Computer Vision: Image and Video Recognition

Fayrix Machine Learning solution processes large arrays of visual data and automates image and video recognition.

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Advantages of computer vision

1
Processing large amounts of data which a human can't even physically cover.
2
Continuous operations. A computer does not need breaks to eat and sleep, while processing visual data compared to a human.
3
High reaction speed. Machine is always working real-time and its only purpose is to do its job.
4
Flexible methodologies. The solution is customized for particular business needs.
5
Business process automation
6
No human mistakes.
7
Releasing human resource from machine work, payroll costs descrease.
8
A computer can take into account many more details compared to a human being.

Text & Image recognition

One of the key aspects in computer vision discipline is image recognition.

The main challenge in image recognition is to match particular visual data with some predefined classes. Such technology solutions are essential for various business areas ranging from processing simple digital images to automated interpretation of medical and military devices.

Agriculture
Crop production quality control
Augmented realty
Defining object location based on sensor data
Autonomous vehicles
Spacial orientation, road signs and signals recognition
Biometry
Person identification
Character recognition
Characters on bills and bank card recognition
Forensic medical examination
Human biometrical footprint identification
Inspection of production quality
Production quality assurance
Facial recognition
Person identification
Gesture analysis
Gesture-powered computer manipulations
Geological analysis
Creating 3D relief maps
Medical images recognition
Detection pathologies and the roots of deseases
Environmental pollution monitoring
Automated detection of abnormal pollution levels
Robotics
Robots spacial orientation
Security and surveillance
Detecting intruders and criminals

How does computer
VISION WORK?

Computer vision project implementation illustrated by solving a problem of hand gesture recognition

Extracting image

Hand/face/object (does the image contain a hand?)

Image preparation

Skin color detection and segmentation (can be adapted to any human phenotype)

Identification of low-level image properties

Collecting picture characteristics

Extracting only meaningful properties of all collected ones

Classfication

Using neural network to recognise the gesture

Exit class

A typical computer vision solution development and integration