33520001
Active Appearance Models
We describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations in the model parameters and the induced image errors.
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.
35191926
Automatic Extraction of the Face Identity-Subspace
Facial variation divides into a number of functional subspaces, and ensemblespecific variation. An improved method of measuring these is presented, within the space defined by an Appearance Model. Initial estimates of the subspaces (lighting, pose, identity and expression) are obtained by Principal Components Analysis on appropriate groups of faces. An expectationmaximization algorithm is applied to image codings to maximise the probability of coding across these non-orthogonal subspaces. Ensemble specific variation is then removed by measuring the spatial predictability of the eigenvectors excluding those which are less predictable than the ensemble. These procedures significantly enhance identity recognition for a disjoint test set.
9944487
Constrained Active Appearance Models
Active Appearance Models (AAMs) have been shown to be useful for interpreting images of deformable objects. Here we place the AAM matching algorithm in a statistical framework, allowing extra constraints to be applied. This enables the models to be combined with other methods of object location. We demonstrate how user interaction can be used to guide the search and give results of experiments showing the effect of constraints on the performance of model matching.
17273589
Developing a Measure of Similarity between Pixel Signatures
Our previous work in computer-aided mammography has used scaleorientation pixel signatures that provide a rich description of local structure. We describe work using the transportation (`earth mover') algorithm to de ne a measure of similarity between two signatures that recognises similar structures whilst remaining robust to background variability and the presence of other structures. Speci cally, we investigate an un-normalised, normalised and novel best-partialmatching (BPM) approach to measuring this transportation distance and compare it with Euclidean distance. Receiver operating characteristic (ROC) methodology is used to compare performance and an example of clinical application is given. The BPM approach outperforms the other methods.
117576
Transforming pixel signatures into an improved metric space
Abstract We address the problem of using scale-orientation pixel signatures to characterise local structure in X-ray mammograms, though the method we report is of general application. When signatures are treated as vectors for statistical analysis, the Euclidean metric is not well behaved. We have previously described a Best Partial Match (BPM) metric that measures signature similarity more naturally, but at high computational cost. We present a method for transforming signatures into a new space in which Euclidean distance approximates BPM distance, allowing BPM distance to be estimated at low computational cost. The new space is constructed using multi-dimensional scaling. The nonlinear transformation between the old and new spaces is learned using support vector regression. We present experimental results for mammographic data.
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.
3993976
A Software Framework for Creating Patient Specific Geometric Models from Medical Imaging Data for Simulation Based Medical Planning of Vascular Surgery
The primary purpose of vascular surgery is to restore blood flow to organs and tissues. Current methods of vascular treatment planning rely solely on diagnostic and empirical data to guide the decision-making process. This paper details a simulation-based medical planning system for cardiovascular disease that use computational methods to evaluate alternative surgical options prior to treatment using patient-specific models of the vascular system. A software framework, called Geodesic, is mtroduced that reduces the time required to build patient-specific models from medical imaging data from several weeks to less than one day.
26633307
Reconstruction of linearly parameterized models from single images with a camera of unknown focal length
This paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, /spl lambda/. The reconstruction program takes as input a set of correspondences between features in the model and features in the image. From this information the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. We demonstrate that this reconstruction task can be framed as an unconstrained optimization problem involving a small number of variables, no more than four, regardless of the number of parameters in the dimension vector.
21050185
A Framework and Architecture for Multirobot Coordination
In this paper, we present a framework and the software architecture for the deployment of multiple autonomous robots in an unstructured and unknown environment with applications ranging from scouting and reconnaissance, to search and rescue and manipulation tasks. Our software framework provides the methodology and the tools that enable robots to exhibit deliberative and reactive behaviors in autonomous operation, to be reprogrammed by a human operator at run-time, and to learn and adapt to unstructured, dynamic environments and new tasks, while providing performance guarantees. We demonstrate the algorithms and software on an experimental testbed that involves a team of car-like robots using a single omnidirectional camera as a sensor without explicit use of odometry.
12495173
Interactions Between Learning and Evolution in Simulated Avian Communication
This paper presents a computational framework for studying the influence of learning on the evolution of avian communication. We conducted computer simulations for exploring the effects of different learning strategies on the evolution of bird song. Experimental results show the genetic assimilation of song repertoires as a consequence of interactions between learning and evolution.