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检索条件"机构=Institute of Computer Graphics and Vision Graz University of Technology"

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Conservative learning for object detectors

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作者： Roth, Peter M. Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Computer Graphics and Vision Graz UConservative Learning for Object Detectorsniversity of Technology Graz Austria

ISBN: (纸本)9783540751700

In this chapter we will introduce a new effective framework for learning an object detector. The main idea is to minimize the manual effort when learning a classifier and to combine the power of a discriminative classifier with the robustness of a generative model. Starting with motion detection an initial set of positive examples is obtained by analyzing the geometry (aspect ratio) of the motion blobs. Using these samples a discriminative classifier is trained using an online version of AdaBoost. In fact, applying this classifier nearly all objects are detected but there is a great number of false positives. Thus, we apply a generative classifier to verify the obtained detections and to decide if a detected patch represents the object of interest or not. As we have a huge amount of data (video stream) we can be very conservative and use only patches for (positive or negative) updates if we are very confident about our decision. Applying this update rules, an incrementally better classifier is obtained without any user interaction. Moreover, an already trained classifier can be retrained online and can therefore easily be adapted to a completely different scene. We demonstrate the framework on different scenarios including pedestrian and car detection. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Adaptive boosting

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Interactive texture segmentation using random forests and total variation

Interactive texture segmentation using random forests and to...

引用

2009 20th British Machine vision Conference, BMVC 2009

作者： Santner, Jakob Unger, Markus Pock, Thomas Leistner, Christian Saffari, Amir Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)1901725391

Common methods for interactive texture segmentation rely on probability maps based on low dimensional features such as e.g. intensity or color, that are usually modeled using basic learning algorithms such as histograms or Gaussian Mixture Models. The use of low level features allows for fast generation of these hypotheses but limits applicability to a small class of images. We address this problem by learning complex descriptors with Random Forests and exploiting their inherent parallelism in a GPU implementation. The segmentation itself is based on a convex energy functional that uses weighted Total Variation regularization and a point-wise data term allowing for continuous foreground/background membership hypotheses. Its globally optimal solution is obtained by a fast primal-dual algorithm providing a reasonable convergence criterion. As a result, we present a versatile interactive texture segmentation framework. We show experiments with natural, artificial and medical data and demonstrate superior results compared to two recent approaches. © 2009. The copyright of this document resides with its authors.

关键词： Learning algorithms

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Playing it real again: A repeated evaluation of magic lens and static peephole interfaces in public space 13

Playing it real again: A repeated evaluation of magic lens a...

引用

15th International Conference on Human-computer Interaction with Mobile Devices and Services, MobileHCI 2013

作者： Grubert, Jens Schmalstieg, Dieter Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9781450322737

We repeated a study on the usage of a magic lens and a static peephole interface for playing a find-and-select game in a public space. While we reproduced the study setup and procedure the task was conducted in a public transportation stop with different characteristics. The results on usage duration and user preference were significantly different from those reported for previous conditions. We investigate possible causes, specifically the differences in the spatial characteristics and the social contexts in which the study took place. © 2013 ACM.

关键词： Augmented reality

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Façade segmentation in a multi-view scenario

Façade segmentation in a multi-view scenario

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2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission, 3DIMPVT 2011

作者： Recky, Michal Wendel, Andreas Leberl, Franz Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9780769543697

We examine a new method of façade segmentation in a multi-view scenario. A set of overlapping, thus redundant street-side images exists and each image shows multiple buildings. A semantic segmentation identifies primary areas in the image such as sky, ground, vegetation, and façade. Subsequently, repeated patterns are detected in image segments previous labeled as "façade areas" and are applied to separate specific facades from each other. Experimentation is based on an industrial street-view dataset from a moving car by well-designed, calibrated, automated cameras. High overlap images define a multi-view scenario. We achieve 97% pixel-wise segmentation effectiveness, outperforming current state-of-the-art methods. © 2011 IEEE.

关键词： Semantic Segmentation

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Automated walkable area segmentation from aerial images for evacuation simulation 1

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2nd International Conference on Geographical Information Systems Theory, Applications and Management, GISTAM 2016

作者： Schenk, Fabian Rüther, Matthias Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (数字)9783319626185

ISBN: (纸本)9783319626178

In this paper, we propose a novel, efficient and fast method to extract the walkable area from high-resolution aerial images for the purpose of computer-aided evacuation simulation for major public events. Compared to previous work, where authors only extracted roads and streets or solely focused on indoor scenarios, we present an approach to fully segment the walkable area of large outdoor environments. We address this challenge by modeling human movements in the terrain with a sophisticated seeded region growing algorithm (SRG), which utilizes digital surface models, true-orthophotos and inclination maps computed from aerial images. Further, we propose a novel annotation and scoring scheme especially developed for assessing the quality of the extracted evacuation maps. Finally, we present an extensive quantitative and qualitative evaluation, where we show the feasibility of our approach by evaluating different combinations of SRG methods and parameter settings on several real-world scenarios. © Springer International Publishing AG 2017.

