Getting started with Computer Vision

A guide to the knowledge and application of visual systems

A guide to the knowledge and application of visual systems


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<strong>Getting</strong><br />

<strong>started</strong> <strong>with</strong><br />

<strong>Computer</strong><br />

<strong>Vision</strong><br />

A guide to the knowledge and application<br />

of visual systems<br />


The goal of computer vision is to extract<br />

meaning from pixels and perform visual<br />

tasks similar to the human visual system. Interest in<br />

how machines ‘see’ and how computer vision<br />

can be used to build products for consumers and<br />

businesses is growing rapidly.<br />

ication<br />

Identif<br />

Recognition<br />

Capabilities of<br />

computer vision<br />

Tracking<br />

Real-time analysis<br />

If you are reading the printed version of this brochure, you can download a hyperlinked pdf at censis.org.uk/brochures<br />


Contents<br />

1 An introduction to computer vision 3<br />

a. FAQs 3<br />

b. A brief history of computer vision 4<br />

c. The evolution of computer vision 5<br />

d. Deep learning breakthrough 5<br />

2 Application examples 7<br />

a. Smart homes 7<br />

b. Smart cities 7<br />

c. Industry 7<br />

d. Healthcare 8<br />

e. Agriculture 8<br />

f. Security 8<br />

g. Autonomous vehicles 8<br />

h. AR/VR & immersive technologies 8<br />

3 How is computer vision used in business? 9<br />

a. Benefits for business, industry and society 9<br />

b. Technical challenges 9<br />

c. Privacy 9<br />

4 How to set up a computer vision system 10<br />

a. Basic components 10<br />

b. Hardware platforms 10<br />

c. Software tools 10<br />

d. Digital imaging system stack 10<br />

5 How to process and interpret images 11<br />

a. Image as an array 11<br />

b. Image processing 12<br />

c. Machine learning 12<br />

d. Deep learning 13<br />

e. Choosing machine learning or deep learning 13<br />

f. Image processing libraries 13<br />

g. Machine learning frameworks 14<br />

6 Embedded vision 15<br />

a. Embedded vision platforms 15<br />

b. Camera modules 16<br />

c. Interfaces 16<br />

7 <strong>Computer</strong> vision & IoT 17<br />

a. Cloud vs edge processing 17<br />

b. Cloud platform and machine learning vendors 18<br />

c. Machine vision and IoT 18<br />

8 Implementing computer vision 19<br />

a. Your first prototype 19<br />

b. How CENSIS can help 20<br />

c. IoT2Go <strong>Vision</strong> kit 20<br />

9 Incubators and learning resources 21<br />

10 The computer vision community in Scotland 22<br />

a. Companies in Scotland 22<br />

b. Research in Scotland 23<br />

Glossary 24<br />


1 An introduction to<br />

computer vision<br />

a. FAQs<br />

What is computer vision?<br />

Of the five human senses, vision is the one that provides most<br />

of the data we receive and is considered our dominant sense.<br />

It provides us <strong>with</strong> a detailed description of the surrounding<br />

world which is constantly changing. Although vision involves<br />

a huge amount of information and complex processing, the<br />

human visual system can interpret this information easily.<br />

The ability to see, process and then act on visual input is<br />

something that most humans take for granted.<br />

<strong>Computer</strong> vision engineering is the practice of using<br />

technology and machines to replicate, and even improve upon,<br />

human vision. The technology captures and stores images<br />

before transforming them into information that can be further<br />

acted upon.<br />

This requires expertise across a range of fields, including sensor<br />

technology, image and signal processing, computer graphics,<br />

computer architecture, algorithms and machine learning.<br />

What are the fundamental computer vision<br />

techniques?<br />

Image classifcation gives a computer the ability to interpret<br />

the input from an image sensor and categorise what it ‘sees’.<br />

Object Detection detecting instances of a certain class (such<br />

as vehicles, humans, buildings) in images or videos.<br />

Object Tracking detecting and recognizing a defined item<br />

in each frame of a video to distinguish it from other objects in<br />

the scene.<br />

3D Image Reconstruction the process of capturing the shape<br />

and appearance of real objects.<br />

Semantic Image Segmentation when specific regions of an<br />

image are labelled according to what the object is.<br />

What’s the difference between image<br />

processing, computer vision and machine<br />

learning?<br />

Each of these fields is based on the input of an image. They<br />

process the pixels and give us an altered output in return. While<br />

their names imply their goals and methodologies, these fields<br />

depend substantially on one another.<br />

Relationship between AI,<br />

machine learning and deep learning<br />

Artificial intelligence<br />

Machine learning<br />

Deep<br />

learning<br />

Artifcial intelligence:<br />

AI is the theory and<br />

development of computer<br />

systems to perform tasks<br />

normally requiring human<br />

intelligence.<br />

Machine learning: is an<br />

application of AI based<br />

around the idea of giving<br />

machines access to data<br />

and letting them learn for<br />

themselves.<br />

Deep learning: is a special<br />

type of machine learning<br />

algorithm, multiple layers of<br />

neural networks that mimic<br />

the connectivity of the<br />

human brain in processing<br />

data and creating patterns<br />

for use in decision making.<br />

As a minimum an AI system must be able to reproduce aspects<br />

of human intelligence<br />

Image processing takes an image as an input and provides a<br />

processed image as an output. The purpose of the processing<br />

is usually to improve the quality of the image. Typical methods<br />

used are filtering, noise removal, sharpening and edge detection.<br />

<strong>Computer</strong> vision broadens the purpose of image processing<br />

to include quantitative and qualitative information from visual<br />

data. Similar to the process of human visual reasoning, computer<br />

vision can distinguish between objects, classify them and sort<br />

them according to their attributes. <strong>Computer</strong> vision, like image<br />

processing, takes an image as an input. However, it returns an<br />

output <strong>with</strong> additional information interpreted from the image<br />

such as size, colour, number, location or orientation.<br />

This can be extended beyond the extraction of meaningful<br />

information from a single image to multiple images or video,<br />

for example, to count the number of cars passing by a point on<br />

the street as they are recorded by a video camera. Temporal<br />

information therefore plays a role in computer vision, much as<br />

it does <strong>with</strong> our own understanding of the world.<br />

Machine learning is the application of intelligence that<br />

provides the computer system <strong>with</strong> the ability to automatically<br />

