As an artist, inspiration can come from anywhere: a particular texture, a design, or even a color scheme.
Instead of spending hours painstakingly extracting the hex codes from all of the important sections of an image, what if there was a way to automatically extract the most important parts of an image?
Color Scheme extraction is able to find the most relevant colors in seconds.
Computer vision is behind some of the most interesting recent advances in technology. From algorithms that can identify skin cancer as well as dermatologists to cars that drive themselves, it’s computer vision algorithms that are behind these advances.
While CV algorithms have been around in various forms since the 1960s, it wasn’t until recently that it’s progressed to far more sophisticated levels. In particular, combining computer vision with machine learning has yielded some amazing results. Read More…
This week we look at Google’s new cloud GPUs, how to deploy deep learning models in the cloud, and what applied machine learning looks like at Facebook, Pinterest and others.
When we look at an image, it’s fairly easy to detect the horizon line.
For computers, this task is somewhat more difficult: they need to understand the basic structure of the image, locate edges which might indicate a horizon, and pare out the edges which do not matter. Fortunately, Algorithmia boils this all down to a single API call: just send your image to deep horizon, an algorithm for horizon detection, and it tells you where the horizon line is.
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.