Computer Vision

Automated Aerial Image Interpretation: from geometric alignment to semantic segmentation and recognition of object categories

The domain of Computer Vision studies and develops computational methods and systems that are capable of perceiving the world through images and videos in a smart manner, as close as possible to the level of human visual perception. Despite being a relatively new subfield in Artificial Intelligence and Robotics, computer vision currently enjoys a fast-growing development in both scientific research and industry. Recent success is due not only to the development of effective machine learning algorithms, but also to the substantial increase in computation power and data storage capabilities.

Computer Vision will play an important role in the world of tomorrow, having the potential to improve quality of life and future technologies. Here we are committed to develop such smart vision systems, which should be capable of operating in close relationship to various areas of robotics, such as autonomous aerial vehicles. We aim to develop high performance prototypes through scientific research, as well as create technological systems that have immediate usability. Thus, we shall try to discover new aspects derived from the connections between eye, sight and thinking. We should also develop computing systems that may support such complex cognitive processes.

The programme addresses Bachelor’s and Master’s students who are passionate about science and are eager to study such methods that enable the automated interpretation of images and videos. The objective of the programme is to help us form a team of students and engineers, with the appropriate theoretical and practical skills and knowledge. In particular, we will focus on the following three directions:

1) The identification of common areas between collections of video frames or photographs and their subsequent matching and geometric alignment.

2) The semantic segmentation of images. This task involves finding image regions that belong to certain semantic categories, such as residential areas, forests, parks, roads, lakes, or rivers, among others.

3) The detection and recognition of various object categories, such as houses or cars. We want to determine the way these object categories or area types interact with each other, at the contextual interpretation level, in order to facilitate their efficient detection and recognition.

Programme Coordinator: Marius Leordeanu, PhD, Associate Professor (Reader)

What do we undertake?

We undertake to design and implement efficient algorithmic solutions. To this purpose, we will first address the task of mid-level interpretation. In particular we will focus on geometric image alignment, including the identification of correspondences between aerial image features. We will also develop methods for creating panoramic views – the result will be a single aerial map containing several frames aligned in the same coordinate system. We will also consider the estimation of the motion field (or dense matches) between successive frames.

Furthermore, we will seek to develop machine learning methods for the categorization and detection of various areas and object types. We will also analyze their contextual relationship in order to obtain a full semantic segmentation and interpretation of aerial images and videos.