learn and improve from experience <strong>with</strong>out having to be<br />

programmed. In computer vision terms, this means ‘training’ a<br />

system. Algorithms and statistical models are used to perform<br />

image analysis using patterns and inference trained on data sets<br />

of many thousands of images for automatic learning, rather<br />

than using explicit instructions as image processing would.<br />


What is artificial intelligence (AI)?<br />

Artificial intelligence is intelligence demonstrated by machines,<br />

where any device can perceive its environment and mimic<br />

human functions such as ‘learning’ and ‘problem solving’.<br />

Artificial intelligence, or AI, is the broad concept of machines<br />

being able to carry out tasks in a way that is considered ‘smart’.<br />

What are neural networks?<br />

Neural networks are a means of machine learning, where<br />

a computer learns to perform a task by analysing training<br />

examples or datasets. Usually, the dataset examples have been<br />

manually labelled in advance. An object recognition system<br />

might be fed thousands of labelled images of cars, houses,<br />

cups and would find visual patterns in the images that correlate<br />

consistently <strong>with</strong> the particular label.<br />

What is deep learning?<br />

Deep learning is the use of neural network methods to perform<br />

image analysis, moving away from statistical methods to neural<br />

network algorithms which are developed to mimic the neurons<br />

of the human brain.<br />

What applications can computer vision<br />

be used for?<br />

Applications of computer vision are many and varied.<br />

Common applications you may be familiar <strong>with</strong> include<br />

augmented reality, facial recognition, gesture and handwriting<br />

recognition, machine vision, remote sensing, robotics,<br />

autonomous vehicles, people counting and iris recognition.<br />

What business sectors use computer vision?<br />

<strong>Computer</strong> vision has numerous applications such as<br />

remote sensing, healthcare (particularly around medical<br />

imaging such as MRI scans or ultrasound imaging), security,<br />

manufacturing, automotive, transport, robotics, sports,<br />

gaming and many others.<br />

The computer vision<br />

market is expected<br />

to reach close to $22 billion<br />

by 2026<br />

https://www.verifiedmarketresearch.com/product/globalcomputer-vision-market-size-and-forecast-to-2025/<br />

b. A brief history of<br />

computer vision<br />

<strong>Computer</strong> vision has a long history in commercial and<br />

government use where light wave sensors in various spectrum<br />

ranges have been deployed in many applications such as:<br />

• Remote sensing for environmental observation<br />

and management<br />

• High resolution cameras that collect intelligence over<br />

battlefields<br />

• Thermal imagers to detect people during police operations<br />

• X-ray sensors for airport security.<br />

The sensors can be stationary or attached to moving objects,<br />

such as satellites, drones and vehicles. When combined <strong>with</strong><br />

connectivity technologies such as Wi-Fi, Bluetooth or 3G/4G/5G,<br />

they create a new set of applications that were not possible before.<br />

<strong>Computer</strong> vision, coupled <strong>with</strong> connectivity, advanced data<br />

analytics and artificial intelligence, are catalysts for each other,<br />

giving rise to revolutionary leaps in IoT innovations<br />

and applications.<br />


c. The evolution of computer vision<br />

1960s<br />

<strong>Computer</strong> vision technology <strong>started</strong> in the early 1960s <strong>with</strong><br />

the aim of trying to mimic human vision systems and to ask<br />

computers to tell us what they see.<br />

<strong>Computer</strong>s ‘see’ the world differently from humans<br />

• They capture an image as an array of pixels<br />

• Borders between objects are discerned by measuring<br />

shades of colour<br />

• Spatial relations between objects can be estimated.<br />

3D models and representations of the environment from<br />

2D images began to be developed. Research continued by<br />

developing ways to analyse real world images which led to<br />

techniques such as edge detection and segmentation. These<br />

were the foundations for low-level scene understanding and<br />

steps towards automating the process of image analysis.<br />

1970s<br />

The 1970s saw the first commercial application of computer<br />

vision technology, which was an optical character recognition<br />

program. Combined <strong>with</strong> text-to-speech technology it provided<br />

the first print-to-speech reading machine for the blind.<br />

1980s<br />

In 1980 the precursor of modern convolutional neural<br />

networks was developed. As neural networks evolved<br />

throughout the 1980s, algorithms <strong>started</strong> to be programmed<br />

to solve individual challenges<br />

2000s<br />

Face detection in real-time was first developed in 2001, by<br />

Viola & Jones and was the first object detection framework to<br />

successfully perform in real time.<br />

Robot cars<br />

tested on<br />

roads by Google<br />

in 2010<br />

2010s<br />

• Hardware technology evolution<br />

Throughout the 2010s, single board computers <strong>with</strong><br />

increasingly powerful GPUs, FPGAs and mobile hardware<br />

platforms have been designed, built and adapted to accelerate<br />

machine learning based computer vision algorithms.<br />

Increased power and efficiency at lower costs have allowed<br />

breakthroughs in using machine learning for computer vision<br />

and deployment is increasing at an exponential rate.<br />

• Sensor technology developments<br />

Advancements are also happening rapidly in many areas<br />

beyond conventional camera sensors. For example, infrared<br />

sensors and lasers combine to sense depth and distance,<br />

which are one of the critical enablers of self-driving cars and<br />

3D mapping applications.<br />

• Data generation<br />

One of the driving factors behind the growth of computer<br />

vision is the amount of data generated which can be used to<br />

create datasets to train and make computer vision better.<br />

d. Deep learning breakthrough<br />

Although computer vision techniques <strong>started</strong> in the late 1950s<br />

