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Exploring images on social media using Amazon Rekognition and Amazon Athena Amazon Web Services

#artificialintelligence

If you're like most companies, you wish to better understand your customers and your brand image. You'd like to track the success of your marketing campaigns, and the topics of interest--or frustration--for your customers. Social media promises to be a rich source of this kind of information, and many companies are beginning to collect, aggregate, and analyze the information from platforms like Twitter. However, more and more social media conversations center around images and video; on one recent project, approximately 30% of all tweets collected included one or more images. These images contain relevant information that is not readily accessible without analysis.


Developing Sophisticated Serverless Applications with AI

#artificialintelligence

What to expect • Quick intro • 3 demo applications • Polly • Rekognition • MXnet • Wrap up. 4. 2017, Amazon Web Services, Inc. or its Affiliates. Event driven A B CEvent on B by A triggers C Invocation Lambda functions Action 6. 2017, Amazon Web Services, Inc. or its Affiliates. How Lambda works S3 event notifications DynamoDB Streams Kinesis events Cognito events SNS events Custom events CloudTrail events LambdaDynamoDB Kinesis S3 Any custom Invoked in response to events - Changes in data - Changes in state Redshift SNS Access any service, including your own Such as… Lambda functions CloudWatch events 7. 2017, Amazon Web Services, Inc. or its Affiliates. No servers to provision or manage Scales with usage Never pay for idle Availability and fault tolerance built in Serverless means… 9. 2017, Amazon Web Services, Inc. or its Affiliates. EVENT DRIVEN CONTINUOUS SCALING PAY BY USAGE Serverless means… 10. 2017, Amazon Web Services, Inc. or its Affiliates.


Automatically detecting personal protective equipment on persons in images using Amazon Rekognition

#artificialintelligence

The following image shows an example input image and its corresponding output from the DetectProtectiveEquipment as seen on the Amazon Rekognition PPE detection console. In this example, we supply face cover as the required PPE and 80% as the required minimum confidence threshold as part of summarizationattributes. We receive a summarization result that indicates that there are four persons in the image that are wearing face covers at a confidence score of over 80% [person identifiers 0, 1,2, 3]. It also provides the full fidelity API response in the per-person results. Note that this feature doesn't perform facial recognition or facial comparison and can't identify the detected persons.


Understand Movie Star Social Networks Using Amazon Rekognition and Graph Databases Amazon Web Services

@machinelearnbot

Amazon Rekognition is an AWS service that makes it easy to add image analysis to your applications. The latest feature added to the API for this deep-learning-powered computer vision is Celebrity Recognition. This simple-to-use functionality detects and recognizes thousands of individuals who are famous, noteworthy, or prominent in their field. Users can harness the tool to index and search digital image libraries for celebrities based on any particular interest. One common way we have seen our customers store data about individuals is within graph databases.


Build Your Own Face Recognition Service Using Amazon Rekognition Amazon Web Services

#artificialintelligence

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. It's based on the same proven, highly scalable, deep learning technology developed by Amazon's computer vision scientists to analyze billions of images daily for Amazon Prime Photos. Facial recognition enables you to find similar faces in a large collection of images. In this post, I'll show you how to build your own face recognition service by combining the capabilities of Amazon Rekognition and other AWS services, like Amazon DynamoDB and AWS Lambda. This enables you to build a solution to create, maintain, and query your own collections of faces, be it for the automated detection of people within an image library, building access control, or any other use case you can think of.