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Automated image forgery detection through classification of JPEG ghosts

Automated image forgery detection through classification of ...

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Zach, Fabian Riess, Christian Angelopoulou, Elli Pattern Recognition Lab. University of Erlangen-Nuremberg Germany

ISBN: (纸本)9783642327162

We present a method for automating the detection of the so-called JPEG ghost s. JPEG ghost s can be used for discriminating single- and double JPEG compression, which is a common cue for image manipulation detection. the JPEG ghost scheme is particularly well-suited for non-technical experts, but the manual search for such ghost s can be both tedious and error-prone. In this paper, we propose a method that automatically and efficiently discriminates single- and double-compressed regions based on the JPEG ghost principle. Experiments show that the detection results are highly competitive with state-of-the-art methods, for both, aligned and shifted JPEG grids in double-JPEG compression. © 2012 Springer-Verlag.

关键词： Image classification

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Combination of sinusoidal and single binary pattern projection for fast 3D surface reconstruction

Combination of sinusoidal and single binary pattern projecti...

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Bräuer-Burchardt, Christian Kühmstedt, Peter Notni, Gunther Fraunhofer IOF Jena Albert-Einstein-Str. 7 Jena D-07745 Germany

ISBN: (纸本)9783642327162

A new method for 3D surface reconstruction is introduced combining classical fringe projection technique and binary single pattern projection. the new technique allows keeping the high accuracy obtained by phase shifting but solves the additional necessary period identification by replacing the extensive Gray-code sequence by a single image of a certain binary pattern. the core of the new method is an algorithm which realizes the assignment of corresponding image regions using epipolar constraint and image correlation. An algorithm is introduced generating a single binary pattern which is optimized concerning image correlation. the results of first measurements show the high robustness of the new method and advantages of the optimized patterns compared to the use of conventional random patterns. © 2012 Springer-Verlag.

关键词： Image correlation

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OUR-CVFH - Oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6DOF pose estimation

OUR-CVFH - Oriented, unique and repeatable clustered viewpoi...

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Aldoma, Aitor Tombari, Federico Rusu, Radu Bogdan Vincze, Markus Vision4Robotics Group ACIN Vienna University of Technology Austria Computer Vision Lab. DEIS - ARCES University of Bologna Italy Open Perception Inc. United States

ISBN: (纸本)9783642327162

We propose a novel method to estimate a unique and repeatable reference frame in the context of 3D object recognition from a single viewpoint based on global descriptors. We show that the ability of defining a robust reference frame on both model and scene views allows creating descriptive global representations of the object view, with the beneficial effect of enhancing the spatial descriptiveness of the feature and its ability to recognize objects by means of a simple nearest neighbor classifier computed on the descriptor space. Moreover, the definition of repeatable directions can be deployed to efficiently retrieve the 6DOF pose of the objects in a scene. We experimentally demonstrate the effectiveness of the proposed method on a dataset including 23 scenes acquired with the Microsoft Kinect sensor and 25 full-3D models by comparing the proposed approach with state-of-the-art global descriptors. A substantial improvement is presented regarding accuracy in recognition and 6DOF pose estimation, as well as in terms of computational performance. © 2012 Springer-Verlag.

关键词： Object recognition

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Salient pattern detection using W2 on multivariate normal distributions

Salient pattern detection using W2 on multivariate normal di...

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Klein, Dominik Alexander Frintrop, Simone Department of Computer Science III University of Bonn Germany

ISBN: (纸本)9783642327162

Saliency is an attribute that is not included in an object itself, but arises from complex relations to the scene. Common belief in neuroscience is that objects are eye-catching if they exhibit an anomaly in some basic feature of human perception. this enables detection of object-like structures without prior knowledge. In this paper, we introduce an approach that models these object-to-scene relations based on probability theory. We rely on the conventional structure of cognitive visual attention systems, measuring saliency by local center to surround differences on several basic feature cues and multiple scales, but innovate how to model appearance and to quantify differences. therefore, we propose an efficient procedure to compute ML-estimates for (multivariate) normal distributions of local feature statistics. Reducing feature statistics to Gaussians facilitates a closed-form solution for the W 2-distance (Wasserstein metric based on the Euclidean norm) between a center and a surround distribution. On a widely used benchmark for salient object detection, our approach, named CoDi-Saliency (for Continuous Distributions), outperformed nine state-of-the-art saliency detectors in terms of precision and recall. © 2012 Springer-Verlag.

