Adam Novozámský

Out of all the five senses, our sight seems to be the most important. Sight and vision help people to connect with their surroundings. People like to express themselves through pictures and approximately 65% of the general population are visual learners.
Nowadays, we live in the world with technologies such as Augmented Reality, Deep Fakes, or 3D-Medical Imaging. We meet images or videos everywhere around us, on the Internet, in newspapers and magazines, on television, at the doctor's office, or in private family albums.
No wonder that Image Processing (IP) and Computer Vision (CV) can be found in thousands of scientific, consumer, industrial, and artistic applications. This fantastic diversity of applications, modalities, and image types makes Image Processing such an exciting topic to study, where every project needs a genuine, innovative approach.
In my PhD study and on the postdoc position, I had an excellent opportunity to study many specialized topics in the fields of IP & CV.

Medical Imaging has a long history reaching back to the end of the 19th century, when in 1895 Wilhelm Conrad Roentgen took the first medical image, the radiograph of the left hand of his wife Bertha. Only two months later, he performed the first clinical use and after half a year he presented a new kind of ray. His discovery had a profound impact on medicine. He was awarded the first Nobel Prize in Physics and many techniques were developed based on his approach.
Radiography, Computer Tomography (CT), Positron emission tomography (PET), Medical Ultrasound, and Magnetic resonance imaging (MRI), these five techniques are representatives of Medical Imaging. The last two are non-invasive and risk-free so far as we know. The first three are considered non-invasive as well, besides some risks associated with radiation exposure. Until recently, we have had to consider imaging in the visible spectrum as invasive, carried out through a surgery or classic endoscopy. But today's progress brings miniaturization and new technologies to overcome this issue.
Over the past several years, our department has worked closely with leading medical institutions in Czechia. Among other things, these long-lasting partnerships have resulted in two medical projects in which I was involved. Both are very specialized and deal with the display of the human body in the visible spectrum. The first one is the Wireless Capsule Endoscopy focused on imaging of the gastrointestinal tract. The second one is the Videokymography focused on imaging vocal cord function.

Image Forensics is very closely related to the history of analog photography. One of the first tampered images originated a few decades after Niépce created the first photographs in 1826. A nice example of the analog image splicing was created sometime around the year 1864, during the American Civil War.
Tampering with images is easy with today's software tools for Image Processing. A forger can add or remove important information which completely changes the message of a processed photo. The integrity of visual data is important for the credibility of news media and especially when used as an evidence in court or during criminal investigations. For this reason, we have observed a dynamic development in this research area in recent years. Over time, two branches of forensic analysis of digital images and videos have become established as essential. One of them is integrity verification (authenticity analysis), which determines if the material has been added, altered, deleted or changed in the image. The second one is image ballistics (source device verification), which identifies the source device of the acquisition process of the image.
Although past research in these areas has mainly focused on data hiding and digital watermarking approaches, today there is a growing alternative approach called the passive one, which does not need to embed any secondary data into the image.
Methods published in this research area have, for example, attempted to detect image splicing, traces of inconsistencies in color filter array interpolation, traces of geometric transformations, cloning, computer graphics generated photos, JPEG compression inconsistencies, file structure inconsistencies, etc. Typically, these methods are based on the fact that digital image editing brings specific detectable statistical or geometrical changes into the image. Others are looking for a distortion in the reality of the image scene.

The contribution of our department to this area of research is covered by these papers:
[Ministry of Interior : VG20102013064]Pizzaro: Forensic analysis and restoration of image and video data [H2020-EU.2.1.1. : 825227]IMD2020: A Large-Scale Annotated Dataset Tailored for Detecting Manipulated Images

Image analysis methods and visualization are critical for understanding various features of cell and molecular biology.
An increasing resolving power and efficiency of microscopic image acquisition hardware brings exponentially growing of biological image data sets. It poses great methodological challenges for image processing and quantitative analysis. Our department has a strong research program in this area of science and creates a technological background for other biological researchers from different angles of view.

Machine learning, especially Neural Networks architecture, is an extremely hot area in Artificial Intelligence and Computer vision. Over the last years, deep learning methods have been shown to outperform previous state-of-the-art techniques in several fields. On the other hand, a lot of scientists use Convolution Neural Networks without understanding their internal structure.
So one of my interests is understanding this internal structure and mechanisms of such machine learnig techniques and designing more efficient networks.