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.
3172134
VideoPlus: A Method for Capturing the Structure and Appearance of Immersive Environments
This paper describes an approach to capturing the appearance and structure of immersive environments based on the video imagery obtained with an omnidirectional camera system. The scheme proceeds by recovering the 3D positions of a set of point and line features in the world from image correspondences in a small set of key frames in the image sequence. Once the locations of these features have been recovered the position of the camera during every frame in the sequence can be determined by using these recovered features as fiducials and estimating camera pose based on the location of corresponding image features in each frame. The end result of the procedure is an omnidirectional video sequence where every frame is augmented with its pose with respect to an absolute reference frame and a 3D model of the environment composed of point and line features in the scene.By augmenting the video clip with pose information we provide the viewer with the ability to navigate the image sequence in new and interesting ways. More specifically the user can use the pose information to travel through the video sequence with a trajectory different from the one taken by the original camera operator. This freedom presents the end user with an opportunity to immerse themselves within a remote environment.
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.
8867506
Robust vision-based pose control
The problem of controlling the spatial position and orientation of a robotic platform based on the image data obtained from a video camera mounted on that platform is considered. More specifically, we propose control laws that will cause the robot to achieve and maintain a fixed position and orientation with respect to a set of feature points in the scene. We demonstrate analytically that the proposed control scheme is globally convergent even in the presence of large calibration errors in both the intrinsic parameters of the camera and in the extrinsic parameters which relate the frame of reference of the camera to the body frame of the robot platform which is being controlled. Furthermore, no a priori knowledge about the structure of the scene is assumed.
14456844
Exploration strategies for mobile robots
The problem of programming a robot to carry out a systematic exploration of its environment using realistic sensors is considered. The robot is modeled as a single point moving in a two-dimensional configuration space populated with visually opaque and transparent obstacles. The robot is equipped with proximity sensors, a vision-based recognition system, and a method of odometry, all of which have some uncertainty associated with their measurements. By using visually distinctive configurations of features in the world as natural landmarks, a series of local maps is constructed. The geometrical relationships between mutually visible landmarks are used to build a relational map from this collection of local maps. This representation forms the basis for a systematic exploration algorithm. The approach is implemented in simulation, and results are presented.<<ETX>>
1646981
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. 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, the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. This paper describes how the reconstruction problem can be framed as an optimization over a compact set with low dimension (no more than four). This optimization problem can be solved efficiently by coupling standard nonlinear optimization techniques with a multistart method. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated.
2293701
Automatically building appearance models from image sequences using salient features
Abstract We address the problem of automatically placing landmarks across an image sequence to define correspondences between frames. The marked up sequence is then used to build a statistical model of the appearance of the object within the sequence. We locate the most salient object features from within the first frame and attempt to track them throughout the sequence. Salient features are those which have a low probability of being misclassified as any other feature, and are therefore more likely to be robustly tracked throughout the sequence. The method automatically builds statistical models of the objects shape and the salient features appearance as the sequence is tracked. These models are used in subsequent frames to further improve the probability of finding accurate matches. Results are shown for several face image sequences. The quality of the model is comparable with that generated from hand labelled images.
16065751
Improving Appearance Model Matching Using Local Image Structure
We show how non-linear representations of local image structure can be used to improve the performance of model matching algorithms in medical image analysis tasks. Rather than represent the image structure using intensity values or gradients, we use measures that indicate the reliability of a set of local image feature detector outputs. These features are image edges, corners, and gradients. Feature detector outputs in flat, noisy regions tend to be ignored whereas those near strong structure are favoured. We demonstrate that combinations of these features give more accurate and reliable matching between models and new images than modelling image intensity alone. We also show that the approach is robust to non-linear changes in contrast, such as those found in multi-modal imaging.
6301159
The Image Fidelity Assessor
In this paper we describe the Image Fidelity Assessor (IFA), a model of the human visual system designed to evaluate perceived fidelity. In particular, we describe the structure of the model, present results from a contrast discrimination experiment used within the model, and demonstrate preliminary results of the model. The IFA is designed to be both physiologically and psychophysically plausible. It consists of a multichannel Gabor pyramid decomposition. The fidelity at each spatial location within each channel is evaluated based on psychometric functions. The psychometric functions were determined experimentally and describe the discrimination ability of the visual system as a function of spatial frequency, orientation, and adaptation level. Limited memory probability summation across the visual channels is used to obtain a measure of perceived image fidelity. Preliminary results of the IFA are consistent with human evaluation of image fidelity.
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.
35329802
Shape Discrimination in the Hippocampus Using an MDL Model
We extend recent work on building 3D statistical shape models, automatically, from sets of training shapes and describe an application in shape analysis. Using an existing measure of model quality, based on a minimum description length criterion, and an existing method of surface re-parameterisation, we introduce a new approach to model optimisation that is scalable, more accurate, and involves fewer parameters than previous methods. We use the new approach to build a model of the right hippocampus, using a training set of 82 shapes, manually segmented from 3D MR images of the brain. We compare the results with those obtained using another previously published method for building 3D models, and show that our approach results in a model that is significantly more specific, general, and compact. The two models are used to investigate the hypothesis that there are differences in hippocampal shape between age-matched schizophrenic and normal control subgroups within the training set. Linear discriminant analysis is used to find the combination of shape parameters that best separates the two subgroups. We perform an unbiased test that shows there is a statistically significant shape difference using either shape model, but that the difference is more significant using the model built using our approach. We show also that the difference between the two subgroups can be visualised as a mode of shape variation.