actions for 397 scenes. We next discover action-scene associations and demonstrate that scene categories can be well identified from their associated actions. Using discovered associations, we address a new task of predicting human actions for images of static scenes. We evaluate prediction of 23 and 38

In the current study, we explored observers ’ use of two distinct analyses for determining their direction of motion, or head-ing: a scene-based analysis and a motion-based analysis. In two ex-periments, subjects viewed sequentially presented, paired digitized images of real-world scenes and judged

We present a framework for automatically enhancing videos of a static scene using a few photographs of the same scene. For example, our system can transfer photographic qualities such as high resolution, high dynamic range and better lighting from the photographs to the video. Additionally

A well-known technique in high dynamic range (HDR) imaging is to take multiple photographs, each one with a different exposure time, and then combine them to produce an HDR image. Unless the scene is static and the camera position is fixed, this process creates the so-called “ghosting ” artifacts

We consider the registration of sequences of images where the observed scene is entirely non-rigid; for example a camera flying over water, a panning shot of a field of sunflowers in the wind, or footage of a crowd applauding at a sports event. In these cases, it is not possible to impose

these processes can be addressed independent from each other, we show that using the available data in exchange enables a more exact reconstruction of the static scene. A 6D data representation consisting of 3D Time-of-Flight (ToF) Sensor data and computed 3D velocities allows segmenting the scene into clusters

This paper describes an approach to obtain interactive recalculation of global illumination for scenes with small moving objects (with respect to the complete geometry), on a standard PC, using Density Estimation techniques. 1 Introduction and State of the Art 1.1

iii Mobile technology is improving in quality and capability faster now than ever before. When first introduced, cell phones were strictly used to make voice calls; now, they play satellite radio, MP3s, streaming television, have GPS and navigation capabilities, and have multi-megapixel video cameras. In the near future, cell phones will have programmable graphics processing units (GPU) that will allow users to play games similar to those currently available for top-of-the-line game consoles. Personal digital assistants enable users with full email, scheduling, and internet browsing capabilities in addition to those features offered on cell phones. Underlying all this mobile technology and entertainment is a battery whose technology has just barely tripled in the past 15 years, compared to available disk capacity that has increased over 1,000-fold. Ray tracing is a rendering technique used to generate photorealistic images that include reflections, refraction, participating media, and can fairly easily be extended to include photon mapping for indirect illumination and caustics. In recent years, ray tracing

FXPAL’s Pantheia system enables users to create virtual models by ‘marking up ’ a physical space with pre-printed visual markers. The meanings associated with the markers come from a markup language that enables the system to create models from a relatively sparse set of markers. This paper describes extensions to our markup language and system that support the creation of interactive virtual objects. Users place markers to define components such as doors and drawers with which an end user of the model can interact. Other interactive elements, such as controls for color changes or lighting choices, are also supported. Pantheia produced a model of a room with hinged doors, a cabinet with drawers, doors, and color options, and a railroad track. Index Terms — Interactive virtual content, marker-based model creation, semantic markers

Introduction In many object recognition applications the objects of interest are moving whereas the background is static or can be stabilized [1, 2]. Motion segmentation can enormously simplify, subsequent object recognition steps. Therefore, detecting and segmenting moving objects in a static scene