Manfred Prantl

Appearance-based active object recognition

We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to improve the classification result obtained from a single view. The approach uses an appearance-based object representation, namely the parametric eigenspace, and augments it by probability distributions. This enables us to cope with possible variations in the input images due to errors in the pre-processing chain or changing imaging conditions. Furthermore, the use of probability distributions gives us a gauge to perform view planning. Multiple observations lead to a significant increase in recognition rate. Action planning is shown to be of great use in reducing the number of images necessary to achieve a certain recognition performance when compared to a random strategy. q 2000 Elsevier Science B.V. All rights reserved.

A comparison of probabilistic, possibilistic and evidence theoretic fusion schemes for active object recognition

Abstract.One major goal of active object recognition systems is to extract useful information from multiple measurements. We compare three frameworks for information fusion and view-planning using different uncertainty calculi: probability theory, possibility theory and Dempster-Shafer theory of evidence. The system dynamically repositions the camera to capture additional views in order to improve the classification result obtained from a single view. The active recognition problem can be tackled successfully by all the considered approaches with sometimes only slight differences in performance. Extensive experiments confirm that recognition rates can be improved considerably by performing active steps. Random selection of the next action is much less efficient than planning, both in recognition rate and in the average number of steps required for recognition. As long as the rate of wrong object-pose classifications stays low the probabilistic implementation always outperforms the other approaches. If the outlier rate increases averaging fusion schemes outperform conjunctive approaches for information integration. We use an appearance based object representation, namely the parametric eigenspace, but the planning algorithm is actually independent of the details of the specific object recognition environment.

Active Object Recognition in Parametric Eigenspace

We present an efficient method within an active vision framework for recognizing objects which are ambiguous from certain viewpoints. The system is allowed to reposition the camera to capture additional views and, therefore, to resolve the classification result obtained from a single view. The approach uses an appearance based object representation, namely the parametric eigenspace, and augments it by probability distributions. This captures possible variations in the input images due to errors in the pre-processing chain or the imaging system. Furthermore, the use of probability distributions gives us a gauge to view planning. View planning is shown to be of great use in reducing the number of images to be captured when compared to a random strategy.

Active fusion—a new method applied to remote sensing image interpretation

Abstract Todays computer vision applications often have to deal with multiple, uncertain and incomplete visual information. In this paper, we introduce a new method, termed “ active fusion ”, which provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable costs. The implementation of active fusion on the basis of probability theory, the Dempster-Shafer theory of evidence and fuzzy sets is discussed. In a sample experiment, active fusion using Bayesian networks is applied to agricultural field classification from multitemporal Landsat imagery. This experiment shows a significant reduction of the number of information sources required for a reliable decision.

Learning temporal context in active object recognition using Bayesian analysis

Active object recognition is a successful strategy to reduce the uncertainty of single view recognition, by planning sequences of views, actively obtaining these views, and integrating multiple recognition results. Understanding recognition as a sequential decision problem challenges the visual agent to select discriminative information sources. The presented system emphasizes the importance of temporal context in disambiguating initial object hypotheses, provides the corresponding theory for Bayesian fusion processes, and demonstrates its performance to be superior to alternative view planning schemes. Instance based learning proposed to estimate the control function enables then real-time processing with improved performance characteristics.

A Robust Affine Matching Algorithm Using an Exponentially Decreasing Distance Function

We describe a robust method for spatial registration, which relies on the coarse correspondence of structures extracted from images, avoiding the establishment of point correspondences. These structures (tokens) are points, chains, polygons and regions at the level of intermediate symbolic representation (ISR). The algorithm recovers conformal transformations (4 affine parameters), so that 2-dimensional scenes as well as planar structures in 3D scenes can be handled. The affine transformation between two different tokensets is found by minimization of an exponentially decreasing distance function. As long as the tokensets are kept sparse, the method is very robust against a broad variety of common disturbances (e.g. incomplete segmentations, missing tokens, partial overlap). The performance of the algorithm is demonstrated using simple 2D shapes, medical, and remote sensing satellite images. The complexity of the algorithm is quadratic on the number of affine parameters.

Affine Matching of Intermediate Symbolic Representations

Spatial registration of images, features and symbols is important for the comparison of these entities, as well as for the fusion of visual information gathered from diverse sources. We introduce a method for spatial registration, which relies on the coarse correspondence of structures extracted from the images, and which does not require the establishment of point correspondences. These structures (tokens) are points, chains, polygons and regions at the level of intermediate symbolic representation (ISR). The conformai (4 affine parameter) transformation is found by a combination of hierarchical hypothesize and test, stepwise refinement of parameters, and parameter variation avoiding local minima. Since belief values guide the probability that a certain token is selected for correspondence, and many-to-many correspondences are possible, the method is very robust against a broad variety of common disturbances like incomplete segmentations, missing tokens or partial overlap. This is demonstrated using synthetic test data as well as quite complicated multisource medical images. The establishment of this kind of spatial relationships between different ISR data sets is used as one module of a system for information fusion in image understanding.