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HALF-SIFT: High-Accurate Localized Features for SIFT

HALF-SIFT: High-Accurate Localized Features for SIFT

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kai Cordes Oliver Muller Bodo Rosenhahn Jorn Ostermann Institut für Informationsverarbeitung (TNT) Leibniz Universität Hannover Hanover Germany

In this paper, the accuracy of feature points in images detected by the scale invariant feature transform (SIFT) is analyzed. It is shown that there is a systematic error in the feature point localization. The systematic error is caused by the improper subpel and subscale estimation, an interpolation with a parabolic function. To avoid the systematic error, the detection of high-accurate localized features (HALF) is proposed. We present two models for the localization of a feature point in the scale-space, a Gaussian and a Difference of Gaussians based model function. For evaluation, ground truth image data is synthesized to experimentally prove the systematic error of SIFT and to show that the error is eliminated using HALF. Experiments with natural image data show that the proposed methods increase the accuracy of the feature point positions by 13.9% using the Gaussian and by 15.6% using the Difference of Gaussians model.

关键词： computer vision Detectors Interpolation Cameras Information analysis Layout Performance evaluation Image analysis Application software Image processing

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Temporal Segmentation and Activity Classification from First-person Sensing

Temporal Segmentation and Activity Classification from First...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Ekaterina H. Spriggs Fernando De La Torre Martial Hebert Carnegie Mellon University

ISBN: (纸本)9781424439942

Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and periodicity of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We provide initial results from investigating two distinct problems - classification of the overall task being performed, and the more difficult problem of classifying individual frames over time into specific actions. We explore first-person sensing through a wearable camera and Inertial Measurement Units (IMUs) for temporally segmenting human motion into actions and performing activity classification in the context of cooking and recipe preparation in a natural environment. We present baseline results for supervised and unsupervised temporal segmentation, and recipe recognition in the CMU-Multimodal activity database (CMU-MMAC).

关键词： Humans Cameras Databases computer vision Hidden Markov models computer graphics Motion analysis Computational modeling Measurement units Motion detection

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AWEAR 2.0 System: Omni-directional Audio-Visual Data Acquisition and Processing

AWEAR 2.0 System: Omni-directional Audio-Visual Data Acquisi...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Michal Havlena Andreas Ess Wim Moreau Akihiko Torii Michal Jancosek Tomas Pajdla Luc Van Gool Center for Machine Perception Department of Cybernetics， Faculty of Elec. Eng. Czech Technical University in Prague Computer Vision Laboratory D-ITET， ETH Zurich Switzerland PSI-VISICS ESAT， KU Leuven Computer Vision Laboratory D-ITET， ETH Zurich Switzerland PSI-VISICS ESAT， KU Leuven

ISBN: (纸本)9781424439942

We present a wearable audio-visual capturing system, termed AWEAR 2.0, along with its underlying vision components that allow robust self-localization, multi-body pedestrian tracking, and dense scene reconstruction. Designed as a backpack, the system is aimed at supporting the cognitive abilities of the wearer. In this paper, we focus on the design issues for the hardware platform and on the performance of the current state-of-the-art computer vision methods on the acquired sequences. We describe the calibration procedure of the two omni-directional cameras present in the system as well as a Structure-from-Motion pipeline that allows for stable multi-body tracking even from rather shaky video sequences thanks to ground plane stabilization. Furthermore, we show how a dense scene reconstruction can be obtained from the data acquired with the platform.

关键词： Data acquisition Cameras computer vision Layout Firewire Batteries Biomedical monitoring Robustness Hardware Calibration

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Robust Real-Time 3D Modeling of Static Scenes Using Solely a Time-of-Flight Sensor

Robust Real-Time 3D Modeling of Static Scenes Using Solely a...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Johannes Feulner Jochen Penne Eva Kollorz Joachim Hornegger Chair of Pattern Recognition Friedrich-Alexander-University

ISBN: (纸本)9781424439942

An algorithm is proposed for the 3D modeling of static scenes solely based on the range and intensity data acquired by a Time-of-Flight camera during an arbitrary movement. No additional scene acquisition devices, like inertia sensor, positioning robots or intensity based cameras are incorporated. The current pose is estimated by maximizing the uncentered correlation coefficient between edges detected in the current and a preceding frame at a minimum frame rate of four fps and an average accuracy of 45mm. The paper also describes several extensions for robust registration like multiresolution hierarchies and projection Iterative Closest Point algorithm. The basic registration algorithm and its extensions were intensively evaluated against ground truth data to validate the accuracy, robustness and real-time-capability.

关键词： 3D modeling 3D reconstruction ToF camera Time-of-Flight

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Shadow multiplexing for real-time silhouette extraction

Shadow multiplexing for real-time silhouette extraction

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Tom Cuypers Yannick Francken Johannes Taelman Philippe Bekaert Expertise Centre for Digital Media Hasselt University Belgium

In this work we propose a real-time implementation for efficient extraction of multi-viewpoint silhouettes using a single camera. The method is based on our previously presented proof-of-concept shadow multiplexing method. We replace the cameras of a typical multi-camera setup with colored light sources and capture the multiplexed shadows. Because we only use a single camera, our setup is much cheaper than a classical setup, no camera synchronization is required, and less data has to be captured and processed. In addition, silhouette extraction is simple as we are segmenting the shadows instead of the texture of objects and background. Demultiplexing runs at 40 fps on current graphics hardware. Therefore this technique is suitable for real-time applications such as collision detection. We evaluate our method on both a real and a virtual setup, and show that our technique works for a large variety of objects and materials.

