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3rd International Conference on Computer Vision Theory and Applications

作者： Bulatov, Dimitri Research Institute for Optronics and Pattern Recognition Germany

ISBN: (纸本)9789898111210

This paper presents two algorithms needed to perform a dense 3D-reconstruction from video streams recorded with uncalibrated cameras. Our algorithm for camera self-calibration makes extensive use of the constant focal length. Furthermore, a fast dense reconstruction can be performed by fusion of tessellations obtained from different sub-sequences (LIFT). Moreover, we will present our system for performing the reconstruction in a projective coordinate system. Since critical motions are common in the majority of practical situations, care has been taken to recognize and deal with them.

关键词： calibration dense reconstruction Euclidean reconstruction

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Consideration of uncertainty in computer vision: Necessity and chance

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pattern recognition and Image Analysis 2008年第2期18卷 216-221页

作者： Meidow, J. Research Institute for Optronics and Pattern Recognition Gutleuthausstr. 1 76275 Ettlingen Germany

Observations and decisions in computer vision are inherently uncertain. The rigorous treatment of uncertainty has therefore received a lot of attention, since it not only improves the results compared to ad hoc methods but also makes the results more explainable. In this paper, the usefulness of stochastic approaches will be demonstrated by example with selected problems. These are given in the context of optimal estimation, self-diagnostics, and performance evaluation and cover all steps of the reasoning chain. The removal or interpretation of unexplainable thresholds and tuning parameters will be discussed for typical tasks in feature extraction, object reconstruction, and object classification. © 2008 Pleiades Publishing, Ltd.

关键词： Computer vision

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Automatic target recognition in SAR images based on a SVM classification scheme

Automatic target recognition in SAR images based on a SVM cl...

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8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA)

作者： Middelmann, Wolfgang Ebert, Alfons Thoennessen, Ulrich FGAN-FOM Research Institute for Optronics and Pattern Recognition Germany

ISBN: (纸本)9783540715900

The performance of classifiers is commonly evaluated by classification rate and false alarm rate (FAR). Many applications like traffic monitoring, surveillance and other security relevant tasks suffer from the problem balancing the performance criteria in an appropriate way. In this contribution, we propose a kernel classification scheme with high performance in discriminating classes and rejecting clutter objects. Especially, it determines a class membership assessment. The classification scheme consists of two kernel classification stages and a maximum decision module as combiner. For tests, we use targets taken from the MSTAR synthetic aperture radar (SAR) dataset and clutter objects extracted from SAR scenes by a screening process. The dependency on parameter variations is shown and receiver operator characteristic (ROC) curves are given. The results confirm the high classification performance at low FARs. The integration into an operational demonstration system is in progress.

关键词： Radar imaging

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Segmentation, classification, and pose estimation of military vehicles in low resolution laser radar images

Segmentation, classification, and pose estimation of militar...

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Conference on Laser Radar Technology and Applications X

作者： Neulist, J Armbruster, W FGAN-FOM Research Institute for Optronics and Pattern Recognition (Germany)

ISBN: (纸本)0819457760

Model-based object recognition in range imagery typically involves matching the image data to the expected model data for each feasible model and pose hypothesis. Since the matching procedure is computationally expensive, the key to efficient object recognition is the reduction of the set of feasible hypotheses. This is particularly important for military vehicles, which may consist of several large moving parts such as the hull, turret, and gun of a tank, and hence require an eight or higher dimensional pose space to be searched. The presented paper outlines techniques for reducing the set of feasible hypotheses based on an estimation of target dimensions and orientation. Furthermore, the presence of a turret and a main gun and their orientations are determined. The vehicle parts dimensions as well as their error estimates restrict the number of model hypotheses whereas the position and orientation estimates and their error bounds reduce the number of pose hypotheses needing to be verified. The techniques are applied to several hundred laser radar images of eight different military vehicles with various part classifications and orientations. On-target resolution in azimuth, elevation and range is about 30 cm. The range images contain up to 20% dropouts due to atmospheric absorption. Additionally some target retro-reflectors, produce outliers due to signal crosstalk. The presented algorithms are extremely robust with respect to these and other error sources. The hypothesis space for hull orientation is reduced to about 5 degrees as is the error for turret rotation and gun elevation, provided the main gun is visible.

关键词： laser radar classification pose estimation ATR

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A perturbation suppressing segmentation technique based on adaptive diffusion

A perturbation suppressing segmentation technique based on a...

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8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA)

作者： Middelmann, Wolfgang Ebert, Alfons Deissler, Tobias Thoennessen, Ulrich FGAN-FOM Research Institute for Optronics and Pattern Recognition Germany

ISBN: (纸本)9783540715900

Segmentation is a fundamental task in pattern recognition and basis for high level applications like scene reconstruction, change detection, or object classification. The performance of these tasks suffers from a distorted segmentation. In this contribution an adaptive diffusion-based segmentation method is proposed suppressing perturbations in the segmentation with focusing on small regions with high contrast to their surrounding. The algorithm determines in each step the diffusion tensor. It is re-weighted with respect to an assessment stage. A comparative study uses high-resolution remote sensing data.

