Monthly Archives: November 2017

Vlastimil Havran: Surface reflectance in rendering algorithms

havran-bigVlastimil Havran is Associate professor at the Czech Technical University in Prague. His research interests include data structures and algorithms for rendering images and videos, visibility calculations, geometric range searching for global illumination, software architectures for rendering, applied Monte Carlo methods, data compression etc. His talk takes place on Monday, December 4, 2017 at 12:00 in room E105.

Surface reflectance in rendering algorithms

The rendering of images by computers, i.e., computationally solving a rendering equation, consists of three components: computing visibility for example by ray tracing, the interaction of light with surface and efficient Monte Carlo sampling algorithms. In this talk, we focus on various aspects of surface reflectance. That is a key issue to get high fidelity of objects’ visual appearance in the rendered images not only in the movie industry but also in real time applications of virtual and augmented reality. First, we recall the initial concepts of surface reflectance and its use in rendering equation. Then we will present our results on the surface reflectance characterization and its possible use in rendering algorithms. Further, we will show why the standard surface reflectance model usually represented as bidirectional reflectance distribution function needs to be extended spatially to achieve high fidelity of visual appearance. As this spatial extension leads to a big data problems, we will describe our algorithm for compression of spatially varying surface reflectance data. We also will describe an effective perceptually motivated method to compare two similar surface reflectance datasets, where one can be the reference data and the second one the result of its compression. As the last topic, we will describe the concepts and problems when we measure such surface reflectance datasets for real-world applications.


Themos Stafylakis: Deep Word Embeddings for Audiovisual Speech Recognition

Themos Stafylakis is a Marie Curie Research Fellow on audiovisual automatic speech recognition at the Computer Vision Laboratory of University of Nottingham (UK). He holds a PhD from Technical University of Athens (Greece) on Speaker Diarization for Broadcast News. He has a strong publication record on speaker recognition and diarization, as a result of his 5-year post-doc at CRIM (Montreal, Canada), under the supervision of Patrick Kenny. He is currently working on lip-reading and audiovisual speech recognition using deep learning methods. His talk takes place on November 22, 2017 at 13:00 in room A112.

Deep Word Embeddings for Audiovisual Speech Recognition

During the last few years, visual and audiovisual automatic speech recognition (ASR) are witnessing a renaissance, which can largely be attributed to the advent of deep learning methods. Deep architectures and learning algorithms initially proposed for audio-based ASR are combined with powerful computer vision models and are finding their way to lipreading and audiovisual ASR. In my talk, I will go through some of the most recent advances in audiovisual ASR, with emphasis on those based on deep learning. I will then present a deep architecture for visual and audiovisual ASR which attains state-of-the-art results in the challenging lipreading-in-the-wild database. Finally, I will focus on how this architecture can generalize to words unseen during training and discuss its applicability in continuous speech audiovisual ASR.