image recognition

Image Recognition and Transfer Learning

Image Recognition and Transfer Learning

For human beings, interpreting what we see is so easy that we are hardly conscious of it; However, for computers, these tasks are very difficult problems to solve.

In this blog, we will briefly introduce image recognition with transfer learning. At its most fundamental, an image recognition algorithm takes images and outputs a label describing the image.

We will classify images from the Caltech 101 dataset with the Open Source Computer Vision (OpenCV) library.

Image Recognition: Getting Value from Visual Data

Image Recognition: Getting Value from Visual Data

Machine vision is one of the pillars of machine learning. Its stature will grow in importance as more accurate algorithms are explored, but has already been used by Amazon for online shopping, Google for image search and Facebook for its facial recognition. Here we give a brief overview of convolutional neural networks, one of the best deep learning algorithms for image recognition. 

Archaeological Site Prediction Using Machine Learning

Archaeological Site Prediction Using Machine Learning

Machine Learning is still in its infancy in archaeology, but the approach holds enormous promise. Just as in finance and disaster mitigation, the use of satellite imagery has become a vital resource for archaeology. As more archaeologists around the world automate the process of identifying archaeological sites with satellite images, sites will be found at a faster rate than ever before.

Image Recognition: Retinopathy

Image Recognition: Retinopathy

Almost every major company now has vested interest in image recognition algorithms: Tesla for autonomous driving, Amazon for product and price comparison, Google for its image search, and of course Facebook for facial recognition. 

The potential for this type of technology is limitless and has already been used in fields as disparate as sport science to astronomy. Here we discuss one recent and important example in the medical field: diabetic retinopathy.  

Diagnosis software, based on image recognition, allows quick and cheap diagnosis, saving human hours as well as human lives.