Filip Šroubek received the M.Sc. degree in computer science from the Czech Technical University, Prague, Czech Republic in 1998 and the Ph.D. degree in computer science from Charles University, Prague, Czech Republic in 2003. From 2004 to 2006, he was on a postdoctoral position in the Instituto de Optica, CSIC, Madrid, Spain. In 2010/2011 he received a Fulbright Visiting Scholarship at the University of California, Santa Cruz. Currently he is with the Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic. His talk takes place on Tuesday, February 24 at 11am in room E105.

Advances in Image Restoration: from Theory to Practice

Abstract: We rely on images with ever growing emphasis. Our perception of the world is however limited by imperfect measuring conditions and devices used to acquire images. By image restoration, we understand mathematical procedures removing degradation from images. Two prominent topics of image restoration that has evolved considerably in the last 10 years are blind deconvolution and superresolution. Deconvolution by itself is an ill-posed inverse problem and one of the fundamental topics of image processing. The blind case, when the blur kernel is also unknown, is even more challenging and requires special optimization approaches to converge to the correct solution. Superresolution extends blind deconvolution by recovering lost spatial resolution of images. In this talk we will cover the recent advances in both topics that pave the way from theory to practice. Various real acquisition scenarios will be discussed together with proposed solutions for both blind deconvolution and superresolution and efficient numerical optimization methods, which allow fast implementation. Examples with real data will illustrate performance of the proposed solutions.

Ondřej Chum received the MSc degree in computer science from Charles University, Prague, in 2001 and the PhD degree from the Czech Technical University in Prague, in 2005. From 2005 to 2006, he was a research Fellow at the Centre for Machine Perception, Czech Technical University. From 2006 to 2007 he was a post-doc at the Visual Geometry Group, University of Oxford, UK. Recently, he is now an associate professor back at the Centre for Machine Perception. His research interests include object recognition, large-scale image and particular-object retrieval, invariant feature detection, and RANSAC-type optimization. He has coorganized the “25 years of RANSAC” Workshop in conjunction with CVPR 2006, Computer Vision Winter Workshop 2006, and Vision and Sports Summer School (VS3) in Prague 2012 and 2014. He was the recipient of the runner up award for the “2012 Outstanding Young Researcher in Image & Vision Computing” by the Journal of Image and Vision Computing for researchers within seven years of their PhD, and the Best Paper Prize at the British Machine Vision Conference in 2002. In 2013, he was awarded ERC-CZ grant. His talk takes place on Wednesday, January 28 at 3pm in room E104.

Visual Retrieval with Geometric Constraint

Abstract: In the talk, I will address the topic of image retrieval. In particular, I will focus on retrieval methods based on bag of words image representation that exploit geometric constrains. Novel formulations of image retrieval problem will be discussed, showing that the classical ranking of images based on similarity addresses only one of possible user requirements. Retrieval methods efficiently solving the new formulations by exploiting geometric constraints will be used in different scenarios. These include online browsing of image collections, image analysis based on large collections of photographs, or model construction.

For online browsing, I will show queries that try to answer question such as: “What is this?” (zoom in at a detail), “Where is that?” (zoom-out to larger visual context), or “What is to the left / right of this?”. For image analysis, two novel problems straddling the boundary between image retrieval and data mining are formulated: for every pixel in the query image, (i) find the database image with the maximum resolution depicting the pixel and (ii) find the frequency with which it is photographed in detail.