关键词： Object detection

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3D object recognition and pose estimation for multiple objects using multi-prioritized RANSAC and model updating

3D object recognition and pose estimation for multiple objec...

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Fenzi, Michele Dragon, Ralf Leal-Taixé, Laura Rosenhahn, Bodo Ostermann, Jörn Leibniz University Hannover Germany

ISBN: (纸本)9783642327162

We present a feature-based framework that combines spatial feature clustering, guided sampling for pose generation, and model updating for 3D object recognition and pose estimation. Existing methods fails in case of repeated patterns or multiple instances of the same object, as they rely only on feature discriminability for matching and on the estimator capabilities for outlier rejection. We propose to spatially separate the features before matching to create smaller clusters containing the object. then, hypothesis generation is guided by exploiting cues collected off- and on-line, such as feature repeatability, 3D geometric constraints, and feature occurrence frequency. Finally, while previous methods overload the model with synthetic features for wide baseline matching, we claim that continuously updating the model representation is a lighter yet reliable strategy. the evaluation of our algorithm on challenging video sequences shows the improvement provided by our contribution. © 2012 Springer-Verlag.

关键词： Gesture recognition

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A Bag of Visual Words Approach for Centromere and Cytoplasmic Staining pattern Classification on HEp-2 Images

A Bag of Visual Words Approach for Centromere and Cytoplasmi...

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25th International symposium on Computer-Based Medical Systems (CBMS)

作者： Iannello, Giulio Onofri, Leonardo Soda, Paolo Univ Campus Biomed Roma Integrated Res Ctr Rome Italy

ISBN: (纸本)9781467320511

Antinuclear autoantibodies (ANAs) are important markers to diagnose autoimmune diseases, very serious and also invalidating illnesses. the benchmark procedure for ANAs diagnosis is the indirect immunofluorescence (IF) assay performed on the HEp-2 substrate. Medical doctors first determine the,fluorescence intensity exhibited by HEp-2 cells, and then report the staining pattern for positive wells only. With reference to staining pattern recognition, in. the literature we found works recognizing five main patterns characterized by well-defined cell edges. these approaches are based on cell segmentation, a task that should be harder than the classification itself We present here a method extending the panel of detectable HEp-2 staining patterns, introducing the centromere and cytoplasmic patterns, which do not show well-defined cell edges, and where a segmentation-based classification may fail. We apply a local approach which extracts SIFT descriptors and then classifies an image through the bag of visual words approach. this permits to represent complex image contents without applying the segmentation procedure. We test our approach on a dataset of HEp-2 images with large variability in both fluorescence intensity and staining patterns. Despite the large skew of the a-priori class distribution, our system correctly recognizes the 98.3% of samples, with a F-measure equal to 92.3%, 95.2% and 99.0%, for each class.

关键词： Diagnosis

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Active metric learning for object recognition

Active metric learning for object recognition

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Ebert, Sandra Fritz, Mario Schiele, Bernt Max Planck Institute for Informatics Saarbrucken Germany

ISBN: (纸本)9783642327162

Popular visual representations like SIFT have shown broad applicability across many task. this great generality comes naturally with a lack of specificity when focusing on a particular task or a set of classes. Metric learning approaches have been proposed to tailor general purpose representations to the needs of more specific tasks and have shown strong improvements on visual matching and recognition benchmarks. However, the performance of metric learning depends strongly on the labels that are used for learning. therefore, we propose to combine metric learning with an active sample selection strategy in order to find labels that are representative for each class as well as improve the class separation of the learnt metric. We analyze several active sample selection strategies in terms of exploration and exploitation trade-offs. Our novel scheme achieves on three different datasets up to 10% improvement of the learned metric. We compare a batch version of our scheme to an interleaved execution of sample selection and metric learning which leads to an overall improvement of up to 23% on challenging datasets for object class recognition. © 2012 Springer-Verlag.

