveillance cameras, but provides automatic image capturing,
evaluation and processing capabilities.

Answer 2

Machine Vision System (MVS)

Question 3

It uses cameras to automatically identify
and locate the pedestrians in images or
videos while considering variations
related to body attire and position,
occlusion, illuminance in different
scenarios, and background clutter.

Answer 3

pedestrian detection

Question 4

Computer vision is used to detect
and classify objects (e.g., road signs
or traffic lights), create 3D maps or
motion estimation, and played a
key role in making autonomous
vehicles a reality.

Answer 4

Self Driving Car

Question 5

The algorithms can now
accurately track and count
freeway traffic or monitor and
analyze traffic density in urban
areas (e.g., at intersections),
helping to design better traffic
management systems and
improve road safety.

Answer 5

Traffic Flow Analysis

Question 6

Both software and hardware
parts of the system should be
customized to the specific
needs of a food company,
including inspection goals
and the type of product to
analyze.

Answer 6

Quality check

Question 7

TRANSPORTATION

Answer 7

Pedestrian Detection
Traffic Flow Analysis
Self Driving Car

Question 8

Food Industry

Answer 8

Quality Check
Measuring, Counting, Sorting
Packaging

Question 9

Automated counting and
sorting system based on
image analysis can grade
fruits, vegetables, nuts,
oysters, etc. according to
their shape, size and maturit

Answer 9

Measuring, Counting, Sorting

Question 10

Automated visual check of a filling
level and package labeling is another
important application of computer
vision in the food industry. Besides
that, a visual system can check the
freshness of a packed product with
the aid of a special ink changing its
color with time and at a different
speed depending on the
temperature.

Answer 10

Packaging

Question 11

Benefits of the automated visual
inspection

Answer 11

1.Better quality
2.Higher productivity
3.Lower scrap and give-away rates
4.Less labor intensity

Question 12

MANUFACTURING INDUSTRY

Answer 12

• Predictive Maintenance
• Product Inspection and Quality Control
• Barcode Scanning and Reading
• Safe Work Environment

Question 13

Taking proactive measures
With machine vision systems, industrial manufacturers can
detect flaws, cracks, or any blemishes in a physical product.
Besides, these systems can easily check for accurate and
precise measurements of components or parts that are used
while the product is being assembled

Answer 13

Preductive Maintenance

Question 14

Robots have been a part of manufacturing companies for a
while now. Now imagine powering these robots with AI and
machine vision. Such robots can easily capture images of
every equipment.

Answer 14

Inspecting Goods

Question 15

By powering machine vision systems with advanced
capabilities like Optical Character Recognition (OCR), Optical
Barcode Recognition (OBR), and other image processing
technologies, manufacturers can use the system to automate
the overall scanning process. The images of the text or
barcodes on an item will be captured, interpreted, and
compared with the information stored in a system.

Answer 15

Reading Barcodes

Question 16

Providing improved health to workers and keeping the
environment safe is of prime concern for manufacturers.
Machine vision systems, with real-time image-processing
capabilities, can gather pictures of the manufacturing plant.
The pictures will also capture workers and their actions. The
data is then fed to a computer system where it is analyzed
and processed.

Answer 16

Improving Worker Safety

Question 17

is an
advanced technology that enables
you to manipulate digital images
through computer software.

Answer 17

Digital Image Processing

Question 18

or picture
element) is the smallest item of
information in an image.

Answer 18

Pixel

Question 19

are arranged in a 2-dimensional
grid, represented using squares. Each
pixel is a sample of an original image,
where more samples typically provide
more-accurate representations of the
origina

Answer 19

pixel

Question 20

based on a contraction
of pix (“pictures”) and el (for “element”).

Answer 20

pixel

Question 21

often used as a pixel
count in digital imaging.

Answer 21

resolution

Question 22

reads the image from the file specified by filename

Answer 22

imread()

Question 23

displays the image stored in the graphics file specified
by filename

Answer 23

imshow()

Question 24

converts the truecolor image RGB to the grayscale image

Answer 24

rgb2gray

Question 25

t hi s I m a g e = i m r e ad (thi s F i l e );
I m s ho w(t hi s I m a g e ( y1 : y2 , x 1 : x 2 ));

Answer 25

resizing the image

Question 26

is a patented
local feature detector and
descriptor.

Answer 26

SURF Speeded Up Robust Feature

Question 27

can be used for tasks such as
object recognition, image
registration, classification, or 3D
reconstruction.

Answer 27

SURF Feature

Question 28

effect is a non-arbitrary mapping between speech sounds and the
visual shape of objects. It was first documented by Wolfgang Köhler in 1929
using nonsense words.

Answer 28

bouba/kiki effect

Question 29

A three-dimensional RGB matrix. Each layer of the
matrix is a two- dimensional matrix of red, green or
blue pixel values.

Answer 29

3 Layered Matrix (RGB)

Question 30

detector uses the Viola-
Jones algorithm to detect people’s faces,

noses, eyes, mouth, or upper body.

Answer 30

cascade object detector

Question 31

BBOX = step(detector,I)

Answer 31

Cascade Object Detector

Question 32

an M-by-4 matrix

defining M bounding boxes containing the detected objects.

Answer 32

BBOX

Question 33

contains a four-element vector, [x y width height], that
specifies in pixels, the upper-left corner and size of a bounding box.

Answer 33

BBOX

Question 34

is a distinct region in an image

Answer 34

feature

Question 35

an
iterative method for estimating a mathematical
model from a data set that contains outliers.

Answer 35

Random sample consensus, or RANSAC,

Question 36

algorithm works by identifying the outliers in a
data set and estimating the desired model using data
that does not contain outliers.

Answer 36

RANSAC