Getting started with Computer Vision

1 An introduction to
computer vision
a. FAQs
What is computer vision?
Of the five human senses, vision is the one that provides most
of the data we receive and is considered our dominant sense.
It provides us with a detailed description of the surrounding
world which is constantly changing. Although vision involves
a huge amount of information and complex processing, the
human visual system can interpret this information easily.
The ability to see, process and then act on visual input is
something that most humans take for granted.
Computer vision engineering is the practice of using
technology and machines to replicate, and even improve upon,
human vision. The technology captures and stores images
before transforming them into information that can be further
acted upon.
This requires expertise across a range of fields, including sensor
technology, image and signal processing, computer graphics,
computer architecture, algorithms and machine learning.
What are the fundamental computer vision
techniques?
Image classifcation gives a computer the ability to interpret
the input from an image sensor and categorise what it ‘sees’.
Object Detection detecting instances of a certain class (such
as vehicles, humans, buildings) in images or videos.
Object Tracking detecting and recognizing a defined item
in each frame of a video to distinguish it from other objects in
the scene.
3D Image Reconstruction the process of capturing the shape
and appearance of real objects.
Semantic Image Segmentation when specific regions of an
image are labelled according to what the object is.
What’s the difference between image
processing, computer vision and machine
learning?
Each of these fields is based on the input of an image. They
process the pixels and give us an altered output in return. While
their names imply their goals and methodologies, these fields
depend substantially on one another.
Relationship between AI,
machine learning and deep learning
Artificial intelligence
Machine learning
Deep
learning
Artifcial intelligence:
AI is the theory and
development of computer
systems to perform tasks
normally requiring human
intelligence.
Machine learning: is an
application of AI based
around the idea of giving
machines access to data
and letting them learn for
themselves.
Deep learning: is a special
type of machine learning
algorithm, multiple layers of
neural networks that mimic
the connectivity of the
human brain in processing
data and creating patterns
for use in decision making.
As a minimum an AI system must be able to reproduce aspects
of human intelligence
Image processing takes an image as an input and provides a
processed image as an output. The purpose of the processing
is usually to improve the quality of the image. Typical methods
used are filtering, noise removal, sharpening and edge detection.
Computer vision broadens the purpose of image processing
to include quantitative and qualitative information from visual
data. Similar to the process of human visual reasoning, computer
vision can distinguish between objects, classify them and sort
them according to their attributes. Computer vision, like image
processing, takes an image as an input. However, it returns an
output with additional information interpreted from the image
such as size, colour, number, location or orientation.
This can be extended beyond the extraction of meaningful
information from a single image to multiple images or video,
for example, to count the number of cars passing by a point on
the street as they are recorded by a video camera. Temporal
information therefore plays a role in computer vision, much as
it does with our own understanding of the world.
Machine learning is the application of intelligence that
provides the computer system with the ability to automatically
learn and improve from experience without having to be
programmed. In computer vision terms, this means ‘training’ a
system. Algorithms and statistical models are used to perform
image analysis using patterns and inference trained on data sets
of many thousands of images for automatic learning, rather
than using explicit instructions as image processing would.
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