text: The problem of identifying the 3D pose of a known object from a given 2D image has important applications in Computer Vision. Our proposed method of registering a 3D model of a known object on a given 2D photo of the object has numerous advantages over existing methods. It does not require prior training, knowledge of the camera parameters, explicit point correspondences or matching features between the image and model. Unlike techniques that estimate a partial 3D pose (as in an overhead view of traffic or machine parts on a conveyor belt), our method estimates the complete 3D pose of the object. It works on a single static image from a given view under varying and unknown lighting conditions. For this purpose we derive a novel illumination-invariant distance measure between the 2D photo and projected 3D model, which is then minimised to find the best pose parameters. Results for vehicle pose detection in real photographs are presented.
  @cite_29: 
  @cite_25: The development and implementation of a line segment-based token tracker is described. Given a sequence of time-varying images, the goal is to track line segments corresponding to the edges extracted from the image being analysed. Two representations for the line segments are presented and discussed. An uncertainty analysis on the parameters of each representation is performed, and the appropriate representation for the tracking problem is then derived. A tracking approach is presented that combines prediction and matching steps. The prediction step is a Kalman filtering-based approach that is used to provide reasonable estimates of the region where the matching process has to seek for a possible match between tokens. Correspondence in the search area is done through the use of a similarity function based on Mahalanobis distance between carefully chosen attributes of the line segments. It is worthwhile to note that tokens as points of interest (corners, triple points) can also be considered without affecting deeply the algorithm. The efficiency of the proposed approach is illustrated in several experiments that have been carried out considering noisy synthetic data and real scenes obtained from the INRIA mobile robot.
  @cite_15: Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many "plausible" ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider "similar." For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in ℝn, learns a distance metric over ℝn that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.
summary: Distance metrics can be used to represent a distance between two data sets, and hence give a measure of their similarity. Therefore, distance metrics can be used to measure similarity between different 2D images, as well as 2D images and 2D projections of a 3D model. A basic distance metric would be the or the 2-norm @math . However, this has the disadvantage of being dependent on the scale of measurement. We use the @cite_29 for our work, which is a scale-invariant distance measure. It is used by @cite_15 for clustering. It is also used by Deriche and Faugeras @cite_25 to match line segments in a sequence of time varying images.