and many of the machine learning algorithms were developed<br />

in the 1980s, computer vision has grown exponentially<br />

in the last decade due to the increased computational<br />

power offered by processing chips, cloud technologies and<br />

other advancements. Alongside the dedicated hardware<br />

developments, in recent years, the emergence of deep learning<br />

algorithms has reinvigorated computer vision. Throughout<br />

the 2010s, computer performance, accelerated by graphics<br />

processing units (GPUs), have grown powerful enough for us to<br />

realise the capabilities of neural network algorithms.<br />

<strong>Computer</strong> vision has<br />

grown exponentially<br />

in the last decade<br />


1950:<br />

computer vision<br />

emerges 1957:<br />

pixel invented, first digital<br />

image<br />

1966:<br />

MIT Artificial<br />

intelligence lab<br />

1969:<br />

CCD invented<br />

1990s:<br />

computer graphics<br />

& computer vision<br />

(image morphing,<br />

view interpolation,<br />

panoramic image<br />

stitching)<br />

2001:<br />

real-time face<br />

detection<br />

1970s:<br />

first commercial<br />

computer vision<br />

application (OCR)<br />

1975:<br />

first commercial digital<br />

camera<br />

1980s:<br />

mathematical and<br />

quantitative analysis<br />

developments<br />

2012<br />

AlexNet, deep neural<br />

network for image<br />

recognition<br />

1990s:<br />

projective 3D reconstructions,<br />

stereo imaging, statistical<br />

learning techniques for facial<br />

recognition<br />

2010s:<br />

GPUs/neural networks<br />


2 Application examples<br />

a.<br />

Smart<br />

homes<br />

<strong>Computer</strong><br />

vision-based user data<br />

will increasingly become<br />

a feature of the home.<br />

When systems can<br />

detect and recognise<br />

objects, they can deliver<br />

smart actions according<br />

to what they were<br />

programmed<br />

to do<br />

Facial recognition<br />

will be used to<br />

unlock the door, or<br />

to remain locked if<br />

an unfamiliar person<br />

approaches<br />

Indoor security<br />

cameras will<br />

send an alert to<br />

a smartphone if<br />

an elderly family<br />

member falls, or if a<br />

toddler is climbing<br />

up stairs<br />

Person detection<br />

can be used to adjust<br />

lighting and temperature<br />

to the number of people<br />

in any room to ensure a<br />

comfortable<br />

environment and save<br />

on electricity and a TV<br />

box that recognises<br />

individuals can turn on a<br />

tailored interface for<br />

entertainment<br />

b.<br />

Smart<br />

cities<br />

Smart cities employ<br />

a combination of<br />

low power sensors,<br />

cameras and<br />

machine learning<br />

software to monitor<br />

the efficient working<br />

of the city<br />

<strong>Computer</strong> vision<br />

and related<br />

technologies<br />

can play a significant<br />

role in managing<br />

smart cities as they<br />

serve as the ‘eyes’ of<br />

the city<br />

Smart city<br />

applications include<br />

monitoring of traffic<br />

and pedestrian<br />

flows using energy<br />

efficient, intelligent<br />

street lighting<br />

<strong>with</strong> ambient light<br />

sensors<br />

Smart parking<br />

systems could also<br />

direct motorists to<br />

a free parking spot<br />

c.<br />

Industry<br />

<strong>Computer</strong> vision can be<br />

combined <strong>with</strong> methods<br />

and technologies to<br />

provide applications<br />

in industry. <strong>Computer</strong><br />

vision used in this field is<br />

often referred to as<br />

‘machine vision’<br />

Automated<br />

applications such as<br />

package inspection,<br />

barcode reading,<br />

3D inspection,<br />

track and trace are<br />

commonly used<br />

Machine vision<br />

combined <strong>with</strong><br />

robotics provides<br />

applications<br />

such as product<br />

and component<br />

assembly<br />

Predictive<br />

maintenance and<br />

defect reduction<br />

also typically use<br />

machine vision<br />

technology<br />

d.<br />

Healthcare<br />

<strong>Computer</strong> vision<br />

applications in<br />

healthcare have been<br />

developed to aid<br />

healthcare professionals<br />

<strong>with</strong> medical imaging<br />

diagnosis, surgery and<br />

health monitoring<br />

These can be used<br />

to detect if elderly<br />

people have fallen<br />

or require other<br />

forms of assistance<br />

Healthcare<br />

robotics can<br />

help <strong>with</strong> assisting<br />

nurses to clean<br />

hospitals<br />

Robots will need<br />

to be able to<br />

navigate the world<br />

around them<br />

through 3D computer<br />

vision<br />


The agriculture<br />

industry is<br />

increasingly using<br />

computer vision<br />

technology for<br />

applications<br />

This can help <strong>with</strong><br />

better productivity,<br />

crop monitoring,<br />

precision agriculture<br />

and locating weeds<br />

and pests<br />

The quality of food<br />

products can be<br />

assessed and graded<br />

into specific grades,<br />

while detecting<br />

defects<br />

Properties such as<br />

colour, shape, size,<br />

surface defects and<br />

contamination can<br />

also be estimated<br />

e.<br />

Agriculture<br />

Intelligent scene<br />

monitoring systems<br />

are playing an<br />

increasingly<br />

significant role in<br />

society<br />

Examples include<br />

Automatic Number<br />

Plate Recognition<br />

(ANPR), people and<br />

vehicle tracking,<br />

crowd analysis and<br />

zone detection for<br />

health & safety<br />

Cameras can be<br />

placed in offices,<br />

hospitals, banks,<br />

ports, car parks,<br />

stadiums, shopping<br />

centres, airports<br />

and more<br />

The challenge<br />

is to identify the<br />

scene and context,<br />

understanding what<br />

demands immediate<br />

attention, what is<br />

valuable and what<br />

can be ignored<br />

f.<br />

Security<br />

Self-driving vehicles<br />

can be made<br />

intelligent, self-reliant<br />

and reliable using<br />

computer vision<br />

technology<br />

<strong>Computer</strong> vision<br />

technology is<br />

being applied<br />

to autonomous<br />

vehicles to make it<br />

safe for passengers<br />

and pedestrians<br />

Self-driving vehicles<br />

must be able to<br />

capture visual data in<br />

real time to create 3D<br />

maps to understand<br />

the surroundings,<br />

while detecting and<br />

classifying objects<br />

in their path such<br />

as traffic lights and<br />

pedestrians<br />

High quality<br />

images and videos<br />

must be obtained<br />

in low light<br />

conditions as well<br />

as daylight, using<br />

LiDAR sensors and<br />

thermal cameras<br />

alongside visible<br />

camera sensors<br />

g.<br />

Autonomous<br />

vehicles<br />

<strong>Computer</strong> vision aids<br />

virtual reality <strong>with</strong><br />

vision capabilities like<br />

SLAM (Simultaneous<br />

localisation and<br />

mapping), user body<br />

tracking and gaze<br />

tracking<br />

<strong>Computer</strong> visionbased<br />

AR overlays<br />

imagery or audio<br />

onto existing realworld<br />

scenery<br />

AR/VR applications in<br />

e-commerce allow<br />

the user to visualise<br />

products <strong>with</strong>in their<br />

homes or virtually try<br />

on clothes to find the<br />

perfect fit<br />

AR/VR applications<br />

in the healthcare<br />

industry empower<br />

professionals to<br />

provide better<br />

diagnosis and make<br />

surgery safer<br />

h.<br />

AR/VR and<br />

immersive<br />

technologies<br />


3 How is computer vision<br />

used in business?<br />

Facial recognition<br />

Financial institutions<br />

Autonomous<br />

vehicles<br />

Medicine<br />

There are a huge<br />

range of applications<br />

where the ability to<br />

extract meaning from<br />

‘seeing’ visual data<br />

is useful<br />

Manufacturing<br />

Agriculture<br />

Digital marketing<br />

Handwriting extraction<br />

and analysis<br />

a. Benefts for business, industry and society<br />

<strong>Computer</strong> vision has the potential to revolutionise many<br />

everyday aspects of our lives. Having the ability to see and<br />

interpret a scene reliably and <strong>with</strong>out tiring, computer vision<br />

systems automate tasks <strong>with</strong>out needing human intervention.<br />

