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检索条件"机构=Multimedia Computing and Computer Vision Lab"

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Improving VLAD: Hierarchical coding and a refined local coordinate system

Improving VLAD: Hierarchical coding and a refined local coor...

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作者： Eggert, Christian Romberg, Stefan Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Germany

ISBN: (纸本)9781479957514

The enormous growth of image databases calls for new techniques for fast and effective image search that scales with millions of images. Most importantly, the setting requires a compact but also descriptive image signature. Recently, the vector of aggregated local descriptors (VLAD) [1] has received much attention in large-scale image retrieval. In this paper we present two modifications for VLAD which improve the retrieval performance of the signature. © 2014 IEEE.

关键词： Image retrieval

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Pose estimation for deriving kinematic parameters of competitive swimmers

Pose estimation for deriving kinematic parameters of competi...

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computer vision Applications in Sports 2017

作者： Zecha, Dan Eggert, Christian Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Germany

In the field of competitive swimming a quantitative evaluation of kinematic parameters is a valuable tool for coaches but also a labor intensive task. We present a system which is able to automate the extraction of many kinematic parameters such as stroke frequency, kick rates and stroke-specific intra-cyclic parameters from video footage of an athlete. While this task can in principle be solved by human pose estimation, the problem is exacerbated by permanently changing self-occlusion and severe noise caused by air bubbles, splashes, light reflection and light refraction. Current approaches for pose estimation are unable to provide the necessary localization precision under these conditions in order to enable accurate estimates of all desired kinematic parameters. In this paper we reduce the problem of kinematic parameter derivation to detecting key frames with a deep neural network human pose estimator. We show that we can correctly detect key frames with a precision which is on par with the human annotation performance. From the correctly located key frames, aforementioned parameters can be successfully inferred. © 2017, Society for Imaging Science and Technology.

关键词： Deep neural networks

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Robust feature bundling

Robust feature bundling

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13th Pacific-Rim Conference on multimedia, PCM 2012

作者： Romberg, Stefan August, Moritz Ries, Christian X. Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Germany

ISBN: (纸本)9783642347771

In this work we present a feature bundling technique that aggregates individual local features with features from their spatial neighborhood into bundles. The resulting bundles carry more information of the underlying image content than single visual words. As in practice an exact search for such bundles is infeasible, we employ a robust approximate similarity search with min-hashing in order to retrieve images containing similar bundles. We demonstrate the benefits of these bundles for small object retrieval, i.e. logo recognition, and generic image retrieval. Multiple bundling strategies are explored and thoroughly evaluated on three different datasets. © 2012 Springer-Verlag.

关键词： Image retrieval

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Bundle min-hashing for logo recognition 13

Bundle min-hashing for logo recognition

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3rd ACM International Conference on multimedia Retrieval, ICMR 2013

作者： Romberg, Stefan Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Augsburg Germany

ISBN: (纸本)9781450320337

We present a scalable logo recognition technique based on feature bundling. Individual local features are aggregated with features from their spatial neighborhood into bundles. These bundles carry more information about the image content than single visual words. The recognition of logos in novel images is then performed by querying a database of reference images. We further propose a novel WGC-constrained RANSAC and a technique that boosts recall for object retrieval by synthesizing images from original query or reference images. We demonstrate the benefits of these techniques for both small object retrieval and logo recognition. Our logo recognition system clearly outperforms the current state-of-the-art with a recall of 83% at a precision of 99%. © 2013 ACM.

关键词： Query processing

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A closer look: Small object detection in faster R-CNN

A closer look: Small object detection in faster R-CNN

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IEEE International Conference on multimedia and Expo (ICME)

作者： Christian Eggert Stephan Brehm Anton Winschel Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (纸本)9781509060689

Faster R-CNN is a well-known approach for object detection which combines the generation of region proposals and their classification into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by the weak performance of Faster R-CNN on small object instances, we perform a detailed examination of both the proposal and the classification stage, examining their behavior for a wide range of object sizes. Additionally, we look at the influence of feature map resolution on the performance of those stages. We introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the Flicker data set improving the detection performance on small object instances.

