Asla Medeiros Sá

Planned Sampling of Spatially Varying BRDFs

Measuring reflection properties of a 3D object involves capturing images for numerous viewing and lightingdirections. We present a method to select advantageous measurement directions based on analyzing the estimationof the bi‐directional reflectance distribution function (BRDF). The selected directions minimize the uncertaintyin the estimated parameters of the BRDF. As a result, few measurements suffice to produce models that describethe reflectance behavior well. Moreover, the uncertainty measure can be computed fast on modern graphics cardsby exploiting their capability to render into a floating‐point frame buffer. This forms the basis of an acquisitionplanner capable of guiding experts and non‐experts alike through the BRDF acquisition process. We demonstratethat spatially varying reflection properties can be captured more efficiently for real‐world applications using ouracquisition planner.

A Camera-Projector System for Real-Time 3D Video

In this paper, we describe a real-time 3D video system that is based on active stereo. Combining a NTSC standard camera/ projector equipment and a suitable color code, the geometric and photometric information of a scene is robustly obtained in 30fps. Our main motivation to develop this system is to create a platform for investigating the issues that will be posed by the next generation of digital video and how it can shape up new media.

Range-enhanced active foreground extraction

We describe a new technique that uses active scene illumination to perform foreground-background segmentation and recover partial HDR information. We explore the fact that relative tones can be recovered by varying illumination intensity, without knowing the camera response function. In our approach, the scene is illuminated with an uncalibrated projector and two images of the scene are captured under different illumination conditions. By taking advantage of the fact that the projector can be set up to illuminate only the foreground, we are able to distinguish the foreground from the background. The output of our system is a segmentation mask, together with a image with additional tonal information for the foreground pixels. As an application, we show how to produce spatially variant tone mapped images, where background and foreground receive different treatments. The segmentation and the visualization algorithms are implemented in real-time, and can be used to produce range-enhanced video sequences.

Actively Illuminated Objects using Graph-Cuts

This paper addresses the problem of foreground extraction using active illumination and graph-cut optimization. Our approach starts by detecting image regions that are likely to belong to foreground objects. These regions are constituted by pixels where the difference in luminance for two differently illuminated images is large. The foreground objects are segmented by graph-cut optimization using those regions as a seed and using a energy function based on probability distributions derived from both input images and their difference. Several light sources and different illumination schemes can be used to mark the foreground. Our method has only two scalar parameters which can be set once for a wide variety of scenes

Real-Time Video Processing for Multi-Object Chromatic Tracking.

This paper presents MOCT, a multi-object chromatic tracking technique for real-time natural video processing. Its main step is the MOCT localization algorithm, that performs local data evaluations in order to apply a multiple output parallel reduction operator to the image. The reduction operator is used to localize the positions of the object centroids, to compute the number of pixels occupied by an object and its bounding boxes, and to update object trajectories in image space. The operator is analyzed using three different computation layouts and tested over several reduction factors.

Real-time 3D video

The next generation of digital video has to bring innovation related to revolutionary aspects. This will be the result of incorporating new types of data into the media. Depth information is certainly the most natural candidate of data type to augment digital video. Not only it is consonant with the human perceptual system, but it also facilitates scene analysis by computers to extract information at higher levels.