As a result, business users can have benefits such as<br />

• Faster and simpler processes – computer vision systems<br />

can carry out monotonous, repetitive tasks at a faster rate,<br />

making the entire process simpler<br />

• Accurate outcomes – computer vision systems can<br />

provide high quality image processing capabilities<br />

• Cost-reductions – errors and therefore faulty products<br />

or services can be minimised, so companies can save<br />

a lot of money that would otherwise be spent on<br />

fixing flawed processes and products<br />

b. Technical challenges<br />

There is a high level of technical understanding<br />

required to create software that collects and interprets<br />

visual data. To train a computer vision system<br />

powered by machine learning, companies need to have a<br />

team of professionals <strong>with</strong> technical expertise.<br />

Companies may also need to have a dedicated team<br />

for regular monitoring and evaluation of the vision<br />

system performance.<br />

c. Privacy<br />

Privacy is the biggest social threat that computer vision poses.<br />

The capabilities of computer vision – identification, recognition,<br />

tracking and real-time analysis – impact directly <strong>with</strong> individual<br />

rights for privacy. With computers learning from thousands and<br />

thousands of images and videos, computers are getting better<br />

at recognising individuals by their facial features, by identifying<br />

their behaviour or monitoring their habits. Everyone’s<br />

information is stored on a cloud.<br />

It is important to understand the potential negative effects of<br />

computer vision applications on society. This is crucial to ensure<br />

that computer vision applications make our lives more comfortable<br />

and efficient and not for purposes of constrain and control.<br />


4 How to set up a computer<br />

vision system<br />

Almost everyone has experienced computer vision and machine learning, often <strong>with</strong>out even knowing.<br />

This section explains how to set up a computer vision system.<br />

a. Basic components<br />

The components of a standard computer vision system are:<br />

• Digital camera/image sensor<br />

At the heart of any camera is the sensor. Modern sensors<br />

are solid-state electronic devices containing up to millions<br />

of discrete photodetector sites called pixels.<br />

• Lighting devices<br />

Many computer vision systems are optimised by illuminating<br />

the scene to be captured, and may require filters to enhance<br />

the sensor characteristics.<br />

• Lens<br />

To focus or enhance the scene<br />

• Frame grabber<br />

To capture individual frames<br />

• Image processing software<br />

To analyse the captured scene<br />

• Machine learning algorithms<br />

For pattern recognition<br />

b. Hardware platforms<br />

CPU<br />

GPU<br />

The central processing unit of a computer used to<br />

perform arithmetic computations. Most modern CPUs<br />

have 2 to 256 cores.<br />

The graphics processing unit of a computer used to<br />

process graphics. GPUs start at a couple of hundred cores<br />

FPGA<br />

and can run in to the thousands. The greater number of<br />

cores allows multiple calculations to be worked on at the<br />

same time which allows image processing to be<br />

performed efficiently.<br />

Field programmable gate arrays have parallel processing<br />

capabilities which make them suitable for image processing.<br />

c. Software tools<br />

There are many software tools <strong>with</strong> the necessary techniques to perform image and video processing tasks as well as machine<br />

learning algorithms.<br />

CPU<br />

GPU<br />

• OpenCV • Scilab • Octave • R • Matlab • Tensorflow • PyTorch • Keras • Caffe<br />

d. Digital imaging system stack<br />

Software<br />

processing<br />

6<br />

5<br />

Visualisation and reproduction<br />

Image post-processor<br />

Viewing image in visual format<br />

Image data optimisation<br />

Presentation<br />

4<br />

Image storage<br />

Formatting and storing image data<br />

Numeric<br />

presentation<br />

Hardware<br />

processing<br />

3<br />

2<br />

Digital signal processor<br />

Sensor<br />

Manipulation of digital signal<br />

Converting light to electrical signal<br />

1<br />

Optics<br />

Gathering Light<br />

Light<br />


5 How to process and<br />

interpret images<br />

a. Image as an array<br />

A digital image is an array of pixels where each pixel is a<br />

combination of numerical values representing the colours<br />

and intensities at a particular point on the image.<br />

A pixel, or picture element, is the smallest visual element of an<br />

image and typically contains three component intensities, or<br />

channels, such as red, green and blue. Colour digital images<br />

are created by combining the channels to reproduce the broad<br />

range of colours seen by the human eye.<br />

A grayscale image refers to the number of different shades, or<br />

depth, of a particular colour. A grayscale image can be created<br />

from any single channel or colour of the image.<br />

The image resolution gives the number of pixels and the aspect<br />

ratio gives the width:height pixel ratio.<br />

The channel contains the number of samples per point, which for<br />

grayscale images is a single sample per pixel, whereas for colour<br />

images is three samples per pixel (red, green, blue).<br />

Pixel<br />

Smallest visual element<br />

Digital Image<br />

A multidimensional array<br />

of numbers<br />

Aspect Ratio<br />

Width:Height<br />

Resolution<br />

Width x Height<br />

Channel: No. of samples per point<br />

Single plane: Grayscale / B&W images<br />

10<br />

9<br />

15<br />

32<br />

10<br />

65<br />

90<br />

32<br />

85<br />

23<br />

65<br />

54<br />

16<br />

70<br />

96<br />

99<br />

90<br />

43<br />

60<br />

85<br />

87<br />

85<br />

65<br />

32<br />

28<br />

56<br />

67<br />

70<br />

96<br />

92<br />

90<br />

43<br />

99<br />

85<br />

87<br />

65<br />

43<br />

56<br />

67<br />

96<br />

92<br />

43<br />

99<br />

87<br />

78<br />

67<br />

92<br />

99<br />

Three Planes: Colour images<br />

©maxEmbedded.com2012<br />


. Image processing<br />

The main purpose of image processing is to improve the<br />

quality of the image by sharpening and restoration; extract<br />

the features of an image to help discriminate objects and/or<br />

classes of objects; classify objects, locate their position and<br />

get an overall understanding of the scene.<br />

Standard methods and algorithms include edge detection,<br />

corner detection, blobs, correlation and thresholding.<br />

These techniques are used to extract as many features from<br />

images of a specific class of object (e.g., bicycles, horses,<br />

etc.) and treat those features as a ‘definition’ of the object.<br />

These ‘definitions’ are then searched for in other images. If<br />

a significant number of features from one type of object are<br />

found in another image, the image can then be classified as<br />

containing that specific object (bicycle, horse, etc.).<br />

When the number of classes go up or the image clarity goes<br />

down, traditional computer vision algorithms find it harder<br />

to cope and machine learning techniques become more<br />

suitable.<br />

The main steps for image processing are:<br />

Image acquisition<br />

Captures the image <strong>with</strong> a sensor or camera and convert it into<br />

a manageable format<br />

Image enhancement<br />

The input image quality is enhanced and important<br />

details extracted<br />

Image restoration<br />

Any corruption such as blur, noise, or camera misfocus<br />

is removed to get a cleaner image<br />

Colour image processing<br />

The coloured images are processed <strong>with</strong> RGB or other<br />

colour space methods<br />

Image compression and decompression<br />

To allow for changes in image resolution and size, to<br />

reduce or restore images depending on the requirement<br />

Morphological processing<br />

Defines the object structure and shape in the image<br />

Feature extraction<br />

For a particular object, the specific features are identified<br />

in the image and techniques like object detection are used<br />

Representation and description<br />

Store and visualise the processed data <strong>with</strong> a suitable file<br />

format and output<br />

c. Machine learning<br />

Machine learning uses patterns in large data to perform tasks<br />

<strong>with</strong>out being explicitly told what to do.<br />

There are mainly three different ways machines can learn:<br />

Supervised learning algorithms<br />

These are designed to learn by example. When training a<br />

supervised learning algorithm, the training data will consist<br />

of inputs paired <strong>with</strong> the correct outputs. During training, the<br />

