Director – Research – Thomson Reuters – Toronto, ON
From Thomson Reuters – Thu, 03 Oct 2019 10:13:51 GMT – View all Toronto, ON jobs
Hey guys!
Looking for papers on Face Reenactment. I’ve gone through Face2Face and the results look pretty good! Was wondering if there are any newer implementations.
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Amazon Textract is a machine learning service that makes it easy to extract text and data from virtually any document. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables. This allows you to use Amazon Textract to instantly “read” virtually any type of document and accurately extract text and data without the need for any manual effort or custom code.
The blog post Automatically extract text and structured data from documents with Amazon Textract shows how to use Amazon Textract to automatically extract text and data from scanned documents without any machine learning (ML) experience. One of the use cases covered in the post is search and discovery. You can search through millions of documents by extracting text and structured data from documents with Amazon Textract and creating a smart index using Amazon ES.
This post demonstrates how to generate searchable PDF documents by extracting text from scanned documents using Amazon Textract. The solution allows you to download relevant documents, search within a document when it is stored offline, or select and copy text.
You can see an example of searchable PDF document that is generated using Amazon Textract from a scanned document. While text is locked in images in the scanned document, you can select, copy, and search text in the searchable PDF document.
To generate a searchable PDF, use Amazon Textract to extract text from documents and add the extracted text as a layer to the image in the PDF document. Amazon Textract detects and analyzes text input documents and returns information about detected items such as pages, words, lines, form data (key-value pairs), tables, and selection elements. It also provides bounding box information, which is an axis-aligned coarse representation of the location of the recognized item on the document page. You can use the detected text and its bounding box information to place text in the PDF page.
PDFDocument is a sample library in AWS Samples GitHub repo and provides the necessary logic to generate a searchable PDF document using Amazon Textract. It also uses open-source Java library Apache PDFBox to create PDF documents, but there are similar PDF processing libraries available in other programming languages.
The following code example shows how to use sample library to generate a searchable PDF document from an image:
The following code shows how to take an image document and generate a corresponding searchable PDF document. Extract the text using Amazon Textract and create a searchable PDF by adding the text as a layer with the image.
The following code example takes an input PDF document from an Amazon S3 bucket and generates the corresponding searchable PDF document. You extract text from the PDF document using Amazon Textract, and create a searchable PDF by adding text as a layer with an image for each page.
To run the code on a local machine, complete the following steps. The code examples are available on the GitHub repo.
For more information, see Getting Started with Amazon Textract.
mvn package.java -cp target/searchable-pdf-1.0.jar Demo.This runs the Java project with Demo as the main class.
By default, only the first example to create a searchable PDF from an image on a local drive is enabled. To run other examples, uncomment the relevant lines in Demo class.
To run the code in Lambda, complete the following steps. The code examples are available on the GitHub repo.
mvn package.The build creates a .jar in project-dir/target/searchable-pdf1.0.jar, using information in the pom.xml to do the necessary transforms. This is a standalone .jar (.zip file) that includes all the dependencies. This is your deployment package that you can upload to Lambda to create a function. For more information, see AWS Lambda Deployment Package in Java. DemoLambda has all the necessary code to read S3 events and take action based on the type of input document.
DemoLambda::handleRequest.documents.Make sure that you set a trigger for the documents folder. If you add a trigger for the whole bucket, the function also triggers every time an output PDF document generates.
In a few seconds, you should see the searchable PDF document in your S3 bucket.
These steps show simple S3 and Lambda integration. For large-scale document processing, see the reference architecture at following GitHub repo.
This post showed how to use Amazon Textract to generate searchable PDF documents automatically. You can search across millions of documents to find the relevant file by creating a smart search index using Amazon ES. Searchable PDF documents then allows you to select and copy text and search within a document after downloading it for offline use.
To learn more about different text and data extraction features of Amazon Textract, see How Amazon Textract Works.
Kashif Imran is a Solutions Architect at Amazon Web Services. He works with some of the largest strategic AWS customers to provide technical guidance and design advice. His expertise spans application architecture, serverless, containers, NoSQL and machine learning.
Hi all,
I just wanted to get a straw poll opinion on this.
