Inspired by the human visual system, convolutional neural networks involve neurons which learn from vast amounts of image data, to be able to recognize, classify and process new images.
Interested to learn more about deep learning? Click on any of the resources below.
Proprietary and Patent Pending Image Engines
A two step process: 1) Confirm the presence of aface. 2) Find its location in the image.
Faces of the same person are recognized,matched and clustered together.
It's a high level vision task where the machine is trained.to recognize the context of where the image was taken e.g beach, party, market etc.
The quality of the image is analysed based on the brightness, contrast, blur, tilt, other aesthetics and social parameters, to help weed out the bad photos.
We take 45 photos to get the perfect shot. This module detects identical and near identical photos, making it easy to clear up space or a create a collection.
Photo clustering is a semantic photo engine that clusters photos of the same event together for easy creation of albums, onetap sharing etc.
Make your albums short, sweet and sharable. Photo album summarization picks out the best photos that represent your entire album.
Automatically enhances (based on various visual cues) and reduces blemishes in faces in photos
Photo enhancement ensures near lossless compression of the image while optimizing, enhancing the photos based on aesthetics and photographic cues