关键词： Image segmentation

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Change detection in satellite images

Change detection in satellite images

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Conference on Signal Processing, Sensor Fusion, and Target recognition XIV

作者： Thönnessen, U Hofele, G Middelmann, W FGAN-FOM Research Institute for Optronics and Pattern Recognition (Germany)

ISBN: (纸本)0819457949

Change detection plays an important role in different military areas as strategic reconnaissance, verification of armament and disarmament control and damage assessment. It is the process of identifying differences in the state of an object or phenomenon by observing it at different times. The availability of spaceborne reconnaissance systems with high spatial resolution, multi spectral capabilities, and short revisit times offer new perspectives for change detection. Before performing any kind of change detection it is necessary to separate changes of interest from changes caused by differences in data acquisition parameters. In these cases it is necessary to perform a pre-processing to correct the data or to normalize it. Image registration and, corresponding to this task, the ortho-rectification of the image data is a further prerequisite for change detection. If feasible, a 1-to-1 geometric correspondence should be aspired for. Change detection on an iconic level with a succeeding interpretation of the changes by the observer is often proposed;nevertheless an automatic knowledge-based analysis delivering the interpretation of the changes on a semantic level should be the aim of the future. We present first results of change detection on a structural level concerning urban areas. After preprocessing, the images are segmented in areas of interest and structural analysis is applied to these regions to extract descriptions of urban infrastructure like buildings, roads and tanks of refineries. These descriptions are matched to detect changes and similarities.

关键词： change detection structural object description assignment registration classification segmentation

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Metric reconstruction from video sequences

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pattern recognition and Image Analysis 2008年第2期18卷 300-308页

作者： Bulatov, D. FGAN-FOM Research Institute for Optronics and Pattern Recognition Gutleuthausstr. 1 76275 Ettlingen Germany

This paper is dedicated to modifying some steps needed to generate a dense 3D-reconstruction from a video stream. Since critical motions are common in the majority of practical situations, care was taken in our work to deal with some of these motions and "stitch" projective reconstructions from different coordinate systems. Moreover, the demand for real-time-near results in these applications is huge, so the system must be able to carry out a high-quality reconstruction in the shortest time. Finally, a new calibration algorithm will be presented and a hint will be given how a fast dense reconstruction can be obtained. © 2008 Pleiades Publishing, Ltd.

关键词： Image reconstruction

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Bayesian hypothesis generation and verification

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pattern recognition and Image Analysis 2008年第2期18卷 269-274页

作者： Armbruster, W. FGAN-FOM Research Institute for Optronics and Pattern Recognition Gutleuthausstr. 1 76275 Ettlingen Germany

Regarding computer vision as optimal decision making under uncertainty, a new optimization paradigm is introduced, namely, maximizing the product of the likelihood function and the posterior distribution on scene hypotheses given the results of feature extraction. Essentially this approach is a Bayesian formulation of hypothesis generation and verification. The approach is illustrated for model-based object recognition in range imagery, showing how segmentation results can optimally be incorporated into model matching. Several new match criteria for model based object recognition in range imagery are deduced from the theory. © 2008 Pleiades Publishing, Ltd.

关键词： Object recognition

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Detection and tracking of objects with direct integration of perception and expectation

Detection and tracking of objects with direct integration of...

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2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

作者： Jüngling, Kai Arens, Michael Research Institute for Optronics and Pattern Recognition Ettlingen Germany

ISBN: (纸本)9781424444427

One of the main challenges in video-based multi-target tracking is the consistent maintenance of object identities over time. We present a novel approach to that challenge that integrates tracking and detection in a single process. We thereby inherently solve the identity problem and gain additional stability of the object detection performance. For that purpose, we extend a state-of-the-art local-feature based object detector by integrating expectations resulting from tracking directly into the detection procedure on the level of features. By that combination of newly gathered and expected local features we are able to directly integrate new data-evidence with object knowledge collected in the past without changing the detection approach itself. Since our tracking approach is solely based on local features, without employing other features like color or shape, it works independently of underlying video-data characteristics and preserves the general applicability independent of object-class specifics. ©2009 IEEE.

关键词： Object detection

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Classification with controlled robustness in high-resolution SAR data

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25th pattern recognition Symposium of the German-Association-for-pattern-recognition

作者： Middelmann, W FGAN-FOM Research Institute for Optronics and Pattern Recognition Germany

ISBN: (纸本)3540408614

Ground target classification in high-resolution SAR data has become increasingly important over the years. Kernel machines like the Support Vector Machine (SVM) and the Relevance Vector Machine (RVM) afford a great chance to solve this problem. But it is not possible to customize these kernel machines. Therefore the main objective of this work has been the development of a mechanism that controls the classification quality versus the computational effort. The investigations have been carried out with usage of the MSTAR public target dataset. The result of this work is an extended RVM, the RVMG. A single parameter is controlling the robustness of the system. The spectrum varies from a machine 15 times faster and of 10% lower quality than the SVM, goes to a 5 times faster and equal quality machine, and ends with a machine a little bit faster than the SVM and of better quality than the Lagrangian Support Vector Machine (***).

关键词： Support vector machines

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