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In this article, a method to accelerate the solution of multiple right‐hand side problems when using the adaptive multi‐preconditioned finite element tearing and interconnecting algorithm is presented. This is done by deflating the conjugate gradient algorithm by means of a coarse space, which is built by a simplification of the recently published RitzGenEO method. While the proposed method no longer...
This article presents a new method to recycle the solution space of an adaptive multipreconditioned finite element tearing and interconnecting algorithm in the case where the same operator is solved for multiple right‐hand sides like in linear structural dynamics. It accelerates the computation from the second time step on by applying a coarse space that is generated from Ritz approximations of local...
Randomized learning methods (i.e., Forests or Ferns) have shown excellent capabilities for various computer vision applications. However, it was shown that the tree structure in Forests can be replaced by even simpler structures, e.g., Random Naive Bayes classifiers, yielding similar performance. The goal of this paper is to benefit from these findings to develop an efficient on-line learner. Based...
In this paper, we introduce a fully autonomous vehicle classification system that continuously learns from largeamounts of unlabeled data. For that purpose, we proposea novel on-line co-training method based on visual and acoustic information. Our system does not need complicated microphone arrays or video calibration and automatically adapts to specific traffic scenes. These specialized detectors...
Recently, combining information from multiple cameras has shown to be very beneficial for object detection and tracking. In contrast, the goal of this work is to train detectors exploiting the vast amount of unlabeled data given by geometry information of a specific multiple camera setup. Starting from a small number of positive training samples, we apply a co-training strategy in order to generate...
A recent dominating trend in tracking called tracking-by-detection uses on-line classifiers in order to redetect objects over succeeding frames. Although these methods usually deliver excellent results and run in real-time they also tend to drift in case of wrong updates during the self-learning process. Recent approaches tackled this problem by formulating tracking-by-detection as either one-shot...
Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boosting and its variants are only able to solve binary tasks. In this paper, we present Online Multi-Class LPBoost (OMCLP) which is directly applicable to multi-class problems. From a theoretical point of view, our algorithm...
Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since they rely on self-updates of an on-line learning method. In contrast to previous work that tackled this problem by employing semi-supervised or multiple-instance learning, we show that augmenting an on-line learning method with complementary...
On-line boosting is one of the most successful on-line algorithms and thus applied in many computer vision applications. However, even though boosting, in general, is well known to be susceptible to class-label noise, on-line boosting is mostly applied to self-learning applications such as visual object tracking, where label-noise is an inherent problem. This paper studies the robustness of on-line...
Many semi-supervised learning algorithms only deal with binary classification. Their extension to the multi-class problem is usually obtained by repeatedly solving a set of binary problems. Additionally, many of these methods do not scale very well with respect to a large number of unlabeled samples, which limits their applications to large-scale problems with many classes and unlabeled samples. In...
Automatic detection of persons is an important application in visual surveillance. In general, state-of-the-art systems have two main disadvantages: First, usually a general detector has to be learned that is applicable to a wide range of scenes. Thus, the training is time-consuming and requires a huge amount of labeled data. Second, the data is usually processed centralized, which leads to a huge...
The required amount of labeled training data for object detection and classification is a major drawback of current methods. Combining labeled and unlabeled data via semi-supervised learning holds the promise to ease the tedious and time consuming labeling effort. This paper presents a novel semi-supervised learning method which combines the power of learned similarity functions and classifiers. The...
The recent development of distributed smart camera networks allows for automated multiple view processing. Quick and easy calibration of uncalibrated multiple camera setups is important for practical uses of such systems by non-experts and in temporary setups. In this paper we discuss options for calibration, illustrated with a basic two-camera setup where each camera is a smart camera mote with a...
In this article we present our software framework for embedded online data fusion, called I-SENSE. We discuss the fusion model and the decision modeling approach using support vector machines. Due to the system complexity and the genetic approach a data oriented model is introduced. The main focus of the article is targeted at our techniques for extracting features of acoustic-and visual-data. Experimental...
Object reacquisition or reidentification is the process of matching objects between images taken from separate cameras. In this paper, we present our work on feature based object reidentification performed on autonomous embedded smart cameras and applied to traffic scenarios. We present a novel approach based on PCA-SIFT features and a vocabulary tree. By building unique object signatures from visual...
In recent years many powerful computer vision algorithms have been invented, making automatic or semiautomatic solutions to many popular vision tasks, such as visual object recognition or camera calibration, possible. On the other hand embedded vision platforms and solutions such as smart cameras have successfully emerged, however, only offering limited computational and memory resources. The first...
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