The concept of ‘computer vision’ pertains to the domain of deep learning technologies which empower the discernment of shapes, objects, or individuals within images or videos.
Computer vision is a branch of information science. Using artificial intelligence, computer vision can identify features in images and videos. It can massively process data more accurately and much more quickly than humans. This is particularly useful in medicine where it can detect anomalies in MRI and CT scan images. Computer vision is used for classification, facial recognition, and object detection in images and videos.
It is very important to give the model a very varied data set
Computer vision is based on deep learning neural networks. To be effective, these models have to be provided with training. How exactly does this work? They need to be fed data that has been labeled. The labels are used to provide information to the model. For example, for a classification task to classify images as either dogs or cats, all the images have to be labeled as “dog” or “cat”. It is very important to give the model a very varied data set. For example, if it is to distinguish between dogs and cats, it needs to see dogs of different breeds and colors to create a general model that has been deeply learned.
Once the model has been trained, it will be able to recognize images as either dogs or cats. Computer vision models can read satellite images to identify geometric forms in agriculture and different colors. These features have enabled us to automate soil mapping.
Floriance Schreiber is a PhD specialist in remote sensing at The Ecosystem Restoration Standard (ERS).