1367527
Space-Time Light Field Rendering
In this paper, we propose a novel framework called space-time light field rendering, which allows continuous exploration of a dynamic scene in both space and time. Compared to existing light field capture/rendering systems, it offers the capability of using unsynchronized video inputs and the added freedom of controlling the visualization in the temporal domain, such as smooth slow motion and temporal integration. In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate these software-synchronized images in the spatial domain to synthesize the final view. In addition, we introduce a very accurate and robust algorithm to estimate subframe temporal offsets among input video sequences. Experimental results from unsynchronized videos with or without time stamps show that our approach is capable of maintaining photorealistic quality from a variety of real scenes.
9370677
Towards space: time light field rendering
So far extending light field rendering to dynamic scenes has been trivially treated as the rendering of static light fields stacked in time. This type of approaches requires input video sequences in strict synchronization and allows only discrete exploration in the temporal domain determined by the capture rate. In this paper we propose a novel framework, space-time light field rendering, which allows continuous exploration of a dynamic scene in both spatial and temporal domain with unsynchronized input video sequences.In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate those software-synchronized images in the spatial domain to synthesize the final view. Our experimental results show that our approach is robust and capable of maintaining photo-realistic results.
19006969
Water drops on surfaces
We present a physically-based method to enforce contact angles at the intersection of fluid free surfaces and solid objects, allowing us to simulate a variety of small-scale fluid phenomena including water drops on surfaces. The heart of this technique is a virtual surface method, which modifies the level set distance field representing the fluid surface in order to maintain an appropriate contact angle. The surface tension that is calculated on the contact line between the solid surface and liquid surface can then capture all interfacial tensions, including liquid-solid, liquid-air and solid-air tensions. We use a simple dynamic contact angle model to select contact angles according to the solid material property, water history, and the fluid front's motion. Our algorithm robustly and accurately treats various drop shape deformations, and handles both flat and curved solid surfaces. Our results show that our algorithm is capable of realistically simulating several small-scale liquid phenomena such as beading and flattened drops, stretched and separating drops, suspended drops on curved surfaces, and capillary action.
14477369
Markov random field modeled range image segmentation
In this paper, range image segmentation is studied in the framework of the maximum a posteriori estimation and Markov random field modeling. A novel range image segmentation model is proposed. The model serves as an evaluator for a small number of segmentation candidates obtained through a fast edge detection algorithm. A local method is employed to search for the optimal segmentation from the candidates. Experimental results show that such combination of heuristics and model-based evaluation leads to a fast and accurate segmentation.
9426825
Adaptive presentation for effective Web-based learning of 3D content
In this paper we focus on incorporating adaptive presentation and 3D visualization into Web-based learning environments to enhance learners' learning outcomes, especially to facilitate learners' spatial reasoning on geometric topics in computer graphics. This system is called CooTutor (coordinate tutor). According to educational media studies, "Media and Method" are the main concerns of developing such a system. In CooTutor, interactive 3D media is used to present spatial relations effectively to the learners. Instructional methods and strategies are embedded in its adaptive mechanism by incorporating ITS (intelligent tutoring systems) techniques. Since geometric transformation is the example domain of our study, learners' spatial ability is considered as an index for adapting the presentation. To achieve better abstraction and flexibility, we have chosen to separate the concept sequencing from the underlying learning materials. We believe that Web-based learning could take more advantages of available computing power to enhance learning by realizing innovative instructional design, as in the case of 3D interactive presentation.
721002
Fast Relevance Discovery in Time Series
In this paper, we propose to model time series from a new angle: state transition points. When fluctuation of values in a time series crosses a certain point, it may trigger state transition in the system, which may lead to abrupt changes in many other time series. The concept of state transition points is essential in understanding the behavior of the time series and the behavior of the system. The new measure is robust and is capable of discovering correlations that Pearson's coefficient cannot reveal. We propose efficient algorithms to identify state transition points and to compute correlation between two time series. We also introduce some triangular inequalities to efficiently find highly correlated time series among many time series.
17761622
A consensus-based method for tracking: Modelling background scenario and foreground appearance
Modelling of the background (''uninteresting parts of the scene''), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model of the background, per pixel. SACON exploits both color and motion information to detect foreground objects. SACON can deal with complex background scenarios including nonstationary scenes (such as moving trees, rain, and fountains), moved/inserted background objects, slowly moving foreground objects, illumination changes etc. However, it is one thing to detect objects that are not likely to be part of the background; it is another task to track those objects. Sample consensus is again utilized to model the appearance of foreground objects to facilitate tracking. This appearance model is employed to segment and track people through occlusions. Experimental results from several video sequences validate the effectiveness of the proposed method.
16302121
Smooth Foreground-Background Segmentation for Video Processing
We propose an efficient way to account for spatial smoothness in foreground-background segmentation of video sequences. Most statistical background modeling techniques regard the pixels in an image as independent and disregard the fundamental concept of smoothness. In contrast, we model smoothness of the foreground and background with a Markov random field, in such a way that it can be globally optimized at video frame rate. As a background model, the mixture-of-Gaussian (MOG) model is adopted and enhanced with several improvements developed for other background models. Experimental results show that the MOG model is still competitive, and that segmentation with the smoothness prior outperforms other methods.