Image and video descriptors play a key role in most computer vision systems and applications. The function of descriptors is to convert pixel-level information into a useful form, which captures the most important factors into a useful form but is insensitive to irrelevant aspects caused by the varying environment. While the definition of irrelevant depends on the application, the most common cases are related to imaging conditions like illumination, viewing angle, scale, noise, and blur. The main focus of our research has been in texture descriptors, in which area we have long traditions and rank among the world leaders.
Texture is an important characteristic of many types of images and videos. The Local Binary Pattern (LBP) texture operator has been highly successful in numerous applications around the world, and has inspired plenty of new research on related methods, including the blur-insensitive Local Phase Quantization (LPQ) method, Weber Law Descriptor (WLD), Binarized Statistical Image Features (BSIF), and Local Orientation Adaptive Descriptor (LOAD) also developed by researchers of CMVS. Spatio-temporal dynamic texture descriptors for video analysis have also been proposed. The most popular one among these is LBP-TOP. One focus of current research is in combining these kind of “traditional” descriptors with deep learning based approaches, with applications in face analysis and biomedical image analysis, for example.
Ojala T, Pietikäinen M & Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7):971-987.
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Last updated: 22.8.2016