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Welcome to I-Cube: South Africa's leading provider of
License
Plate Recognition; Facial
Recognition & Image
Analysis
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A smart video camera understands what it sees
Imagine a smart video camera that automatically makes a note of license plate numbers on cars. It’s more than a camera. It can also analyze the content of images. How can video cameras best be taught to understand and react to what they see? How is the memory used in such a camera?
These are questions that Benny Thörnberg at Mid Sweden University answers in his dissertation on machine vision systems.
Benny Thörnberg has spent five years developing efficient design methods for smart camera electronics. The use of the memory in a smart camera is clearly the greatest challenge facing engineers. Further, the set of tools available to electronics engineers has by no means developed apace with the miniaturization and smarting up of electronics products.
"My dissertation describes a tool that helps designers of systems for machine vision. The tool facilitates programming and optimizes the use of memory in smart cameras,” says Benny Thörnberg.
Fields of application for the technology are numerous. Surveillance of traffic and homes, industrial automation, image enhancement, and night vision systems for cars are just a few examples.
The aim of the project is to develop a smart camera that can gage the size of woodchips. A chipper has to produce tiny chips of wood of roughly even size in order to enable the production of paper pulp and paper of high quality.
A major portion of the work has been carried out in a research project together with a few local companies and the Knowledge Foundation.
"Close collaboration with the local business community is a necessary condition for us to make sure we are researching relevant issues. Industry offers many interesting image analysis problems that are well suited to automation," says Benny Thörnberg.
The title of the dissertation is Memory Modeling and Synthesis for Real-Time Video Processing Systems.
I-Cube provides security and recognition systems in the following industry:
Farming
AUSTRALIAN researchers have developed a computerised system that uses Machine Vision Technology to help farmers manage domestic and wild animals on their properties.
The system is capable of distinguishing between sheep, goats, cattle, horses, pigs, kangaroos and emus and can be used with other species.
Machine vision is the ability of a computer to see. It uses cameras, analogue-to-digital conversion and digital signal processing. The data goes to a computer or robot controller.
The project involves the University of Queensland, the University of Southern Queensland the federal government and RPM Rural Products.
The government, through the departments of Agriculture and the Environment and Water Resources, has provided $600,000 from the Natural Heritage Trust for the development and trial phases of the project.
The system identifies animals and controls their movements via automated gates to access watering or feed points.
It is expected to boost farmers' productivity and efficiency in remote areas and control loss of feed and water to feral animals.
The MVT technology also aims to stop the artificial build-up of feral populations so that introduced and native animals do not become pests, researchers said.
Farmers on large properties in remote areas often found it difficult to monitor which species used the resources provided for livestock, said Neil Finch from UQ's School of Animal Studies.
The tool uses miniaturised, energy-efficient hardware. It can also read radio frequency identification ear tags to identify individual animals.
Machine vision could also enable automated drafting based on species or radio frequency identity eartags. Other likely uses are weight estimation and condition scoring of livestock, as well as monitoring and management of wildlife.
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