It is well known that linear filters are not powerful enough for many low-level image processing tasks. But it is also very difficult to design robust non-linear filters that respond exclusively to features of interes...
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ISBN:
(纸本)9783540749332
It is well known that linear filters are not powerful enough for many low-level image processing tasks. But it is also very difficult to design robust non-linear filters that respond exclusively to features of interest and that are at the same time equivariant with respect to translation and rotation. this paper proposes a new class of rotation-equivariant non-linear filters that is based on the principle of group integration. these filters become efficiently computable by an iterative scheme based on repeated differentiation of products and summations of the intermediate results. Our experiments show that the proposed filter detects pollen porates with only half as many errors than alternative approaches, when high localization accuracy is required.
A method for exploiting the information in low-level image segmentations for the purpose of object recognition is presented. the key idea is to use a whole ensemble of segmentations per image, computed on different ra...
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ISBN:
(纸本)3540444122
A method for exploiting the information in low-level image segmentations for the purpose of object recognition is presented. the key idea is to use a whole ensemble of segmentations per image, computed on different random samples of image sites. Along the boundaries of those segmentations that are stable under the sampling process we extract strings of vectors that contain local image descriptors like shape, texture and intensities. Pairs of such strings are aligned, and based on the alignment scores a mixture model is trained which divides the segments in an image into fore- and background. Given such candidate foreground segments, we show that it is possible to build a state-of-the-art object recognition system that exhibits excellent performance on a standard benchmark database. this result shows that despite the inherent problems of low-level image segmentation in poor data conditions, segmentation can indeed be a valuable tool for object recognition in real-world images.
We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future,...
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ISBN:
(纸本)3540444122
We present a new method for planning the optimal next view for a probabilistic visual object tracking task. Our method uses a variable number of cameras, can plan an action sequence several time steps into the future, and allows for real-time usage due to a computation time which is linear both in the number of cameras and the number of time steps. the algorithm can also handle object loss in one, more or all cameras, interdependencies in the camera's information contribution, and variable action costs. We evaluate our method by comparing it to previous approaches with a prerecorded sequence of real world images.
Most face recognition systems are based on some form of batch learning. Online face recognition is not only more practical, it is also much more biologically plausible. Typical batch learners aim at minimizing both tr...
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ISBN:
(纸本)9783540749332
Most face recognition systems are based on some form of batch learning. Online face recognition is not only more practical, it is also much more biologically plausible. Typical batch learners aim at minimizing both training error and (a measure of) hypothesis complexity. We show that the same minimization can be done incrementally as long as some form of "scaffolding" is applied throughout the learning process. Scaffolding means: make the system learn from samples that are neither too easy nor too difficult at each step. We note that such learning behavior is also biologically plausible. Experiments using large sequences of facial images support the theoretical claims. the proposed method compares well with other, numerical calculus-based online learners.
Scene text detection has been studied extensively. Existing methods detect either words or text lines and use either word-level or line-level annotated data for training. In this paper, we propose a dual-task network ...
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ISBN:
(纸本)9781728188089
Scene text detection has been studied extensively. Existing methods detect either words or text lines and use either word-level or line-level annotated data for training. In this paper, we propose a dual-task network that can perform word-level and line-level text detection simultaneously and use training data of both levels of annotation to boost the performance. the dual-task network has two detection heads for word-level and line-level text detection, respectively. then we propose a mutual guidance scheme for the joint training of the two tasks with two modules: line filtering module utilizes the output feature map of the text line detector to filter out the non-text regions for the word detector, and word enhancing module provides prior positions of words for the text line detector depending on the output feature map of the word detector. Experimental results of word-level and line-level text detection demonstrate the effectiveness of the proposed dual-task network and mutual guidance scheme, and the results of our method are competitive with state-of-the-art methods.
this paper describes the development and system concept for an active and highly integrated, digitally controlled SAR system calibrator. A total of 16 active transponder and receiver systems and 17 receiver only syste...
