CMVS Research areas

The Center for Machine Vision and Signal analysis has achieved ground-breaking research results in many areas of its activity, including texture analysis, facial image analysis, 3D computer vision, energy-efficient architectures for embedded systems, and biomedical engineering. Among the highlights of its research are the Local Binary Pattern (LBP) methodology, LBP-based face descriptors, and methods for geometric camera calibration, which all are highly-cited and widely used around the world. The areas of application for CMVS's current research include affective computing, perceptual interfaces for humancomputer interaction, biometrics, augmented reality, and biosignal analysis.


Computer vision core: Image and video descriptors

Core part of research in CMVS is fundamental research on generic algorithms for computer vision. The Center has achieved ground-breaking research results in many areas of its activity, including texture and dynamic texture analysis.

Computer vision core: Face Analysis

Automatic face analysis is a very active topic in computer vision research as it is useful in several applications, like biometric identification, visual surveillance, human-machine interaction, video conferencing and content-based image retrieval.

Computer vision core: 3D vision

3D computer vision has been one of the core research areas in CMVS since early 1990’s. This research has resulted in many novel methods and software tools that have been widely used in the research community and companies.

Multimodal perceptual interfaces

Recognizing humans, their actions and emotions with computer vision, and providing an intelligent machine's response, will address some of the fundamental problems of affective human-computer interaction

Multimodal perception for affective computing

The capability to recognize human emotions plays a significang role in applications ranging from human computer interaction and entertainment to psychology and education. In CMVS we believe that combining complementary information from different modalities increases the accuracy of emotion recognition.


Low-energy computing

Low-energy computing requires that the power demands are on par with energy that can be harvested from the environment. In practice, only a few mW may be available for sensing, communications, and computing. In this domain we investigate energy efficient fully programmable architectures and high-level design tool chains.

Biomedical signal analysis

Individualised healthcare is a recent global megatrend aiming to improve health and wellbeing. We are developing breakthrough technologies to tackle key challenges including next generation signal analysis techniques towards personalized medicine and wellness solutions.


Biomedical image analysis

In recent years, increasing resolving power and automation of biomedical imaging systems have resulted in an exponential growth of the image data. Manual analysis of these data sets is extremely labor intensive and hampers the objectivity and reproducibility of results. Hence, there is a growing need for automatic image processing and analysis methods. In CMVS, our aim has been to apply modern computer vision techniques to biomedical image analysis which is one of our emerging research areas.