Less Light, Better Bite: How Ambient Lighting Influences Taste Perceptions

Atmospheric factors within a retail environment provide efficient and effective methods for influencing customer behavior. Drawing on the concept of sensory compensation, this research investigates how ambient lighting influences taste perceptions. Three studies demonstrate that dim lighting enhances taste perceptions. The results of Studies 1a and 1b provide support that low lighting positively influences consumers\u27 perceived taste of single taste dimension foods (e.g., sweet). Study 2 shows the number of taste dimensions (e.g., sweet vs. sweet and salty) stimulated serves as a boundary condition, attenuating the significant effect of dim lighting on taste perceptions

The synthesis and analysis of color images

A method is described for performing the synthesis and analysis of digital color images. The method is based on two principles. First, image data are represented with respect to the separate physical factors, surface reflectance and the spectral power distribution of the ambient light, that give rise to the perceived color of an object. Second, the encoding is made efficient by using a basis expansion for the surface spectral reflectance and spectral power distribution of the ambient light that takes advantage of the high degree of correlation across the visible wavelengths normally found in such functions. Within this framework, the same basic methods can be used to synthesize image data for color display monitors and printed materials, and to analyze image data into estimates of the spectral power distribution and surface spectral reflectances. The method can be applied to a variety of tasks. Examples of applications include the color balancing of color images, and the identification of material surface spectral reflectance when the lighting cannot be completely controlled

Tessellated Voxelization for Global Illumination using Voxel Cone Tracing

Modeling believable lighting is a crucial component of computer graphics applications, including games and modeling programs. Physically accurate lighting is complex and is not currently feasible to compute in real-time situations. Therefore, much research is focused on investigating efficient ways to approximate light behavior within these real-time constraints. In this thesis, we implement a general purpose algorithm for real-time applications to approximate indirect lighting. Based on voxel cone tracing, we use a filtered representation of a scene to efficiently sample ambient light at each point in the scene. We present an approach to scene voxelization using hardware tessellation and compare it with an approach utilizing hardware rasterization. We also investigate possible methods of warped voxelization. Our contributions include a complete and open-source implementation of voxel cone tracing along with both voxelization algorithms. We find similar performance and quality with both voxelization algorithms

ITERL: A Wireless Adaptive System for Efficient Road Lighting

This work presents the development and construction of an adaptive street lighting system that improves safety at intersections, which is the result of applying low-power Internet of Things (IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements are sent to a coordinator node that collects and passes them to a local controller, which then makes decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to maintain up-to-date information for the system, enabling preventive maintenance.Consejería de Fomento y Vivienda Junta de Andalucía G-GI3002 / IDIOFondo Europeo de Desarrollo Regional G-GI3002 / IDI

SenseCam image localisation using hierarchical SURF trees

The SenseCam is a wearable camera that automatically takes photos of the wearer's activities, generating thousands of images per day. Automatically organising these images for efficient search and retrieval is a challenging task, but can be simplified by providing semantic information with each photo, such as the wearer's location during capture time. We propose a method for automatically determining the wearer's location using an annotated image database, described using SURF interest point descriptors. We show that SURF out-performs SIFT in matching SenseCam images and that matching can be done efficiently using hierarchical trees of SURF descriptors. Additionally, by re-ranking the top images using bi-directional SURF matches, location matching performance is improved further