Beril Sirmacek
Beril Sirmacek
Beril Sirmacek is a Dutch AI researcher. She received her PhD degree in Electrical and Electronics Engineering in 2009. Later she has worked with German Aerospace Center and pursued a habilitation degree with University of Osnabrueck. She is an assistant professor at Jonkoping AI Lab and also leading her company create4D. Beril is passionate to use computer vision and AI algorithms for creating useful healthcare and earth care solutions. Beril has taught a MSc course at the University of Augsburg in 2010, only on OpenCV. As a computer vision scientist and developer, she has been using it regularly for many years.
ML conf EU 2020ML conf EU 2020
32 min
Computer Vision Using OpenCV
As an AI scientist and a developer, I have been engaged with AI-applications for many years especially focusing on object detection and recognition purposes. I love thinking that we can get creative in designing neural networks. We can train them supervised, unsupervised, semi or self-supervised, and this gives possibilities to mimic the human brain in a narrow domain. However, in vision applications, there are still things where AI is lacking and will be lacking without computer vision knowledge. Computer vision has been solving detection and recognition problems for many years. However, in the last decade, it seems like AI is seen as a replacement of computer vision. AI can find the optimal model for a specific type of data set and it might achieve generalization better. AI can be designed in a way that it can learn life-long which also brings possibilities of creating models which serve better when they are used longer. However, an AI vision system will be lacking capabilities without computer vision knowledge. First of all, it will require a very big data set to train the model what can be expensive or even not possible. On the other hand, computer vision systems can be modeled only using a hand-drawn template image. Training AI models also requires GPUs. Nevertheless, I do not want to encourage everyone to train AI models for solving any simple problem which could be solved easily computer vision. Last but not least, knowing computer vision, machine learning and especially feature engineering methods helps to design hybrid models that might be more robust to adversarial attacks or changing conditions.
In this lecture, I will briefly introduce how computer vision (especially using the OpenCV library) and machine learning can be used for creating detection and recognition models. Some experience with python, jupyter notebook and some machine learning background would be useful to get more benefits from this lecture.