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ISBN:
(纸本)0780390504
this paper describes the development and system concept for an active and highly integrated, digitally controlled SAR system calibrator. A total of 16 active transponder and receiver systems and 17 receiver only systems will be fabricated for a calibration campaign. In May 2006, the TerraSAR-X satellite will be launched. the calibration units serve for absolute radiometric calibration of the SAR image data. Additionally, they are equipped with an extra receiver path for two dimensional antenna patternrecognition of the satellite antenna. the calibrator is monitored by a dedicated digital control unit. For antenna pattern estimation, a two dimensional antenna patternrecognition algorithm is presented.
We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. the pose estimation without initial information is a challenging optimiza...
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ISBN:
(纸本)9783540749332
We present an approach for estimating the 3D position and in case of articulated objects also the joint configuration from segmented 2D images. the pose estimation without initial information is a challenging optimization problem in a high dimensional space and is essential for texture acquisition and initialization of model-based tracking algorithms. Our method is able to recognize the correct object in the case of multiple objects and estimates its pose with a high accuracy. the key component is a particle-based global optimization method that converges to the global minimum similar to simulated annealing. After detecting potential bounded subsets of the search space, the particles are divided into clusters and migrate to the most attractive cluster as the time increases. the performance of our approach is verified by means of real scenes and a quantative error analysis for image distortions. Our experiments include rigid bodies and full human bodies.
We present an approach to non-rigid object tracking designed to handle textured objects in crowded scenes captured by non-static cameras. For this purpose, groups of low-level features are combined into a model descri...
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ISBN:
(纸本)3540444122
We present an approach to non-rigid object tracking designed to handle textured objects in crowded scenes captured by non-static cameras. For this purpose, groups of low-level features are combined into a model describing boththe shape and the appearance of the object. this results in remarkable robustness to severe partial occlusions, since overlapping objects are unlikely to be indistinguishable in appearance, configuration and velocity all at the same time. the model is learnt incrementally and adapts to varying illumination conditions and target shape and appearance, and is thus applicable to any kind of object. Results on real-world sequences demonstrate the performance of the proposed tracker. the algorithm is implemented withthe aim of achieving near real-time performance.
Different from many gesture-based human-robot interaction applications, which focused on the recognition of the interactional or the pointing gestures, this paper proposes a vision-based method for manipulative gestur...
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ISBN:
(纸本)3540444122
Different from many gesture-based human-robot interaction applications, which focused on the recognition of the interactional or the pointing gestures, this paper proposes a vision-based method for manipulative gesture recognition aiming to achieve natural, proactive, and non-intrusive interaction between humans and robots. the main contributions of the paper are an object-centered scheme for the segmentation and characterization of hand trajectory information, the use of particle filtering methods for an action primitive spotting, and the tight coupling of bottom-up and top-down processing that realizes a task-driven attention filter for low-level recognition steps. In contrast to purely trajectory based techniques, the presented approach is called object-oriented w.r.t. two different aspects: it is object-centered in terms of trajectory features that are defined relative to an object, and it uses object-specific models for action primitives. the system has a two-layer structure recognizing boththe HMM-modeled manipulative primitives and the underlying task characterized by the manipulative primitive sequence. the proposed top-down and bottom-up mechanism between the two layers decreases the image processing load and improves the recognition rate.
the detection of moving objects is crucial for robot navigation and driver assistance systems. In this paper the detectability of moving objects is studied. To this end, image correspondences over two and three frames...
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ISBN:
(纸本)9783540749332
the detection of moving objects is crucial for robot navigation and driver assistance systems. In this paper the detectability of moving objects is studied. To this end, image correspondences over two and three frames are considered whereas the images are acquired by a moving monocular camera. the detection is based on the constraints linked to static 3D points. these constraints (epipolar, positive depth, positive height, and trifocal constraint) are discussed briefly, and an algorithm incorporating all of them is proposed. the individual constraints differ in their action depending on the motion of the object. thus, the detectability of a moving object is influenced by its motion. three types of motions are investigated: parallel, lateral, and circular motion. the study of the detection limits is applied to real imagery.
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