Mr. Dragan Sekulovski Profile

Dragan Sekulovski

at Philips Research Nederland BV

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Proceedings Article | 21 February 2012 Paper

An evaluation of different setups for simulating lighting characteristics

Bart Salters, Michael Murdoch, Dragan Sekulovksi, Shih-Han Chen, Pieter Seuntiens

Proceedings Volume 8291, 82911M (2012) https://doi.org/10.1117/12.912107

KEYWORDS: Light sources and illumination, Prototyping, Virtual reality, Visualization, Time multiplexed optical shutter, Computer simulations, Device simulation, Statistical analysis, 3D modeling, Image processing

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The advance of technology continuously enables new luminaire designs and concepts. Evaluating such designs has traditionally been done using actual prototypes, in a real environment. The iterations needed to build, verify, and improve luminaire designs incur substantial costs and slow down the design process. A more attractive way is to evaluate designs using simulations, as they can be made cheaper and quicker for a wider variety of prototypes. However, the value of such simulations is determined by how closely they predict the outcome of actual perception experiments. In this paper, we discuss an actual perception experiment including several lighting settings in a normal office environment. The same office environment also has been modeled using different software tools, and photo-realistic renderings have been created of these models. These renderings were subsequently processed using various tonemapping operators in preparation for display. The total imaging chain can be considered a simulation setup, and we have executed several perception experiments on different setups. Our real interest is in finding which imaging chain gives us the best result, or in other words, which of them yields the closest match between virtual and real experiment. To answer this question, first of all an answer has to be found to the question, "which simulation setup matches the real world best?" As there is no unique, widely accepted measure to describe the performance of a certain setup, we consider a number of options and discuss the reasoning behind them along with their advantages and disadvantages.

Proceedings Article | 28 January 2008 Paper

Enriching text with images and colored light

Dragan Sekulovski, Gijs Geleijnse, Bram Kater, Jan Korst, Steffen Pauws, Ramon Clout

Proceedings Volume 6820, 682004 (2008) https://doi.org/10.1117/12.766421

KEYWORDS: Genetic algorithms, Image segmentation, Quantization, Light sources and illumination, Image processing, Image retrieval, Detection and tracking algorithms, Prototyping, Multimedia, Distance measurement

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We present an unsupervised method to enrich textual applications with relevant images and colors. The images are collected by querying large image repositories and subsequently the colors are computed using image processing. A prototype system based on this method is presented where the method is applied to song lyrics. In combination with a lyrics synchronization algorithm the system produces a rich multimedia experience. In order to identify terms within the text that may be associated with images and colors, we select noun phrases using a part of speech tagger. Large image repositories are queried with these terms. Per term representative colors are extracted using the collected images. Hereto, we either use a histogram-based or a mean shift-based algorithm. The representative color extraction uses the non-uniform distribution of the colors found in the large repositories. The images that are ranked best by the search engine are displayed on a screen, while the extracted representative colors are rendered on controllable lighting devices in the living room. We evaluate our method by comparing the computed colors to standard color representations of a set of English color terms. A second evaluation focuses on the distance in color between a queried term in English and its translation in a foreign language. Based on results from three sets of terms, a measure of suitability of a term for color extraction based on KL Divergence is proposed. Finally, we compare the performance of the algorithm using either the automatically indexed repository of Google Images and the manually annotated Flickr.com. Based on the results of these experiments, we conclude that using the presented method we can compute the relevant color for a term using a large image repository and image processing.

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