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11th International Conference on Energy Minimization Methods in computer vision and pattern Recognition, EMMVCPR 2017

作者： Zern, Artjom Rohr, Karl Schnörr, Christoph Image and Pattern Analysis Group Heidelberg University Heidelberg Germany Biomedical Computer Vision Group BIOQUANT Heidelberg University Heidelberg Germany

ISBN: (纸本)9783319781983

In [2], a smooth geometric labeling approach was introduced by following the Riemannian gradient flow of a given objective function on the so-called assignment manifold. The approach evaluates a user-defined data term and performs spatial regularization by Riemannian averaging of the assignment vectors. In this paper, we extend this approach in order to impose global convex constraints on the labeling results based on linear filter statistics in the label space. The smoothness of the approach is preserved by using logarithmic barrier functions to handle the new constraints. We discuss how suitable filters can be determined from example data of a given image class, and we demonstrate numerically the effectiveness of the constraints in several academic labeling scenarios. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Geometry

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Combining the advantages of local and global optic flow methods

Combining the advantages of local and global optic flow meth...

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24th Symposium of the German pattern Recognition Association, DAGM 2002

作者： Bruhn, Andres Weickert, Joachim Schnorr, Christoph Mathematical Image Analysis Group Faculty of Mathematics and Computer Science Saarland University 66041 Saarbrücken Germany Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim 68131 Mannheim Germany

ISBN: (纸本)354044209X

Differential methods are frequently used techniques for optic flow computations. They can be classified into local methods such as the Lucas-Kanade technique or Bigün's structure tensor method, and into global methods such as the Horn-Schunck approach and its modifications. Local methods are known to be more robust under noise, while global techniques yield 100% dense flow fields. No clear attempts to combine the advantages of these two classes of methods have been made in the literature so far. This problem is addressed in our paper. First we juxtapose the role of smoothing processes that are required in local and global differential methods for optic flow computation. This discussion motivates us to introduce and evaluate a novel method that combines the advantages of local and global approaches: It yields dense flow fields that are robust against noise. Finally experiments with different sequences are performed demonstrating its excellent results. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Flow fields

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Skew estimation and correction for form documents using wavelet decomposition

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2nd International Conference on Image analysis and Recognition, ICIAR 2005

作者： Xi, Dihua Kamel, Mohamed Lee, Seong-Whan Pattern Analysis and Machine Intelligence Research Group Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Center for Artificial Vision Research Korea University Seoul Korea Republic of

ISBN: (纸本)3540290699

Form document image processing has become an increasingly essential technology in office automation tasks. One of the problems is that the document image may appear skewed for many reasons. Therefore, the skew estimation plays an important role in any automatic document analysis system. In the past few years, many algorithms have been developed to detect the skew angle of text document images. However, these algorithms suffer from two major deficiencies. Firstly, most of them suppose that the original image is monochrome and therefore they are not suitable to apply to documents with a complicated background. Secondly, most of the current methods were developed for general document images that are not as complicated as form documents. In this paper, we present a new approach to skew detection for grey-level form document images. In our system, image decomposition by 2D wavelet transformations is used to estimate the skew angle. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Image processing

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Expressive Synthetic Voices: Considerations for Human Robot Interaction

Expressive Synthetic Voices: Considerations for Human Robot ...

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IEEE International Symposium on Robot and Human Interactive Communication

作者： S. Hennig R. Chellali Pattern Analysis & Computer Vision group at the Istituto Italiano di Technologia in affiliation with Universita degli Studi di Genova Pattern Analysis & Computer Vision group at the Istituto Italiano di Technologia

ISBN: (纸本)9781467346047

As speech synthesis technology develops more advanced paralinguistic capabilities, open questions emerge regarding how humans perceive the use of such vocal capabilities by robots. Perceptions of spoken interaction are complex and influenced by multiple factors including the linguistic content of a message, social context, perceived intelligence of the agent, and form factor of its embodiment. This paper shares results from a study that controlled for the above factors in order to investigate the effect on human listeners of a male synthetic voice with an expressive range. Participants were randomly assigned to three conditions, counterbalancing for gender and language background, in which how paralinguistic cues were applied was varied. As the voice became more expressive and appropriate for the context, observers were more likely to describe the communication as effective, but were less likely to refer to the unseen agent as a person. Possible effects of the listener gender and cultural-linguistic background are examined. Implications for future methodologies in this field are discussed.

关键词： Human machine interaction Voice Speech synthesis Listeners Gender Agents Intelligence SYNTHETICS Human

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Designing 3-D nonlinear diffusion filters for high performance cluster computing

Designing 3-D nonlinear diffusion filters for high performan...

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24th German Association for pattern Recognition Symposium, DAGM 2002

作者： Bruhn, Andrés Jakob, Tobias Fischer, Markus Kohlberger, Timo Weickert, Joachim Brüning, Ulrich Schnörr, Christoph Mathematical Image Analysis Group Mathematics and Computer Science Saarland University Building 27.1 Saarbrücken66041 Germany Computer Architecture Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany Computer Vision Graphics and Pattern Recognition Group Department of Mathematics and Computer Science University of Mannheim Mannheim68131 Germany

ISBN: (纸本)354044209X

This paper deals with parallelization and implementation aspects of PDE based image processing models for large cluster environments with distributed memory. As an example we focus on nonlinear isotropic diffusion filtering which we discretize by means of an additive operator splitting (AOS). We start by decomposing the algorithm into small modules that shall be parallelized separately. For this purpose image partitioning strategies are discussed and their impact on the communication pattern and volume is analyzed. Based on the results we develop an algorithmic implementation with excellent scaling properties on massively connected low latency networks. Test runs on a high–end Myrinet cluster yield almost linear speedup factors up to 209 for 256 processors. This results in typical denoising times of 0.5 seconds for five iterations on a 256 × 256 × 128 data cube. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Cluster computing

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Image foreground extraction — A supervised framework based on region transfer

Image foreground extraction — A supervised framework based ...