关键词： Image segmentation

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On-line random naive bayes for tracking

On-line random naive bayes for tracking

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Godec, Martin Leistner, Christian Saffari, Amir Bischof, Horst Institute for Computer Vision and Graphics Graz University of Technology Graz Austria

ISBN: (纸本)9780769541099

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 on the principals of on-line Random Forests, we adapt the Random Naive Bayes classifier to the on-line domain. For that purpose, we propose to use on-line histograms as weak learners, which yield much better performance than simple decision stumps. Experimentally we show, that the approach is applicable to incremental learning on machine learning datasets. Additionally, we propose to use an iir filtering-like forgetting function for the weak learners to enable adaptivity and evaluate our classifier on the task of tracking by detection. © 2010 IEEE.

关键词： Decision trees

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InfoSeg: Unsupervised Semantic Image Segmentation with Mutual Information Maximization 43rd

InfoSeg: Unsupervised Semantic Image Segmentation with Mutua...

引用

43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021

作者： Harb, Robert Knöbelreiter, Patrick Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9783030926588

We propose a novel method for unsupervised semantic image segmentation based on mutual information maximization between local and global high-level image features. The core idea of our work is to leverage recent progress in self-supervised image representation learning. Representation learning methods compute a single high-level feature capturing an entire image. In contrast, we compute multiple high-level features, each capturing image segments of one particular semantic class. To this end, we propose a novel two-step learning procedure comprising a segmentation and a mutual information maximization step. In the first step, we segment images based on local and global features. In the second step, we maximize the mutual information between local features and high-level features of their respective class. For training, we provide solely unlabeled images and start from random network initialization. For quantitative and qualitative evaluation, we use established benchmarks, and COCO-Persons, whereby we introduce the latter in this paper as a challenging novel benchmark. InfoSeg significantly outperforms the current state-of-the-art, e.g., we achieve a relative increase of 26 % in the Pixel Accuracy metric on the COCO-Stuff dataset. © 2021, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Histogram of oriented cameras - A new descriptor for visual SLAM in dynamic environments

Histogram of oriented cameras - A new descriptor for visual ...

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2010 21st British Machine vision Conference, BMVC 2010

作者： Pirker, Katrin Rüther, Matthias Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)1901725405

Simultaneous localization and mapping (SLAM) is a basic prerequisite in autonomous mobile robotics. Most existing visual SLAM approaches either assume a static environment, or simply 'forget' old parts of the map to cope with map size constraints and scene dynamics. We present a novel map representation for sparse visual features. A new 3D point descriptor called Histogram of Oriented Cameras (HOC) encodes anisotropic spatial visibility information and the importance of each three-dimensional landmark. Each feature holds and updates a histogram of the poses of observing cameras. It is hereby able to estimate its probability of occlusion and importance for localization from a given viewpoint. In a series of simulated and real-world experiments we prove that the proposed descriptor allows to cope with dynamic changes in the map, improves localization accuracy and enables reasonable control of the map size. © 2010. The copyright of this document resides with its authors.

关键词： Cameras

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Pose estimation of known objects by efficient silhouette matching

Pose estimation of known objects by efficient silhouette mat...

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International Conference on Pattern Recognition

作者： Reinbacher, Christian Rüther, Matthias Bischof, Horst Institute for Computer Vision and Graphics Graz University of Technology Graz Austria

ISBN: (纸本)9780769541099

Pose estimation is essential for automated handling of objects. In many computer vision applications only the object silhouettes can be acquired reliably, because untextured or slightly transparent objects do not allow for other features. We propose a pose estimation method for known objects, based on hierarchical silhouette matching and unsupervised clustering. The search hierarchy is created by an unsupervised clustering scheme, which makes the method less sensitive to parametrization, and still exploits spatial neighborhood for efficient hierarchy generation. Our evaluation shows a decrease in matching time of 80% compared to an exhaustive matching and scalability to large models. © 2010 IEEE.

关键词： Pattern recognition

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Combining edge images and depth maps for robust visual odometry 28

Combining edge images and depth maps for robust visual odome...

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28th British Machine vision Conference, BMVC 2017

作者： Schenk, Fabian Fraundorfer, Friedrich Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)190172560X

In this work, we propose a robust visual odometry system for RGBD sensors. The core of our method is a combination of edge images and depth maps for joint camera pose estimation. Edges are more stable under varying lighting conditions than raw intensity values and depth maps further add stability in poorly textured environments. This leads to higher accuracy and robustness in scenes, where feature-or photoconsistency-based approaches often fail. We demonstrate the robustness of our method under challenging conditions on various real-world scenarios recorded with our own RGBD sensor. Further, we evaluate on several sequences from standard benchmark datasets covering a wide variety of scenes and camera motions. The results show that our method performs best in terms of trajectory accuracy for most of the sequences indicating that the chosen combination of edge and depth terms in the cost function is suitable for a multitude of scenes. © 2017. The copyright of this document resides with its authors.

关键词： Cost functions

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