Google opens its Cloud Vision API to beta development
Last week, Google made its beta release of its Cloud Vision API open to the general public. Demonstrably, Google’s Cloud Vision API is the first known instance of selling commoditized artificial intelligence online.
Cloud Vision API provides powerful image analytics capabilities in an easy-to-use API. It enables application developers to build the next generation of applications that can observe, understand and manipulate content within online images. The service enables customers to detect a broad set of entities within an image from everyday objects to faces and product logos. With Cloud Vision, developers can access the API from images stored in Google Cloud Storage.
Cloud Vision API supports a broad set of scenarios from:
• Insights from images: Powered by the same technologies behind Google Photos, Cloud Vision API detects broad sets of objects in images — from flowers to popular landmarks.
• Inappropriate content detection: Powered by Google SafeSearch, Cloud Vision API moderates content from crowd sourced images by detecting different types of inappropriate content.
• Image sentiment analysis: Cloud Vision API can analyze emotional attributes of people in images, including: joy, sorrow and anger; along with detecting popular product logos.
• Text extraction: Optical Character Recognition (OCR) enables developers to detect text within images, along with automatic language identification across a broad set of languages.
Google released a video that demonstrates a Raspberry Pi robot that uses the API to demonstrate the system’s features.
Google also announced pricing for Cloud Vision API and added additional capabilities to identify the dominant color of an image. In terms of cost, Google will apply label detection on an image for as little as $2 per 1,000 images and optical character recognition (OCR) for $0.60 for 1,000 images. Pricing will be effective, starting March 1.
API | artificial intelligence | biometrics | Cloud | face detection | Google | image recognition | machine learning
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