algorithm will search for patterns in the data that correlate<br />

<strong>with</strong> the desired outputs. After training, a supervised learning<br />

algorithm will take in new unseen inputs and will determine<br />

which label the new inputs will be classified as, based on prior<br />

training data. The objective of a supervised learning model is<br />

to predict the correct label for newly presented input data.<br />

Unsupervised learning<br />

Give the machine unlabelled data and it will find patterns in<br />

the data. The algorithm will pick up the difference between<br />

objects as they find logical patterns.<br />

Reinforcement learning<br />

The algorithm is trained in a reward and punishment<br />

mechanism. The agent is rewarded for correct moves and<br />

punished for the wrong ones. In doing so, the algorithm tries<br />

to minimize wrong moves and maximize the right ones.<br />

Unsupervised<br />

Supervised<br />

Machine<br />

learning<br />

• No labels<br />

• No feedback<br />

• ‘Find hidden structure’<br />

• Labelled data<br />

• Direct feedback<br />

• Predict outcome/future<br />

Reinforcement<br />

• Decision process<br />

• Reward system<br />

• Learn series of actions<br />


d. Deep learning<br />

Deep learning is a special subset of machine learning and has<br />

revolutionised computer vision. Many problems that once<br />

seemed improbable to be solved are solved to the point<br />

where machines are getting better results than humans.<br />

Deep learning introduced the concept of end-to-end learning<br />

where the machine is just given a dataset of images which<br />

have been annotated <strong>with</strong> what class of object is present in<br />

each image.<br />

e. Choosing machine learning or deep learning<br />

Classic computer vision analysis excels at measurements,<br />

finding defects or matching patterns. It is the ideal solution<br />

for repeatable dimension measurements of an object in a<br />

controlled environment, such as examining machined parts or<br />

printed circuit boards. Traditional techniques work very well in<br />

constrained environments. However they don’t handle novel<br />

situations very well.<br />

In comparison, machine learning is trainable, and as it gains<br />

access to a wider data set, it’s able to locate, identify and<br />

segment a wider number of objects or faults <strong>with</strong> more<br />

variable appearance or perspective, such as identifying and<br />

counting foods such as broccoli on a conveyor belt.<br />

Breakthroughs in the field of artificial neural networks in recent<br />

years have driven companies across industries to implement<br />

deep learning solutions, from chatbots in customer service<br />

to image and object recognition in retail, and many more.<br />

Deep learning has unlocked a myriad of sophisticated new AI<br />

applications.<br />

The performance of deep learning algorithms <strong>with</strong> complex<br />

tasks have made it particularly appealing as a solution.<br />

However, it is not always the best approach to computer<br />

vision and machine learning related problems. Deep learning<br />

methods are ideal for replacing human eyes for object<br />

classification problems, or to emulate expertise by interpreting<br />

images such as medical x-rays. Deep learning algorithms take<br />

a long time to train however, requiring a lot of code compared<br />

to relatively few lines of classic computer vision code.<br />

While each use case is unique and will depend on business<br />

objectives, AI maturity, timescale, data and resources, among<br />

other things, are general considerations to take into account<br />

before deciding whether or not to use deep learning to solve<br />

a given problem.<br />

f. Image processing libraries<br />

OpenCV<br />

The Open Source <strong>Computer</strong> <strong>Vision</strong> Library (OpenCV)<br />

is one of the most popular computer vision libraries that<br />

provides many algorithms and functions. It includes<br />

modules such as image processing, object detection and<br />

deep learning to name just a few.<br />

The library is written in C++ and supports C++, Java, Python<br />

and MATLAB interfaces.<br />

Scilab An open source software similar to MATLAB,<br />

<strong>with</strong> a computer vision and image processing module.<br />

Octave An open source software also similar to MATLAB,<br />

<strong>with</strong> a computer vision and image processing module.<br />

R An open source data analysis library <strong>with</strong> packages for<br />

image processing.<br />


g. Machine learning frameworks<br />

<strong>Computer</strong>s learn by viewing thousands of labelled images to understand the traits of what’s being visualised. They learn to<br />

associate characteristics they detect in the images <strong>with</strong> each label. This method of machine learning means that the same<br />

principle can be applied to diverse areas such as:<br />

• Evaluating the quality of packages in a factory<br />

• Diagnosing organ function from an MRI scan<br />

• Identifying trends in the stock market<br />

• Locating traffic signs and many more.<br />

There are a great variety of free open-source tools to help to get <strong>started</strong> <strong>with</strong> machine learning tasks.<br />

TensorFlow<br />

An open-source platform for machine learning created by<br />

Google. It has a comprehensive, flexible ecosystem of tools,<br />

libraries and community resources that lets researchers push the<br />

state-of-the-art in ML and developers easily build and deploy<br />

ML-powered applications. Tensorflow works best for image<br />

classification, image recognition, image segmentation, image<br />

to image translation. Tensorflow includes a set of libraries for<br />

creating and training custom deep learning models and neural<br />

networks. Tensorflow supports several popular programming<br />

languages, including C++, Python, and Java.<br />

PyTorch<br />

A Python based scientific computing package that uses the<br />

power of GPUs, currently one of the preferred deep learning<br />

research platforms built to provide maximum flexibility and<br />

speed.<br />

Keras<br />

Keras is an open-source Python library for creating deep<br />

learning models. It’s a great solution for those who only begin<br />

to use machine learning algorithms in their projects as it<br />

simplifies the creation of a deep learning model from scratch.<br />

Accord.NET<br />

Accord also includes a .Net machine learning framework<br />

combined <strong>with</strong> audio and image processing libraries written in<br />

C#. It is a good framework for both creative and general tasks.<br />

The image processing algorithms can be used for tasks such<br />

as face recognition, image joining, or tracking moving objects.<br />

Accord also include libraries that provide a more traditional<br />

range of machine learning functions starting from neural<br />

networks and ending <strong>with</strong> decision tree systems.<br />

Caffe<br />

Convolutional Architecture for Fast Feature Embedding (Caffe)<br />

is an open-source framework that can be used for creating and<br />

training popular types of deep learning architectures. Caffe is<br />

good for tasks such as image classification, segmentation and<br />

recognition. Caffe is written in C++ but it also has a Python<br />

interface.<br />

Google Colab<br />

Google Colaboratory, or simply Colab, is one of the top<br />

image processing services. While it’s a cloud service rather<br />

than a framework, it can still be used for building custom<br />

deep learning applications from scratch. Tasks such as image<br />

classification, segmentation and object detection can be<br />

performed. Google Colab offers free usage of both CPU- and<br />

GPU-based acceleration.<br />

NVIDIA DeepStream SDK<br />

To build and deploy AI-powered Intelligent Video Analytics<br />

apps and services. DeepStream offers a multi-platform<br />

scalable framework <strong>with</strong> TLS security to deploy on the edge<br />

and connect to any cloud. https://developer.nvidia.com/<br />

deepstream-sdk<br />

<strong>Computer</strong> vision developments are evolving very quickly <strong>with</strong> new frameworks being written, new networks and datasets being<br />

released and new chips being designed at increasing pace. There are many more frameworks and platforms available, both opensource<br />

or subscription. Picking the right framework for the machine learning application is an important step of project development.<br />