关键词： Proposals Companies Object detection Feature extraction Pipelines Image resolution Barium

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Norm-Induced Entropies for Decision Forests

Norm-Induced Entropies for Decision Forests

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IEEE Workshop on Applications of computer vision (WACV)

作者： Christoph Lassner Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (纸本)9781479966844

The entropy measurement function is a central element of decision forest induction. The Shannon entropy and other generalized entropies such as the Renyi and Tsallis entropy are designed to fulfill the Khinchin-Shannon axioms. Whereas these axioms are appropriate for physical systems, they do not necessarily model well the artificial system of decision forest induction. In this paper, we show that when omitting two of the four axioms, every norm induces an entropy function. The remaining two axioms are sufficient to describe the requirements for an entropy function in the decision forest context. Furthermore, we introduce and analyze the p-norm-induced entropy, show relations to existing entropies and the relation to various heuristics that are commonly used for decision forest training. In experiments with classification, regression and the recently introduced Hough forests, we show how the discrete and differential form of the new entropy can be used for forest induction and how the functions can simply be fine tuned. The experiments indicate that the impact of the entropy function is limited, however can be a simple and useful post-processing step for optimizing decision forests for high performance applications.

关键词： Entropy Training Context Vegetation Vectors computer vision Equations

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IMPROVING VLAD: HIERARCHICAL CODING AND A REFINED LOCAL COORDINATE SYSTEM

IMPROVING VLAD: HIERARCHICAL CODING AND A REFINED LOCAL COOR...

引用

IEEE International Conference on Image Processing

作者： Christian Eggert Stefan Romberg Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (纸本)9781479957521

关键词： VLAD Image retrieval Compact signatures LCS

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Key-Pose Prediction in Cyclic Human Motion

Key-Pose Prediction in Cyclic Human Motion

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IEEE Workshop on Applications of computer vision (WACV)

作者： Dan Zecha Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

In this paper we study the problem of estimating inner cyclic time intervals within repetitive motion sequences of top-class swimmers in a swimming channel. Interval limits are given by temporal occurrences of key-poses, i.e. distinctive postures of the body. A key-pose is defined by means of only one or two specific features of the complete posture. It is often difficult to detect such subtle features directly. We therefore propose the following method: Given that we observe the swimmer from the side, we build a pictorial structure of pose lets to robustly identify random support poses within the regular motion of a swimmer. We formulate a maximum likelihood model which predicts a key-pose given the occurrences of multiple support poses within one stroke. The maximum likelihood can be extended with prior knowledge about the temporal location of a key-pose in order to improve the prediction recall. We experimentally show that our models reliably and robustly detect key-poses with a high precision and that their performance can be improved by extending the framework with additional camera views.

关键词： Detectors Time series analysis Joints Mathematical model Cameras Equations Feature extraction

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PARTIAL CONTOUR MATCHING FOR DOCUMENT PIECES WITH CONTENT-BASED PRIOR

PARTIAL CONTOUR MATCHING FOR DOCUMENT PIECES WITH CONTENT-BA...

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IEEE International Conference on multimedia and Expo

作者： Fabian Richter Christian X. Ries Stefan Romberg Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (纸本)9781479947607

In this paper we present a method for aligning shredded document pieces based on outer contours and content-based prior information. Our approach relies on domain-specific knowledge that document pieces must complement each other when aligned correctly. Building on this intuition we propose a variant of MSAC (M-estimator SAmple Consensus) to estimate an hypothesis that recovers the spatial relationship between pairs of pieces. To do so we first approximate their boundaries by polygons from which we define consensus sets between fragments. Each consensus set provides multiple hypotheses for aligning one piece onto the other. An optimal hypothesis is identified by applying a two-stage procedure in which we discard locally inconsistent hypotheses before verifying the remainder for global consistency.

关键词： Document analysis MSAC Partial contour matching

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Robust Estimation of Flight Parameters for SKI Jumpers

Robust Estimation of Flight Parameters for SKI Jumpers

引用

IEEE International Conference on multimedia and Expo Workshops (ICMEW)

作者： Katja Ludwig Moritz Einfalt Rainer Lienhart Multimedia Computing and Computer Vision Lab University of Augsburg

ISBN: (数字)9781728114859

ISBN: (纸本)9781728114866

This paper presents a model that robustly estimates important flight parameters for ski jumpers during their flight phase based on several camera views from the side along the jumpers' typical flight trajectories. A convolutional neural network for pose estimation, but also trained to detect skis, serves as a base model. It identifies 98.0% of the relevant flight parameters correctly within an angle threshold of 5 degrees, improving by 11.6% over previous work. In postprocessing, a pose checker first removes all wrong poses by using comparisons of distances and relative positions of the detected keypoints. A second step executes two RANSAC variants. One robustly estimates the average pose and another one the average pose angles. This model lifts the detection performance to 99.3% of the relevant flight parameters within a threshold of 5 degrees.

关键词： Cameras Computational modeling Hip Pose estimation Videos Principal component analysis Head

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