关键词： Light sources Image reconstruction Layout Data mining Digital cameras Demultiplexing Graphics Hardware Rendering (computer graphics) Lenses

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Using closed captions to train activity recognizers that improve video retrieval

Using closed captions to train activity recognizers that imp...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sonal Gupta Raymond J. Mooney Department of Computer Sciences University of Texas Austin Austin TX USA

Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle & zoom, rapid camera movements etc. Large corpora of labeled videos can be used to train automated activity recognition systems, but this requires expensive human labor and time. This paper explores how closed captions that naturally accompany many videos can act as weak supervision that allows automatically collecting `labeled' data for activity recognition. We show that such an approach can improve activity retrieval in soccer videos. Our system requires no manual labeling of video clips and needs minimal human supervision. We also present a novel caption classifier that uses additional linguistic information to determine whether a specific comment refers to an on-going activity. We demonstrate that combining linguistic analysis and automatically trained activity recognizers can significantly improve the precision of video retrieval.

关键词： Humans Cameras Labeling Content based retrieval Multimedia communication Broadcasting Games Multimedia systems Training data DVD

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An Optimal Control Approach for Deformable Registration

An Optimal Control Approach for Deformable Registration

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Gabriel L. Hart Christopher Zach Marc Niethammer Department of Computer Science University of North Carolina at Chapel Hill NC Department of Computer Science University of North Carolina at Chapel Hill NC BiomedicalResearch Imaging Center University of North Carolina at Chapel Hill NC

ISBN: (纸本)9781424439942

This paper addresses large-displacement-diffeomorphic mapping registration from an optimal control perspective. This viewpoint leads to two complementary formulations. One approach requires the explicit computation of coordinate maps, whereas the other is formulated strictly in the image domain (thus making it also applicable to manifolds which require multiple coordinate charts). We discuss their intrinsic relation as well as the advantages and disadvantages of the two approaches. Further, we propose a novel formulation for unbiased image registration, which naturally extends to the case of time-series of images. We discuss numerical implementation details and carefully evaluate the properties of the alternative algorithms.

关键词： Optimal control Image registration Biomedical optical imaging Nonlinear optics Image motion analysis Biomedical imaging computer science Optical imaging Biomedical computing Image analysis

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Towards Automated Large Scale Discovery of Image Families

Towards Automated Large Scale Discovery of Image Families

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Mohamed Aly Peter Welinder Mario Munich Pietro Perona Computational Vision Lab Caltech Evolution Robotics Pasadena CA

ISBN: (纸本)9781424439942

Gathering large collections of images is quite easy nowadays with the advent of image sharing websites, such as ***. However, such collections inevitably contain duplicates and highly similar images, what we refer to as image families. Automatic discovery and cataloguing of such similar images in large collections is important for many applications, e.g. image search, image collection visualization, and research purposes among others. In this work, we investigate this problem by thoroughly comparing two broad approaches for measuring image similarity: global vs. local features. We assess their performance as the image collection scales up to over 11, 000 images with over 6, 300 families. We present our results on three datasets with different statistics, including two new challenging datasets. Moreover, we present a new algorithm to automatically determine the number of families in the collection with promising results.

关键词： Large-scale systems Clustering algorithms Layout Partitioning algorithms Gunshot detection systems computer vision Robot vision systems Robotics and automation Poles and towers Visualization

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Predicate logic based image grammars for complex pattern recognition

Predicate logic based image grammars for complex pattern rec...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vinay Shet Maneesh Singh Claus Bahlmann Visvanathan Ramesh Siemens Corporate Research Inc. Princeton NJ USA

In this paper, an extended work reported in [Shet, et al , 2007] to detect complex objects in aerial images was discussed. Such objects, e.g. surface to air missile launcher sites, are highly variable in appearance and can only be characterized by their functional design and surrounding context, such as physical arrangement of access structures. Constraints in acquiring sufficient annotated data for learning make it challenging for purely data driven approaches to adequately generalize. In this work, structure arising from functional requirements and surrounding context has been encoded using predicate logic based grammars. Observation and model uncertainties have been integrated within the bi lattice framework. Also in this paper a proposed method to automatically optimize weights associated with logical rules is presented. Automated logical rule weight learning is an important aspect of the application of such systems in the computer vision domain. The proposed approach casts the instantiated inference tree as a knowledge based neural net, interprets rule uncertainties as link weights in the network, and applies a constrained, back propagation (BP) algorithm to converge upon a set of weights for optimal performance. The BP algorithm has been accordingly modified to compute local gradients over the bi lattice specific inference operation and respect constraints specific to vision applications. Both extension have been evaluated over real and simulated data with favorable results.

关键词： Logic pattern recognition Uncertainty Lattices computer vision Inference algorithms Object detection Missiles Optimization methods Application software

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Finding Iconic Images

Finding Iconic Images

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Tamara L. Berg Alexander C. Berg Computer Science Dept. SUNY Stony Brook Computer Science Dept. Columbia University

ISBN: (纸本)9781424439942

We demonstrate that is it possible to automatically find representative example images of a specified object category. These canonical examples are perhaps the kind of images that one would show a child to teach them what, for example a horse is - images with a large object clearly separated from the background. Given a large collection of images returned by a web search for an object category, our approach proceeds without any user supplied training data for the category. First images are ranked according to a category independent composition model that predicts whether they contain a large clearly depicted object, and outputs an estimated location of that object. Then local features calculated on the proposed object regions are used to eliminate images not distinctive to the category and to cluster images by similarity of object appearance. We present results and a user evaluation on a variety of object categories, demonstrating the effectiveness of the approach.

关键词： Horses Web search Training data Predictive models

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