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AI GRPOUP 4 PRESENTATION.pptx
- 1. ARTIFICIAL INTELIGENCE (A.I) COMPUTER VISION(C.V) Presented by: Group 4
- 2. COMPUTER VISION Computer visionis a field of science that studies how computers can improve their understanding of images and video. It draws upon the engineering and scientific fields to develop technologies that can improve machines perception of objects as humans.
- 3. DEFINITION (CON’T) Computer visionis an AI field that enables systems to collect and interpret visual input for purposes such as processing and classifying images.
- 4. BRIEF HISTORY OFAI COMPUTER VISION • Computer vision has been around for many years. Its commercial use was first tested in the 1950s to distinguish between handwritten text and typed words. • Scientists and engineers have been working on developing ways to make machines that can see and interpret visual data for over 60 years.
- 5. C.V HISTORY (CON’T) •In 1974, optical character recognition and intelligent character recognition were introduced. These two technologies could recognize text printed on any font or typeface. • In 2000, the focus of study in object recognition was shifted to face recognition. By 2001, the first real-world applications of face recognition emerged. In 2010, the Images data set was made available.
- 6. HOW COMPUTER VISIONWORKS • One way to teach a computer how to recognize patterns is to feed it thousands of images, if not millions, of them. This method allows the computer to study the various elements that relate to those patterns.
- 7. APPLICATION OF COMPUTERVISION •Computer vision is one of the areas in Machine Learning where core concepts are already being integrated into major products that we use every day.
- 8. COMPUTER VISION INSELF DRIVING CARS • Computer vision technology allows self-driving cars to see clearly in their surroundings. It uses cameras that are positioned around the vehicle to capture images of roads, read traffic signs, detect other cars, objects and pedestrians.
- 9. FACIAL RECOGNISION •Computer visionalso plays an important role in facial recognition applications, the technology that enables computers to match images of people’s faces to their identities already stored in a database.
- 10. COMPUTER VISION INHEALTH SECTOR •Computer vision has also been an important part of advances in health-tech. Computer vision algorithms can help automate tasks such as detecting cancerous moles in skin images or finding symptoms in x-ray and MRI scans.
- 11. EXAMPLES OF COMPUTERVISION Image classification: sees an image and can classify it (a dog, an apple, a person’s face). More precisely, it is able to accurately predict that a given image belongs to a certain class. For example, a social media company might want to use it to automatically identify and segregate objectionable images uploaded by users.
- 12. Object detection canuse image classification to identify a certain class of image and then detect and tabulate their appearance in an image or video. Examples include detecting damages on an assembly line or identifying machinery that requires maintenance.
- 13. •Object tracking followsor tracks an object once it is detected. This task is often executed with images captured in sequence or real-time video feeds. Autonomous vehicles, for example, need to not only classify and detect objects such as pedestrians, other cars and road infrastructure, they need to track them in motion to avoid collisions and obey traffic laws.
- 14. Content-based image retrievaluses computer vision to browse, search and retrieve images from large data stores, based on the content of the images rather than metadata tags associated with them. This task can incorporate automatic image annotation that replaces manual image tagging. These tasks can be used for digital assets management systems and can increase the accuracy of search and retrieval.
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