6 Embedded vision<br />

<strong>Computer</strong> vision systems have traditionally relied on a PC due to the processing power required to perform image analysis.<br />

A frame grabber or interface card sends image data from the camera to the computer which then analyses the images and<br />

relays information to another part of the system. These systems can be bulky or complex, however they offer good<br />

performance specifications.<br />

The industry now is using more and more single-board<br />

computers and camera electronics have also become smaller.<br />

New camera and computer systems for applications<br />

are now<br />

• Highly compact<br />

• Powerful<br />

• Low-cost<br />

• Large memory<br />

• Energy-efficient<br />

Driven by the need to integrate small cameras into mobile<br />

phones, embedded vision technology advances are now at<br />

the stage where it is practical to incorporate computer vision<br />

capabilities almost anywhere.<br />

Embedded vision systems are usually easier to use and<br />

integrate than PC-based systems. They often only include a<br />

small camera <strong>with</strong>out a housing connected to a processing<br />

board (embedded board/module) via a connector. The<br />

components are combined into one device and images<br />

sent from the camera are processed directly on the system’s<br />

processing board.<br />

a. Embedded vision platforms<br />

There are many popular devices that are commonly used for running computer vision algorithms<br />

Provider Board CPU GPU RAM Price<br />

Raspberry Pi Zero / Zero W 1GHz, Single Core - 2,4 or 8GB $5 and $10<br />

NVIDIA Jetson Nano Quad-core ARM A57 128-core 4GB 64-bit $99<br />

Maxwell GPU<br />

Raspberry Pi RPi 4 Quad core Broadcom 1, 2 or 4GB $35 - $55<br />

Cortex-A72<br />

VideoCore VI<br />

Google Coral dev board NXP i.MX 8M SOC Integrated GC7000<br />

(quad Cortex-A53, Lite Graphics<br />

Cortex-M4F)<br />

Seeed Studio Rock Pi N10 Dual Cortex-A72, Mali T860MP4 4/6/8GB $99 - $169<br />

1.8GHz, quad<br />

Cortex-A53<br />

Cheapest: Raspberry Pi Zero / Zero W<br />

Best for beginners: Raspberry Pi 4<br />

Best flexibility: NVIDIA Jetson Nano Dev Kit<br />

Best for machine learning <strong>with</strong> Tensorflow: Google Coral Dev Board<br />


. Camera modules<br />

As image sensor components become smaller, cheaper and more efficient, the range of applications they can be<br />

applied to increases.<br />

• Image quality, <strong>with</strong> true colours, clear contrast and resolution as important factors<br />

• Easy operation and prototyping capabilities, often <strong>with</strong> a development kit and plug and play interfaces<br />

• Easy system integration <strong>with</strong> well-defined interfaces and software protocols<br />

c. Interfaces<br />

Choosing the right interface is crucial for any imaging application. Understanding the applications requirements in terms of<br />

resolution, frame rates, transfer speed requirements, among others, will determine the best interface to use. A comparison of<br />

popular digital camera interfaces is shown in the table below.<br />

Comparison of popular digital camera interfaces<br />

Interface FireWire 1394.b Camera Link® USB 2.0 USB 3.0 GigE<br />

Data Transfer Rate 800 Mb/s 3.6 Gb/s 480 Mb/s 5Gb/s 1000 Mb/s<br />

Max Cable Length 100m 10m 5m 3m 100m<br />

No. devices Up to 63 1 Up to 127 Up to 127 Unlimited<br />

Connector 9pin-9pin 26pin USB USB Rj45/Cat53 or 6<br />

Capture board Optional Required Optional Optional Not required<br />

Power Optional Required Optional Optional Required<br />

Source: Edmund Optics: https://www.edmundoptics.co.uk/knowledge-center/application-notes/imaging/camera-types-and-interfaces-for-machine-vision-applications/<br />


7 <strong>Computer</strong> vision & IoT<br />

Connecting computer vision systems to the Internet of Things<br />

(IoT) creates a powerful network capability. Being able to<br />

identify objects from cameras allows the local node to be<br />

more intelligent and have greater autonomy, thus reducing<br />

the processing load on central servers and allowing a more<br />

distributed control architecture.<br />

Devices such as smartphones and IoT sensors are generating<br />

data that needs to be analysed in real time using machine<br />

learning or used to train deep learning models. However,<br />

machine learning inference and training require substantial<br />

computational and memory resources to run quickly.<br />

Edge computing, where computer nodes are placed close to<br />

end devices, is a viable way to meet the high computation and<br />

low-latency requirements of deep learning on edge devices<br />

and also provides additional benefits in terms of privacy,<br />

bandwidth efficiency and scalability.<br />

a. Cloud vs edge processing<br />

<strong>Computer</strong> vision tasks typically require fast processing capabilities – particularly for real-time image and scene understanding.<br />

<strong>Vision</strong> processing in the cloud<br />

To use cloud resources, data must be moved from the data source location on the network edge (i.e. camera modules,<br />

smartphones) to a centralised location on the cloud using a remote server or data centre. Moving data from the source to the<br />

cloud can introduce several challenges<br />

Latency<br />

There is a time lag between the collection and processing of data in the cloud, which is unnoticeable in many use cases.<br />

However, in time-sensitive applications, this time lag, which may only be milliseconds, becomes essential. Real-time inference<br />

is critical to many applications such as autonomous vehicles or voice-based assistance solutions. Sending data to the cloud for<br />

inference or training may incur delays from the network.<br />

Scalability<br />

Sending data from the sources to the cloud consumes significant bandwidth, which in turn increases data processing and<br />

transfer times, introducing scalability issues, as network access to the cloud can become a bottleneck as the number of<br />

connected devices increases.<br />

Privacy<br />

Sending data to the cloud risks privacy concerns from users who own the data or whose behaviours are captured in the data.<br />

Users may be wary of uploading sensitive information to the cloud and how an application may use that data.<br />


<strong>Vision</strong> processing at the edge<br />

Cloud processing is not ideal for real time and mission-critical applications. Once sensors detect an anomaly in a high volume<br />

continuous manufacturing process, for example, the system must take corrective action immediately, otherwise the defect will<br />

propagate. The time from detection to correction must be in seconds.<br />

In the case of a self-driving car, the response time must be in milliseconds. For these applications, the round trip from device to<br />

gateway to the cloud and back takes too long. A different architecture is needed where the data collection and processing are<br />

closer to the devices (or edge).<br />

Edge processing moves the computer, storage and networking closer to the source of the data, significantly reducing travel time<br />

and latency. Embedded smart devices enable more sophisticated processing at the sensor level.<br />

