Deep learning-based methods have existed for several decades. It was only in 2012 that these methods became the most important ones when a convolutional neural network technique won the ImageNet classification competition reducing the error rate from 25% to 16% all at once (contrary to iterative evolutions of ~1%).
Every year, at least one major evolution in the form of a new architecture or mechanism pushes the limits of deep learning. We are following all these major developments and we are trying these techniques as soon as possible. We can help you solve image processing problems using these new templates.
There are several learning techniques that are very effective on a small data set. Random forest or support vector machines (SVM) have shown their performance in solving a large number of problems. Some techniques even demonstrated results in 2019 that exceeded the performance of other Deep Learning techniques. We help you to use these techniques, which are sometimes part of a large treatment cycle.
Heuristics and techniques without learning
By defining the context well, many problems can be solved directly with techniques that do not require learning (Machine/Deep Learning). Mathematical or heuristic algorithms can be used alone, or in combination with learning techniques to solve a problem. Thanks to our experience, we can help you optimize your treatments in terms of accuracy and speed of execution.