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We present a photogrammetric system for on-line pose measurement of a robot. The system is based on photogrammetric measurement techniques, namely resection. We describe the theoretical foundations of our approach as well as early details of our implementation and hardware set-up. The results achieved are compared to those of a commercial ball-bar system.
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...
From the task of automatically reconstructing real world scenes using range images, the problem of planning the image acquisition arises. Although solutions for small objects in known environments are already available, these approaches lack scalability to large scenes and to a high number of degrees of freedom. In this paper, we present a new planning algorithm for initially unknown, large indoor...
With the services that autonomous robots are to provide becoming more demanding, the states that the robots have to estimate become more complex. In this paper, we develop and analyze a probabilistic, vision-based state estimation method for individual, autonomous robots. This method enables a team of mobile robots to estimate their joint positions in a known environment and track the positions of...
In the last few years the research in 3-D object recognition has focused more and more on active approaches. In contrast to the passive approaches of the past decades where a decision is based on one image, active techniques use more than one image from different viewpoints for the classification and localization of an object. In this context several tasks have to be solved. First, how to choose the...
Logistic regression is presumably the most popular representative of probabilistic discriminative classifiers. In this paper, a kernel variant of logistic regression is introduced as an iteratively re-weighted least-squares algorithm in kernel-induced feature spaces. This formulation allows us to apply highly efficient approximation methods that are capable of dealing with large-scale problems. For...
In this paper we present a new approach for the localization and classification of 2-D objects that are situated in heterogeneous background or are partially occluded. We use an appearance-based approach and model the local features derived from wavelet multiresolution analysis by statistical density functions. In addition to the object model we define a new model for the background and a function...
We consider the task of cell classification in fluorescent micrographs. We combine the use of Independent Component analysis as a preprocessing step and a Self-organizing Map for the resulting ICA feature space to classify image patches into cell and noncell images and to investigate the features of image patches in the vicinity of the classification border. We compare the classification performance...
In this paper we address the problem of model-based image segmentation by fitting deformable models to the image data. From uncertain a priori knowledge of the model parameters an initial probability distribution of the model edge in the image is obtained. From the vicinity of the surmised edge local statistics are learned for both sides of the edge. These local statistics provide locally adapted...
In this paper we present SIMBA, a content based image retrieval system performing queries based on image appearance. We consider absolute object positions irrelevant for image similarity here and therefore propose to use invariant features. Based on a general construction method (integration over the transformation group), we derive invariant feature histograms that catch different cues of image content:...
In this paper we propose a novel method for the construction of invariant textural features for grey scale images. The textural features are based on an averaging over the 2D Euclidean transformation group with relational kernels. They are invariant against 2D Euclidean motion and strictly increasing grey scale transformations. Beside other fields of texture analysis applications we consider texture...
By interferometric SAR measurements digital elevation models (DEM) of large areas can be acquired in a short time. Due to the sensitivity of the interferometric phase to noise, the accuracy of the DEM depends on the signal to noise ratio (SNR). Usually the disturbed elevation data are restored employing statistical modeling of sensor and scene. But in undulated terrain layover and shadowing phenomena...
We consider approaches to computer vision problems which require the minimization of a global energy functional over binary variables and take into account both local similarity and spatial context. The combinatorial nature of such problems has lead to the design of various approximation algorithms in the past which often involve tuning parameters and tend to get trapped in local minima. In...
We present a novel approach to the weighted graph-matching problem in computer vision, based on a convex relaxation of the underlying combinatorial optimization problem. The approach always computes a lower bound of the objective function, which is a favorable property in the context of exact search algorithms. Furthermore, no tuning parameters have to be selected by the user, due to the convexity...
This paper addresses the problem of learning shape models from examples. The contributions are twofold. First, a comparative study is performed of various methods for establishing shape correspondence - based on shape decomposition, feature selection and alignment. Various registration methods using polygonal and Fourier features are extended to deal with shapes at multiple scales and the importance...
In this paper, we present a new approach to scale-space which is derived from the 3D Laplace equation instead of the heat equation. The resulting lowpass and bandpass filters are discussed and they are related to the monogenic signal. As an application, we present a scale adaptive filtering which is used for denoising images. The adaptivity is based on the local energy of spherical quadrature filters...
Medial axis transform (MAT) is very sensitive to the noise, in the sense that, even if a shape is perturbed only slightly, the Hausdorff distance between the MATs of the original shape and the perturbed one may be large. But it turns out that MAT is stable, if we view this phenomenon with the one-sided Hausdorff distance, rather than with the two-sided Hausdorff distance. In this paper, we show that,...
We compute the range flow field, i.e. the 3D velocity field, of a moving deformable surface from a sequence of range data. This is done in a differential framework for which we derive a new constraint equation that can be evaluated directly on the sensor data grid. It is shown how 3D structure and intensity information can be used together in the estimation process. We then introduce a method to compute...
This paper describes a newalgorithm for illumination-invariant change detection that combines a simple multiplicative illumination model with decision theoretic approaches to change detection. The core of our algorithm is a new statistical test for linear dependence (colinearity) of vectors observed in noise. This criterion can be employed for a significance test, but a considerable improvement of...
Machine learning is a desirable property of computer vision systems. Especially in process monitoring knowledge of temporal context speeds up recognition. Moreover, memorizing earlier results allows to establish qualitative relations between the stages of a processes. In this contribution we present an architecture that learns different visual aspects of assemblies. It is organized hierarchically...
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