Key to this has been the introduction of lower-cost, compact embedded boards <strong>with</strong> processing power required for real-time<br />

image analysis. Placing the processing at the edge of the network allows for real-time results, low power consumption, strong<br />

privacy and is a viable solution to meet the challenges introduced by cloud processing. Embedded smart devices are ideal for<br />

repeated and automated robotic processes, such as edge detection in a pick-and-place system.<br />

b. Cloud platform and machine learning vendors<br />

Cloud capabilities and resources for machine learning are increasingly significantly. Today, cloud computing providers increasingly<br />

offer GPU and FPGA co-processors to accelerate processing workloads, including:<br />

Amazon Rekognition: https://aws.amazon.com/rekognition/<br />

Amazon Sagemaker: https://aws.amazon.com/sagemaker/<br />

Google Cloud <strong>Vision</strong> API: https://cloud.google.com/vision<br />

IBM Watson Visual Recognition: https://www.ibm.com/uk-en/<br />

cloud/watson-visual-recognition<br />

Microsoft Azure <strong>Computer</strong> <strong>Vision</strong> API: https://azure.microsoft.<br />

com/en-gb/services/cognitive-services/computer-vision/<br />

c. Machine vision and IoT<br />

Machine vision systems, which is a general term for computer<br />

vision used for industrial applications, connected to the IoT can<br />

create a powerful network capability. Allowing the local node<br />

to be more intelligent and have greater autonomy, reducing<br />

the processing load on central servers, can provide efficient<br />

operations and open up a wide range of applications,<br />

offering valuable insights into the operation of industrial<br />

systems. This in turn is opening up new ways of monitoring<br />

equipment and connecting autonomous robotic systems<br />

to the IoT infrastructure.<br />


8 Implementing<br />

computer vision<br />

As the demand for intelligent vision solutions grows, tools must integrate computer vision, processing, analytics,<br />

machine learning and connectivity into applications to help translate visual data into meaningful insights.<br />

a. Your frst prototype<br />

To develop a computer vision prototype, there are many important considerations to be made regarding choices<br />

of hardware and software to suit the application requirements. A brief summary of the main areas are:<br />

Cameras<br />

• Image sensor performance<br />

• Camera features and characteristics<br />

• Data rate/transfer<br />

• Camera/PC interfaces<br />

Optics<br />

• Focal length<br />

• Field of view<br />

• Magnification<br />

• Image quality<br />

Illumination<br />

• How to enhance features of interest<br />

• Monochrome or colour image<br />

Processing<br />

• Suitable development environment<br />

• Software development kits<br />

• Language flexibility<br />

• Single processor/multi-core support<br />

• Image processing tools<br />

• GPU utilisation<br />

• Cloud-based analytics<br />

• Memory requirements<br />

Output/Display<br />

• Processed image or video<br />

• Data analytics of object count or location<br />

• System information or monitoring<br />

• Angle of illumination<br />


. How CENSIS can help<br />

CENSIS launched the <strong>Vision</strong> Lab, a dedicated facility to help<br />

businesses adopt or deliver innovative computer vision or<br />

imaging solutions.<br />

CENSIS is uniquely positioned to help kickstart or accelerate<br />

businesses’ use of computer vision due to our connections<br />

<strong>with</strong> academia and industry and the funding we can bring to<br />

innovative technology projects. Our in-house technical and<br />

business development teams can also provide engineering<br />

support and consultancy.<br />

The hardware and software we have in the <strong>Vision</strong> Lab greatly<br />

develops our technical capabilities in computer vision and<br />

related fields and includes:<br />

• Development kits for image sensing, machine learning<br />

• 3D time-of-flight sensor for industrial machine<br />

vision applications<br />

• Machine vision cameras<br />

• Camera modules for embedded vision<br />

• MVTec Halcon, a comprehensive standard software<br />

package for machine vision industries, <strong>with</strong> capabilities<br />

in areas such as analysis, matching, measuring,<br />

identification, 3D vision and deep learning algorithms<br />

The CENSIS <strong>Vision</strong> Lab can help SMEs <strong>with</strong> product<br />

development or product enhancement around new<br />

technology, through funding, collaboration, consultancy<br />

and access to equipment and expertise.<br />

c. IoT2Go <strong>Vision</strong> Kit<br />

CENSIS has created IoT2Go, a series of plug and play IoT<br />

development kits for organisations to try out an IoT solution in<br />

their own premises. IoT2Go was developed as part of a Scottish<br />

Government programme to raise awareness of IoT technologies.<br />

The kits are quick and easy to set up and can be used by people<br />

<strong>with</strong> no technical or coding experience.<br />

The IoT2Go <strong>Vision</strong> kit can capture a real-time count of<br />

people and objects and has image classification and object<br />

detection demos.<br />


9 Incubators & learning<br />

resources<br />

Incubators<br />

• NVIDIA Inception<br />

https://www.nvidia.com/en-us/deep-learning-ai/startups/<br />

• NVIDIA Deep Learning Institute<br />

https://www.nvidia.com/en-us/deep-learning-ai/education/<br />

• Intel Edge AI Incubator<br />

https://www.siliconrepublic.com/start-ups/intel-edge-ai-incubator-ireland-computer-vision-start-up<br />

• Imagimob AI Early Access Program<br />

https://www.imagimob.com/imagimob-ai-early-access-program<br />

Learning Resources<br />

Links to useful online courses,videos and resources:<br />

• Introduction to <strong>Computer</strong> <strong>Vision</strong> on Udacity, free course<br />

https://www.udacity.com/course/introduction-to-computer-vision--ud810<br />

• Awesome <strong>Computer</strong> <strong>Vision</strong>, a list of resources on Github<br />

https://github.com/jbhuang0604/awesome-computer-vision<br />

• <strong>Computer</strong> <strong>Vision</strong> course by Subhransu Maji<br />

https://sites.google.com/view/cmpsci670/lecture-slides<br />

• Video Tutorial by Alberto Romay<br />

https://www.youtube.com/playlist?list=PL7v9EfkjLswLfjcI-qia-Z-e3ntl9l6vp<br />


10 The computer vision<br />

community in Scotland<br />

<strong>Computer</strong> vision research and development in Scotland has a long history<br />

going back to the 1960s <strong>with</strong> the Department of Machine Intelligence<br />

and Perception at the University of Edinburgh. Research robot Freddy,<br />

built in the 1960s, was one of the earliest systems to integrate perception<br />

and action. Freddy utilised a heavy robot arm fixed to an overhead gantry<br />

<strong>with</strong> adaptive grippers. A binocular vision system was also mounted to<br />

the gantry. Freddy was able to recognise a variety of objects and could be<br />

instructed to assemble simple artefacts, such as a toy car, from a random<br />

heap of components.<br />

http://www.aiai.ed.ac.uk/project/freddy/<br />

The Department of Machine Intelligence and Perception, later the<br />

Department of Artificial Intelligence, was the forerunner to both the Turing<br />

