16474707
Reconstructing Polyhedral Models of Architectural Scenes from Photographs
This paper presents a new image-based modeling method that facilitates the recovery of accurate polyhedral models of architectural scenes. The method is particularly effective because it exploits many of the constraints that are characteristic of architectural scenes. This work is placed in the context of the FaCade project, whose goal is to use images to produce photo-realistic novel views of architectural scenes.
15498523
A framework for sensor planning and control with applications to vision guided multi-robot systems
The paper presents an approach to the problem of controlling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configuration is particularly important in the context of teams equipped with vision sensors since most estimation schemes of interest will involve some form of triangulation. We provide a theoretical framework for tackling the sensor planning problem and a practical computational strategy, inspired by work on particle filtering, for implementing the approach. The ideas have been demonstrated both in simulation and on actual robotic platforms. The results indicate that the framework is able to solve fairly difficult sensor planning problems online without requiring excessive amounts of computational resources.
2609415
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
We present a new approach for modeling and rendering existing architectural scenes from a sparse set of still photographs. Our modeling approach, which combines both geometry-based and imagebased techniques, has two components. The first component is a photogrammetricmodeling method which facilitates the recovery of the basic geometry of the photographed scene. Our photogrammetric modeling approach is effective, convenient, and robust because it exploits the constraints that are characteristic of architectural scenes. The second component is a model-based stereo algorithm, which recovers how the real scene deviates from the basic model. By making use of the model, our stereo technique robustly recovers accurate depth from widely-spaced image pairs. Consequently, our approach can model large architectural environments with far fewer photographs than current image-based modeling approaches. For producing renderings, we present view-dependent texture mapping, a method of compositing multiple views of a scene that better simulates geometric detail on basic models. Our approach can be used to recover models for use in either geometry-based or image-based rendering systems. We present results that demonstrate our approach’s ability to create realistic renderings of architectural scenes from viewpoints far from the original photographs. CR Descriptors: I.2.10 [Artificial Intelligence]: Vision and Scene Understanding Modeling and recovery of physical attributes; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism Color, shading, shadowing, and texture I.4.8 [Image Processing]: Scene Analysis Stereo; J.6 [Computer-Aided Engineering]: Computer-aided design (CAD).
626048
Corresponding Articular Cartilage Thickness Measurements in the Knee Joint by Modelling the Underlying Bone
We present a method for corresponding and combining cartilage thickness readings from a population of patients using the underlying bone structure as a reference. Knee joint femoral bone and cartilage surfaces are constructed from a set of parallel slice segmentations of MR scans. Correspondence points across a population of bone surfaces are defined and refined by minimising an objective function based on the Minimum Description Length of the resulting statistical shape model. The optimised bone model defines a set of corresponding locations from which 3D measurements of the cartilage thickness can be taken and combined for a population of patients. Results are presented for a small group of patients demonstrating the feasibility and potential of the approach as a means of detecting sub-millimetre cartilage thickness changes due to disease progression.
6527681
The use of kernel principal component analysis to model data distributions
Abstract We describe the use of kernel principal component analysis (KPCA) to model data distributions in high-dimensional spaces. We show that a previous approach to representing non-linear data constraints using KPCA is not generally valid, and introduce a new ‘proximity to data’ measure that behaves correctly. We investigate the relation between this measure and the actual density for various low-dimensional data distributions. We demonstrate the effectiveness of the method by applying it to the higher-dimensional case of modelling an ensemble of images of handwritten digits, showing how it can be used to extract the digit information from noisy input images.
3869848
Modelling Facial Behaviours
We consider the problem of learning how a person’ s face behaves in a long video sequence, with the aim of synthesising convincing sequences demonstrating the same behaviours. We describe a novel approach to segment a sequence into short sections, each representing a distinct action (or a part of an action). These sections are grouped and a model of the variability of the action learnt. A variable length Markov model is trained on the sequence of such actions to learn the temporal relationships. The result is a system that can generate realistic sequences of an individual face.
1277199
Modeling and minimization of interconnect energy dissipation in nanometer technologies
As the technology sizes of semiconductor devices continue to decrease, the effect of nanometer technologies on interconnects, such as crosstalk glitches and timing variations, become more significant. In this paper, we study the effect of nanometer technologies on energy dissipation in interconnects. We propose a new power estimation technique which considers DSM effects, resulting in significantly more accurate energy dissipation estimates than transition-count based methods for on-chip interconnects. We also introduce an energy minimization technique which attempts to minimize large voltage swings across the cross-coupling capacitances between interconnects. Even though the number of transitions may increase, our method yields a decrease in power consumption of up to 50%.
18409496
Building optimal 2D statistical shape models
Statistical shape models are used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need, during training, to establish a dense correspondence across a training set of segmented shapes. We show that model construction can be treated as an optimisation problem, automating the process and guaranteeing the effectiveness of the resulting models. This is achieved by optimising an objective function with respect to the correspondence. We use an information theoretic objective function that directly promotes desirable features of the model. This is coupled with an effective method of manipulating correspondence, based on reparameterising each training shape, to build optimal statistical shape models. The method is evaluated on several training sets of shapes, showing that it constructs better models than alternative approaches. q 2003 Elsevier B.V. All rights reserved.