Single image horizon line estimation is one of the most fundamental geometric problems in computer vision. Knowledge of the horizon line – the level of the viewer’s eye – enables a wide variety of applications, like detecting pedestrians or vehicles, and adjusting the perspective of photographs.
If you read our recent post on language detection, you already know how easy it is to use Algorithmia’s services to identify which language a given piece of text is written in.
Now let’s put that into action to perform a specific task: organizing documents into language-specific folders.
We’ll build our language detection microservice using Algorithmia’s language identification algorithm. Then, we’ll look through all the .txt and .docx files in a directory to see which language each one is written in.
Quick, what languages are these two sentences written in:
“Hey bana bir sorununuz olur mu?”
What about this one?
“Halló ég er með vandamál getur þú hjálpað mér?”
Not easy, right?
This week we look at Google’s real-time parking predictions, the largest dataset of annotated YouTube videos, and how Facebook is improving image search using deep learning.
Plus! What we’re reading this week and things to try at home!
This week we look at how he world’s best poker players are getting crushed by AI, why Apple joined the Partnership on AI, and how an algorithm is diagnosing skin cancer as accurate as dermatologists.
In our introduction to saliency detection post, we showed how to harness the power of the human brain using a saliency algorithm to detect the most distinct and noticeable objects in an image.
Whenever we look at a photo or watch a video, we always notice certain things over others. This could be a person’s face, or a sports car, or even a symbol that is located in the corner of a video.
This week we look at the 40% drop in car crashes using Tesla’s Autopilot, why Microsoft is investing into Montreal AI research, check out some algorithms IRL, and building neural networks in Python.
Sentiment Analysis is the use of natural language processing, statistics, and text analysis to extract, and identify the sentiment of text into positive, negative, or neutral categories. We often see sentiment analysis used to arrive at a binary decision: somebody is either for or against something, users like or dislike something, or the product is good or bad.