Reconstruction of Articulated Objects from Point Correspondences in a Single Uncalibrated Image

This paper investigates the problem of recovering information about the configuration of an articulated object, such as a human figure, from point correspondences in a single image. Unlike previous approaches, the proposed reconstruction method does not assume that the imagery was acquired with a calibrated camera. An analysis is presented which demonstrates that there are a family of solutions to this reconstruction problem parameterized by a single variable. A simple and effective algorithm is proposed for recovering the entire set of solutions by considering the foreshortening of the segments of the model in the image. Results obtained by applying this algorithm to real images are presented

Solving the Graph Cut Problem via \u3cem\u3el\u3c/em\u3e1 Norm Minimization

Graph cuts have become an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields. In this paper, the graph cut problem is reformulated as an unconstrained l1 norm minimization. This l1 norm minimization can then be tackled by solving a sequence of sparse linear systems involving the Laplacian of the underlying graph. The proposed procedure exploits the structure of these linear systems and can be implemented effectively on modern parallel architectures. The paper describes an implementation of the algorithm on a GPU and discusses experimental results obtained by applying the procedure to graphs derived from image processing problems

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, λ. 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. 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 which generates multiple starting points for the optimizer by sampling the parameter space uniformly. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated

Solving Image Registration Problems Using Interior Point Methods

This paper describes a novel approach to recovering a parametric deformation that optimally registers one image to another. The method proceeds by constructing a global convex approximation to the match function which can be optimized using interior point methods. The paper also describes how one can exploit the structure of the resulting optimization problem to develop efficient and effective matching algorithms. Results obtained by applying the proposed scheme to a variety of images are presented

Camera Trajectory Estimation using Inertial Sensor Measurements and Structure fom Motion Results

This paper describes an approach to estimating the trajectory of a moving camera based on the measurements acquired with an inertial sensor and estimates obtained by applying a structure from motion algorithm to a small set of keyframes in the video sequence. The problem is formulated as an offline trajectory fitting task rather than an online integration problem. This approach avoids many of the issues usually associated with inertial estimation schemes. One of the main advantages of the proposed technique is that it can be applied in situations where approaches based on feature tracking would have significant difficulties. Results obtained by applying the procedure to extended sequences acquired with both conventional and omnidirectional cameras are presented

Sensor Planning and Control in a Dynamic Environment

This 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\u27 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. We extend our previous work by showing how modeled system dynamics and configuration space obstacles can be handled. These 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