Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras, videos and deep learning models, machines can accurately identify and classify objects – and then react to what they "see." Processing the image Deep learning models automate much of this process, but the models are often trained by first being fed thousands of labeled or pre-identified images. Understanding the image The final step is the interpretative step, where an object is identified or classified. 5. Computer vision is used across industries to enhance the consumer experience, reduce costs and increase security. Here are a few examples of computer vision in action today.
Computer vision Technology is rising, and increasingly gathering followers who want to adapt this technology to achieve new business heights. The rise of this technology can be attributed to the recent projections that have catapulted this technology to new zeniths. According to a market research, the computer vision market is valued at US$11.94 Billion and is likely to reach to US$17.38 Billion by 2023 growing at a CAGR of 7.80% from 2018 and 2023. The growth of the computer vision market is driven by the increasing adoption of computer vision into semi-autonomous and autonomous vehicles, and consumer drones which is dominated by the rising adoption of Industry 4.0. Recent advancements into computer vision technology with deep learning software, advanced cameras and image sensors, have expanded the scope for computer vision systems which can be deployed in a wide range of applications in different industries.
Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind. As the internet matured the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of high costs involved in human labeling efforts. Training data subset selection and active learning techniques have been proposed as possible solutions to these challenges respectively. A special class of subset selection functions naturally model notions of diversity, coverage and representation and they can be used to eliminate redundancy and thus lend themselves well for training data subset selection. They can also help improve the efficiency of active learning in further reducing human labeling efforts by selecting a subset of the examples obtained using the conventional uncertainty sampling based techniques. In this work we empirically demonstrate the effectiveness of two diversity models, namely the Facility-Location and Disparity-Min models for training-data subset selection and reducing labeling effort. We do this for a variety of computer vision tasks including Gender Recognition, Scene Recognition and Object Recognition. Our results show that subset selection done in the right way can add 2-3% in accuracy on existing baselines, particularly in the case of less training data. This allows the training of complex machine learning models (like Convolutional Neural Networks) with much less training data while incurring minimal performance loss.
The number of registered AI start-up firms increased by over than 50 per cent globally in 2016 – with funding almost doubling to $9.89bn during the same period. According to market analysts and research firm Venture Scanner, since March last year, the number of registered AI firms has risen globally from 957 to 1,535 across 71 different countries. Categories covered in its findings include businesses involved in; computer vision / image recognition, computer vision / image recognition, context aware computing, deep learning, machine learning, gesture control, natural language procession, personalised recommendation engines, smart robots, speech recognition, speech to speech translation, video automatic content recognition and virtual assistants. Venture Scanner claims of the 1,535 companies tracked, 731 of them have received funding, totalling $9.89 billion - up from $4.8 billion a year earlier. Venture Scanner has also revealed that the US currently leads the way for AI start-up companies, with more than 740 registered.