Fuzzy Graph Tracking

. This paper is concerned with the problem of tracking groups of extended features in image sequences. A graph based image structure tracker is presented. The tracking is based on matching fuzzy features and feature relationships. Edges are extracted from subsequent frames. Fuzzy relational graphs are built from each image. The nodes of these graphs represent edge segments while the arcs code the relations among these segments. For each segment in one frame a set of potential assignments in the next frame is determined. Out of this assignment pool the global correspondence with highest similarity of features and feature relationships is calculated. Tracking is reduced to finding sets of mutually compatible nodes in graphs constructed from subsequent frames. The method is able to give guidelines for the correction of segmentation errors in particular frames. Keywords: tracking, fuzzy relations, graphs 1 Introduction Tracking is difficult because it requires to solve the correspondence..

Optimal Camera Parameter Selection for State Estimation with Applications in Object Recognition

Abstract In this paper we introduce a formalism for optimal camera parameter selection for iterative state estimation. We consider a framework based on Shannon’s information theory and select the camera parameters that maximize the mutual information, i.e. the information that the captured image conveys about the true state of the system. The technique explicitly takes into account the a priori probability governing the computation of the mutual information. Thus, a sequential decision process can be formed by treating the a posteriori probability at the current time step in the decision process as the a priori probability for the next time step. The convergence of the decision process can be proven. We demonstrate the benefits of our approach using an active object recognition scenario. The results show that the sequential decision process outperforms a random strategy, both in the sense of recognition rate and number of views necessary to return a decision.

Classifier Independent Viewpoint Selection for 3-D Object Recognition

Abstract 3–D object recognition has been tackled by passive approaches in the past. This means that based on one image a decision for a certain class and pose must be made or the image must be rejected. This neglects the fact that some other views might exist, which allow for a more reliable classification. This situation especially arises if certain views of or between objects are ambiguous. In this paper we present a classifier independent approach to solve the problem of choosing optimals views (viewpoint selection) for 3–D object recognition. We formally define the selection of additional views as an optimization problem and we show how to use reinforcement learning for continuous viewpoint training and selection without user interaction. The main focus lies on the automatic configuration of the system, the classifier independent approach and the continuous representation of the 3–D space. The experimental results show that this approach is well suited to distinguish and recognize similar looking objects in 3–D by taking a minimum amount of views.

On Optimal Camera Parameter Selection in Kalman Filter Based Object Tracking

In this paper we present an information theoretic framework that provides an optimality criterion for the selection of the best sensor data regarding state estimation of dynamic system. One relevant application in practice is tracking a moving object in 3--D using multiple sensors. Our approach extends previous and similar work in the area of active object recognition, i.e. state estimation of static systems. We derive a theoretically well founded metric based on the conditional entropy that is also close to intuition: select those camera parameters that result in sensor data containing most information for the following state estimation