Institute in Glasgow, formed in 1983 and developed to combine research<br />

in AI <strong>with</strong> technology transfer to industry, and also to the current School of<br />

Informatics at Edinburgh which has leading research expertise in computer<br />

vision and machine learning.<br />

a. Companies in Scotland<br />

Advances in computer vision and machine learning are making it possible to build exciting new solutions for a range of industrial<br />

applications. Scotland has a strong base of computer vision companies - a selection is listed below.<br />

Company City Specialist Areas Link<br />

Odos Imaging Edinburgh 3D sensing and imaging solutions www.odos-imaging.com<br />

Peacock Stirling Robotics, automation, image processing, www.peacocktech.co.uk<br />

Technology<br />

machine vision<br />

Five AI Edinburgh Autonomous vehicles www.five.ai<br />

Machines Edinburgh Highly accurate train positioning system www.machines<strong>with</strong>vision.com<br />

<strong>with</strong> <strong>Vision</strong><br />

for continuously monitoring track condition<br />

Optos Dunfermline Retina imaging devices and development www.optos.com<br />

STMicroelectronics Edinburgh CMOS image sensor development, imaging systems, www.st.com<br />

Design Centre<br />

optical engineering, semiconductor solutions for<br />

autonomous driving and IoT<br />

NCTech Edinburgh High-resolution 360deg imagery and LiDAR www.nctechimaging.com<br />

Sense Edinburgh 3D perception systems for mobility, www.sensephotonics.com<br />

Photonics<br />

industrial and robotics autonomy<br />


. Research in Scotland<br />

There is an important and growing imaging and computer vision research community in universities throughout Scotland.<br />

A selection of research areas and universities are listed in the table below.<br />

University Research Group Areas of Research Link<br />

Heriot-Watt <strong>Vision</strong>lab Robotics, http://visionlab.eps.hw.ac.uk/<br />

University<br />

Automotive driver assistance,<br />

Surveillance,<br />

Human behaviour inference,<br />

Detection & tracking,<br />

Analysis of shape in 2D,<br />

Range and LiDAR analysis<br />

Signal & Image MRI, Ultrasound imaging, Novel imaging https://www.hw.ac.uk/uk/schools/<br />

Processing modalities, Imaging techniques in radio engineering-physical-sciences/institutes/<br />

Laboratory and optical astronomy sensors-signals-systems/siplab.htm<br />

Dundee <strong>Computer</strong> Healthcare and biomedical imaging, https://cvip.computing.dundee.ac.uk/<br />

University <strong>Vision</strong> & Image Visual perception of people and places.<br />

Processing<br />

The University Edinburgh Centre Virtual reality environments https://www.edinburgh-robotics.org/<br />

of Edinburgh for Robotics<br />

Machine Iconic vision in 2D http://www.ipab.inf.ed.ac.uk/mvu/<br />

<strong>Vision</strong> Unit<br />

Institute of Statistical machine learning, computer https://www.inf.ed.ac.uk/research/ipab/<br />

Perception, Action vison, mobile and humanoid robotics,<br />

and Behaviour motor control, graphics and visualisation<br />

University <strong>Computer</strong> <strong>Vision</strong> 3D vision systems https://www.gla.ac.uk/schools/computing/<br />

of Glasgow & Autonomous research/researchsections/ida-section/<br />

Systems<br />

computervisionandautonomoussystems/<br />

<strong>Computer</strong> Human body modelling in 3D http://www.dcs.gla.ac.uk/cvg/<br />

<strong>Vision</strong> & Graphics<br />

University <strong>Vision</strong> & Image Text and language processing, http://vip.cs.stir.ac.uk/<br />

of Stirling Processing Special visual perception<br />

Interest Group<br />

Glasgow School of Neural networks applied to condition https://www.gcu.ac.uk/cebe/<br />

Caledonian Computing, monitoring<br />

University Engineering &<br />

Built Environment<br />

Glasgow School of Real-time 3D visualisation, interaction http://www.gsa.ac.uk/research/research-<br />

School of Art Simulation and technologies centres/school-of-simulation-and-<br />

Visualisation<br />

visualisation/<br />

SINAPSE Scottish Imaging Medical Imaging – MRI, PET, SPECT, EEG, http://www.sinapse.ac.uk/<br />

Network<br />

deep learning in medical imaging<br />

A consortium of<br />

Aberdeen, Dundee,<br />

Edinburgh, Glasgow,<br />

St Andrews, Stirling,<br />

Strathclyde<br />


Glossary<br />

TERM<br />


AI<br />

Artificial Intelligence<br />

CMOS<br />

Complementary Metal-Oxide Semiconductor<br />

CPU<br />

Central Processing Unit<br />

FPGA<br />

Field Programmable Gate Array<br />

GPU<br />

Graphics Processing Unit<br />

IoT<br />

Internet of Things<br />

IoT2Go<br />

CENSIS IoT starter kit<br />

LiDAR<br />

Light Detection And Ranging<br />

SDK<br />

Software Development Kit<br />

SME<br />

Small and Medium Enterprises<br />

Join our<br />

community at<br />

censis.org.uk<br />


CENSIS is the centre of excellence for sensor and imaging<br />

systems (SIS) and Internet of Things (IoT) technologies.<br />

We help organisations of all sizes explore innovation<br />

and overcome technology barriers to achieve business<br />

transformation.<br />

As one of Scotland’s Innovation Centres, our focus is not<br />

only creating sustainable economic value in the Scottish<br />

economy, but also generating social benefit. Our industryexperienced<br />

engineering and project management teams<br />

work <strong>with</strong> companies or in collaborative teams <strong>with</strong> university<br />

research experts.<br />

We act as independent trusted advisers, allowing organisations<br />

to implement quality, efficiency and performance<br />

improvements and fast-track the development of new<br />

products and services for global markets.<br />

Contact details:<br />

CENSIS<br />

The Inovo Building<br />

121 George Street<br />

Glasgow<br />

G1 1RD<br />

Contact details:<br />

Contact CENSIS details:<br />

Tel: 0141 330 3876<br />

Email: info@censis.org.uk<br />

The Inovo Building<br />

CENSIS<br />

121 George Street<br />

The Glasgow Inovo Building<br />

G1 1RD<br />

121 George Street<br />

Glasgow<br />

Tel: 0141 330 3876<br />

Email: info@censis.org.uk<br />

G1 1RD<br />

Tel: 0141 330 3876<br />

Email: info @censis.org.uk<br />

Join the CENSIS mailing list at www.censis.org.uk<br />

Join the CENSIS mailing list at: www.censis.org.uk<br />

Join the CENSIS mailing list at www.censis.org.uk<br />

Follow us us on on us Twitter Twitter on Twitter<br />

@CENSIS121<br />

@CENSIS121<br />

Interest in how<br />

machines ‘see’<br />

and how computer<br />

vision can be used<br />

is growing.<br />


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