I’m currently a C-suite level employee of a company and I make over $100k/year. My background is backend and front end development and now my job is largely project management with some coding.
I’ve been taking the Fast AI course and wondered whether career transitions to machine learning are quite do-able?
I currently work from home (remote) so would be looking to do the same with machine learning.
I suppose I’m wondering whether salary + remote work would remain as reasonable expectations in a career switch.
Any info or insight would be great 👍
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Not sure how many Ultrasound or medical imaging folks are in here, but thought this might be useful to this group. I’m part of an ultrasound research lab at Duke University, and we’ve recently open-sourced work on ultrasound image post-processing which allows one to mimic proprietary post-processing black-boxes found on commercial ultrasound scanners. Here is the: Paper, Github, Colab notebook.
https://arxiv.org/abs/1908.05782
When creating an ultrasound image from scratch, it is common to have speckle noise, Gaussian noise, clutter, reverberation, and other undesirable forms of image degradation. While raw ultrasound images are very familiar to researchers, medical providers will typically only look at heavily post-processed images in the clinic. Unfortunately, commercial post-processing is generally proprietary and kept secret. The inaccessibility makes apples-to-apples comparisons of novel methods to current clinical practice difficult. It also makes the translation of novel methods into the clinic difficult. Ideally, the post-processing is not secret, and everyone can always have lovely images to look at as a baseline. We find that it is possible to mimic the post-processing found on commercial scanners through CycleGANs by just using images acquired via regular use. CycleGANs do not require any image registration or image pairing to train, which is very convenient. We are releasing the fully trained models so that any researcher has access to clinical-grade like post-processing. We refer to our trained models as MimickNet.
TLDR: Clinical Ultrasound Post-Processing is kept proprietary and secret. However, by using data collected just via intended ultrasound scanner use, it is possible to mimic the post-processing algorithm found on some of the best ultrasound scanners. We are making these models available to any researcher, so we all have access to clinical-grade post-processing.
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Have you ever wondered what it takes to produce the complex imagery in films like Star Wars or Transformers? The man behind the magic, Colie Wertz, is here to explain.
Wertz is a conceptual artist and modeler who works on film, television and video games. He sat down with AI Podcast host Noah Kravitz to explain his specialty in hard modeling, in which he produces digital models of objects with hard surfaces like vehicles, robots and computers.
To make these images, Wertz has taken to using AI art tools such as GauGAN, a real-time painting web app that allows users to create realistic landscapes using generative adversarial networks.
Rather than use GauGAN in the traditional manner, Wertz makes the tools “trick themselves” by putting a mountain in the sky, or snow falling at the bottom of the page, to create a unique image. Then he incorporates his signature spaceships into the scene.

Wertz appreciates how easily GauGAN builds a background. He says, “Coming from the hard surface world, that’s the kind of stuff that’s kind of always been a curveball for me, like matte painting and background composition.” Now, Wertz is able to focus on the ship and how to “integrate it into a background.”
For some of his creations, Wertz uses the GauGAN landscape to inspire his ship designs. He views AI art as a “creative partner” rather than a replacement for more traditional forms of art.
Wertz’s artistic career kickstarted after he left an architectural design firm in South Carolina and moved to Los Angeles to develop his digital art skills. There, he entered one of his spaceship models created with Photoshop into a contest put on by visual effects production company Electric Image.

Caption: Wertz views AI art as a “creative partner” rather than a replacement for more traditional forms of art.
The judges were impressed, and Wertz ended up with a job at Industrial Light & Magic, a visual effects company founded by George Lucas. Wertz’s first job was working on the rerelease of Return of the Jedi, building digital models for matte painters.
For listeners curious about Wertz’s current work, they can look at his portfolio, visit his website or follow him on Instagram.
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The post GauGAN Rocket Man: Conceptual Artist Uses AI Tools for Sci-Fi Modeling appeared first on The Official NVIDIA Blog.
It all depends on the available data and the variety of activities. You can’t just pour a bunch of data into an ML model and expect it do detect threats. In many cases, the key is to combine human analysts with ML algorithms.
https://bdtechtalks.com/2019/07/29/machine-learning-ai-cybersecurity/
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