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International Conference on Signal and Information Processing (IConSIP)

作者： Biplab Banerjee Sudipan Saha Shabbir N. Merchant Pattern Analysis and Computer Vision Group Istituto Italiano Di Tecnologia Genoa Italy

ISBN: (纸本)9781509015238

We propose a simple technique for extracting dominant foreground objects from images in this paper. Image foreground segmentation is a challenging task given problems due to complex background, clutter and occlusion, spectral variability in object appearance etc. Most of the techniques in this respect are semi-automatic in nature which require prior assumptions regarding the object location in the image in the form of rough bounding boxes. On the contrary, we propose a patch transfer based approach which propagates rough segmentation masks from the training to the test image patches. Further, these segmentation masks are combined and updated using a Markov random field model to generate the final foreground segmentation. The proposed technique is simple, robust and can work with very few labeled samples. Results obtained on the Weizmann Horse and the Leeds Butterfly dataset exhibit the superiority of the proposed approach with respect to existing methods from the literature.

关键词： Image segmentation Proposals Training Semantics Active contours Hidden Markov models Object recognition

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Fully-automated analysis of muscle fiber images with combined region and edge-based active contours

Fully-automated analysis of muscle fiber images with combine...

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Workshops Bildverarbeitung fur die Medizin: Algorithmen Systeme Anwendungen, BVM 2006 - Workshop on Image Processing for Medicine: Algorithms - Systems - Applications, BVM 2006

作者： Brox, Thomas Kim, Yoo-Jin Weickert, Joachim Feiden, Wolfgang Computer Vision and Pattern Recognition Group University of Bonn Romerstr. 164 53117 Bonn Germany Institute of Neuropathology Saarland University 66421 Homburg/Saar Germany Math. Image Analysis Group Saarland University 66041 Saarbrücken Germany

ISBN: (纸本)9783540321361

This paper presents a new approach to automated muscle fiber analysis based on segmenting myofibers with combined region and edge-based active contours. It provides reliable and fully-automated processing, thus, enabling time-saving batch processing of the entire biopsy sample stemming from routinely HE-stained cryostat sections. The method combines color, texture, and edge cues in a level set based active contour model succeeded by a refinement with morphological filters. Falsepositive segmentations as compared to former methods are minimized. A quantitative comparison between manual and automated analysis of muscle fibers images did not reveal any significant differences. We gratefully acknowledge partial funding by the DFG.

关键词： Batch data processing

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On Moving Object Segmentation from Monocular Video with Transformers

On Moving Object Segmentation from Monocular Video with Tran...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Christian Homeyer Christoph Schnörr Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and *** this paper, we present a novel fusion architecture for monocular motion segmentation - M 3 Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Code will be released upon publication.

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On Moving Object Segmentation from Monocular Video with Transformers

arXiv

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arXiv 2024年

作者： Homeyer, Christian Schnörr, Christoph Robert Bosch GmbH Corporate Research Computer Vision Lab Hildesheim Germany Image and Pattern Analysis Group Heidelberg University Germany

Moving object detection and segmentation from a single moving camera is a challenging task, requiring an understanding of recognition, motion and 3D geometry. Combining both recognition and reconstruction boils down to a fusion problem, where appearance and motion features need to be combined for classification and segmentation. In this paper, we present a novel fusion architecture for monocular motion segmentation - M3Former, which leverages the strong performance of transformers for segmentation and multi-modal fusion. As reconstructing motion from monocular video is ill-posed, we systematically analyze different 2D and 3D motion representations for this problem and their importance for segmentation performance. Finally, we analyze the effect of training data and show that diverse datasets are required to achieve SotA performance on Kitti and Davis. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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LEDNet: Deep Network for Single Image Haze Removal 2018

LEDNet: Deep Network for Single Image Haze Removal

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Proceedings of the 11th Indian Conference on computer vision, Graphics and Image Processing

作者： Akshay Dudhane Subrahmanyam Murala Abhinav Dhall Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar India Learning Affect and Semantic Image Analysis Group Indian Institute of Technology Ropar India

ISBN: (纸本)9781450366151

Haze during the bad weather, degrades the visibility of the scene drastically. Degradation of scene visibility varies with respect to the transmission coefficient/map (Tc) of the scene. Estimation of accurate Tc is key step to reconstruct the haze free scene. Previously, local as well as global priors were proposed to estimate the Tc. We, on the other hand, propose integration of local and global approaches to learn both point level and object level Tc. The proposed local encoder decoder network (LEDNet) estimates the scene transmission map in two stages. During first stage, network estimates the point level Tc using parallel convolutional filters and spatial invariance filtering. The second stage comprises of a two level encoder-decoder architecture which anticipates the object level Tc. We also propose, local air-light estimation (LAE) algorithm, which is able to obtain the air-light component of the outdoor scene. Combination of LEDNet and LAE improves the accuracy of haze model to recover the scene radiance. Structural similarity index, mean square error and peak signal to noise ratio are used to evaluate the performance of the proposed approach for single image haze removal. Experiments on benchmark datasets show that LEDNet outperforms the existing state-of-the-art methods for single image haze removal.

关键词： Haze removal transmission map CNN

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