关键词： Object recognition

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Synergy-based learning of facial identity

Synergy-based learning of facial identity

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Köstinger, Martin Roth, Peter M. Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria

ISBN: (纸本)9783642327162

In this paper we address the problem that most face recognition approaches neglect that faces share strong visual similarities, which can be exploited when learning discriminative models. Hence, we propose to model face recognition as multi-task learning problem. this enables us to exploit both, shared common information and also individual characteristics of faces. In particular, we build on Mahalanobis metric learning, which has recently shown good performance for many computer vision problems. Our main contribution is twofold. First, we extend a recent efficient metric learning algorithm to multi-task learning. the resulting algorithm supports label-incompatible learning which allows us to tap the rather large pool of anonymously labeled face pairs also for face identification. Second, we show how to learn and combine person specific metrics for face identification improving the classification power. We demonstrate the method for different face recognition tasks where we are able to match or slightly outperform state-of-the-art multi-task learning approaches. © 2012 Springer-Verlag.

关键词： Face recognition

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BioWizard: Discovering and Validating associations Between Biological Entities by Integrated Analysis of Scientific Literature and Experimental Data

BioWizard: Discovering and Validating Associations Between B...

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25th International symposium on Computer-Based Medical Systems (CBMS)

作者： Spampinato, Concetto Giordano, Daniela Kavasidis, Isaak Milardo, Sebastiano Univ Catania Dept Elect Elect & Comp Engn I-95125 Catania Italy

ISBN: (纸本)9781467320511

In this paper, we present BioWizard, a bioinformatics knowledge discovery tool for extracting and validating implicit associations between biological entities. By mining specialized scientific literature, BioWizard not only generates biological hypotheses in the form of associations between genes, proteins and diseases, but also validates the plausibility of such associations against high-throughput biological data (microarrays) and annotated databases. the main novelties of the proposed approach are that: 1) it infers associations between biological entities by mining full text papers instead of only abstracts as usually performed by the existing tools, 2) a named entity recognition that improves the precision of the derived associations by enriching the vocabularies used in the mining loop with terms extracted directly from the text and, 3) the inferred associations are filtered according to their evidence in experimental data. We tested the precision and the recall of our system in retrieving known-associations (which did not appear in the same document) from gold standards and the results shown the ability of BioWizard in retrieving valid associations, thus providing a valuable tool for the use of biomedical researchers to speed up scientific progress.

关键词： Character recognition

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Active learning of ensemble classifiers for gesture recognition

Active learning of ensemble classifiers for gesture recognit...

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Joint 34th symposium of the german association for pattern recognition, DAGM 2012 and 36th symposium of the Austrian association for pattern recognition, OAGM 2012

作者： Schumacher, J. Sakič, D. Grumpe, A. Fink, Gernot A. Wöhler, C. Lehrstuhl Informatik XII Technische Universität Dortmund Germany Image Analysis Group Technische Universität Dortmund Germany

ISBN: (纸本)9783642327162

In this study we consider the classification of emblematic gestures based on ensemble methods. In contrast to HMM-based approaches processing a gesture as a whole, we classify trajectory segments comprising a fixed number of sampling points. We propose a multi-view approach in order to increase the diversity of the classifiers across the ensemble by applying different methods for data normalisation and dimensionality reduction and by employing different classifier types. A genetic search algorithm is used to select the most successful ensemble configurations from the large variety of possible combinations. In addition to supervised learning, we make use of both labelled and unlabelled data in an active learning framework in order to reduce the effort required for manual labelling. In the supervised learning scenario, recognition rates per moment in time of more than 86% are obtained, which is comparable to the recognition rates obtained by a HMM approach for complete gestures. the active learning scenario yields recognition rates in excess of 80% even when only a fraction of 20% of all training samples are used. © 2012 Springer-Verlag.

关键词： Supervised learning

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