Recently a series of key factors are deeply transforming lighting systems. Single lights can be controlled individually and with advanced rendering capabilities. Furthermore, a shift is underway from independent light sources to integrated lighting installations. These factors fuel the need for intuitive, flexible control techniques that can fully exploit the rendering capabilities of a lighting infrastructure. In this paper, we present a novel framework to automatically create lighting atmospheres in any type of environment. The desired light settings are derived by approximation of user-defined input images or videos. This input modality gives an immediate and intuitive representation of a lighting atmosphere and allows the user to translate into a light setting any visual content. The effectiveness and versatility of the proposed solution is demonstrated and discussed by deploying the system in several challenging, real-life application scenarios.

In computer vision, many applications could greatly benefit from multi-spectral image data. Our aim is to illustrate the effectiveness of multi-spectral analysis obtained from a simple and cost-effective system. While the proposed approach is broadly applicable, in this paper we focus on the specific case of skin detection. To obtain the multi-spectral data, we have assembled a system using multiple LEDs with different spectra to illuminate the scene and a conventional RGB camera to acquire images. A methodology is proposed to avoid strict requirements on the experimental environment, by adopting a simple training procedure which is tuned for the detection of human skin. Next a specific feature set is defined and a corresponding normalization method is designed to improve the robustness to changes in skin color and incident light, issues not addressed by available prior art. Finally, we use supervised learning to train our skin detector. We demonstrate the accuracy and effectiveness of our skin detector through extensive benchmarking. The proposed methodology enables a superior performance of skin detection compared to relevant alternative proposals.

The advent of integrated lighting installations, consisting of individually controllable lamps with advanced rendering capabilities, is fundamentally transforming lighting. This brings a need for an intuitive control capable of fully exploiting the rendering capabilities of the complete lighting infrastructure. In this paper we present a new method to automatically create lighting atmospheres in any type of environment, that allows for an natural interaction with the lighting system and generates unique, suggestive effects. To prove the effectiveness and versatility of the proposed solution, we deploy the system in several application scenarios, and discuss the results.

Flower Power is an interactive installation that creates immersive light atmospheres by capturing the colors of flowers and re-projecting them in the environment. A camera is suspended over a flower bed and its position depends on the positions of the visitors around the installation, which are detected using presence sensors. The image captured by the camera is rendered into the surrounding environment as a suggestive, saturated light scene. The participants can thus give the power to their favorite flower to set the overall light experience. In this paper we describe the motivation and the realization of the installation, and we discuss insights and reactions collected during the 2010 STRP Festival.

Robust background subtraction under sudden illumination changes is a challenging problem. In this paper, we propose an approach to address this issue, which combines the Eigenbackground algorithm together with a statistical illumination model. The first algorithm is used to give a rough reconstruction of the input frame, while the second one improves the foreground segmentation. We introduce an online spatial likelihood model by detecting reliable background and foreground pixels. Experimental results illustrate that our approach achieves consistently higher accuracy compared to several state-of-the-art algorithms.

Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful for facial representation. In this paper, we propose to learn discriminative LBP-Histogram (LBPH) bins for the task of facial expression recognition. Our experiments illustrate that the selected LBPH bins provide a compact and discriminative facial representation. We experimentally illustrate that it is necessary to consider multiscale LBP for representing faces, and most discriminative information is contained in uniform patterns. By adopting SVM with the selected multiscale LBPH bins, we obtain the best recognition performance of 93.1% on the Cohn-Kanade database. 1

In this paper, we extensively investigate local features based facial expression recognition with face registration errors, which has never been addressed before. Our contributions are three fold. Firstly, we propose and experimentally study the histogram of oriented gradients (HOG) descriptors for facial representation. Secondly, we present facial representations based on local binary patterns (LBP) and local ternary patterns (LTP) extracted from overlapping local regions. Thirdly, we quantitatively study the impact of face registration errors on facial expression recognition using different facial representations. Overall LBP with overlapping gives the best performance (92.9% recognition rate on the Cohn-Kanade database), while maintaining a compact feature vector and best robustness against face registration errors.