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Fast Fog Detection for Camera Based Advanced Driver Assistance Systems

Fast Fog Detection for Camera Based Advanced Driver Assistan...

17th International IEEE Conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems Engineering Conference 2014

作者： Rita Spinneker Carsten Koch Su-Birm Park Jason Jeongsuk Yoon Hochschule Emden/Leer University of Applied Sciences Institute of InformaticsAutomation and Robotics (I2AR)EmdenGermany LG Electronics Future IT Lab Computer Vision Team SeoulRepublic of Korea

In an increasing number of cars,the driver is supported by Advanced Driver Assistance Systems(ADAS).In particular camera based ADAS are a key component for further improvements in safety and driving *** imaging sensors are performing well under good weather conditions,their efficiency suffers under adverse environmental influences such as heavy rain,fog or *** handle such optical threats and to estimate information quality of cameras in order to warn the assistance system of possible critical working conditions,a self-diagnosis mechanism is of great importance for reliable operation of an optical *** this paper an approach of camera based fog detection as part of a self-diagnosis mechanism for ADAS based on the blurring effect of fog is *** encouraging results of our experiments have shown that the presented approach of analysing the power spectrum slope(PSS) of a small image block in close proximity to the vanishing point enables a fast discrimination of street scenes with and without fog.

关键词： image Fast Fog Detection for Camera Based Advanced Driver Assistance Systems

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AN UNCERTAINTY DECODING APPROACH TO NOISE- AND REVERBERATION-ROBUST SPEECH RECOGNITION

AN UNCERTAINTY DECODING APPROACH TO NOISE- AND REVERBERATION...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Roland Maas Akshaya Thippur Armin Sehr Walter Kellermann Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Erlangen Germany Computer Vision and Active Perception Lab School of Computer Science and Communication KTH Royal Institute of Technology Stockholm Sweden Beuth University of Applied Sciences Berlin Department VII Berlin Germany

ISBN: (纸本)9781479903573

The generic REMOS (Reverberation Modeling for robust Speech recognition) concept is extended in this contribution to cope with additional noise components. REMOS originally embeds an explicit reverberation model into a hidden Markov model (HMM) leading to a relaxed conditional independence assumption for the observed feature vectors. During recognition, a nonlinear optimization problem is to be solved in order to adapt the HMMs' output probability density functions to the current reverberation conditions. The extension for additional noise components necessitates a modified numerical solver for the nonlinear optimization problem. We propose an approximation scheme based on continuous piecewise linear regression. Connected-digit recognition experiments demonstrate the potential of REMOS in reverberant and noisy environments. They furthermore reveal that the benefit of an explicit reverberation model, overcoming the conditional independence assumption, increases with increasing signal-to-noise-ratios.

关键词： automatic speech recognition reverberation robustness noise robustness uncertainty decoding piecewise linear regression Speech recognition Hidden Markov models Noise robustness Linear Regression Reverberation DECODING Additive noise Conditional Independence Uncertainty

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ChaLearn Joint Contest on Multimedia Challenges beyond Visual Analysis: An overview 23

ChaLearn Joint Contest on Multimedia Challenges beyond Visua...

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23rd International Conference on Pattern Recognition, ICPR 2016

作者： Escalante, Hugo Jair Ponce-López, Victor Wan, Jun Riegler, Michael A. Chen, Baiyu Clapés, Albert Escalera, Sergio Guyon, Isabelle Baró, Xavier Halvorsen, Pål Müller, Henning Larson, Martha ChaLearn CA United States INAOE Puebla Mexico Computer Vision Center UAB Barcelona Spain Dept. Mathematics and Computer Science UB Spain EIMT/IN3 Open University of Catalonia Barcelona Spain NLPR Institute of Automation Chinese Academy of Sciences China Simula Research Laboratory Norway Université Paris-Saclay Paris France University of Applied Sciences Western Switzerland Sierre Switzerland Multimedia Information Retrieval Lab Delft University of Technology Delft Netherlands University of California Berkeley Berkeley United States

ISBN: (纸本)9781509048472

This paper provides an overview of the Joint Contest on Multimedia Challenges Beyond Visual Analysis. We organized an academic competition that focused on four problems that require effective processing of multimodal information in order to be solved. Two tracks were devoted to gesture spotting and recognition from RGB-D video, two fundamental problems for human computer interaction. Another track was devoted to a second round of the first impressions challenge of which the goal was to develop methods to recognize personality traits from short video clips. For this second round we adopted a novel collaborative-competitive (i.e., coopetition) setting. The fourth track was dedicated to the problem of video recommendation for improving user experience. The challenge was open for about 45 days, and received outstanding participation: almost 200 participants registered to the contest, and 20 teams sent predictions in the final stage. The main goals of the challenge were fulfilled: the state of the art was advanced considerably in the four tracks, with novel solutions to the proposed problems (mostly relying on deep learning). However, further research is still required. The data of the four tracks will be available to allow researchers to keep making progress in the four tracks. © 2016 IEEE.

关键词： Human computer interaction

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Exploring the Potential of Symbolic Regression in Precision Livestock Farming 11

Exploring the Potential of Symbolic Regression in Precision ...

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11th European Conference on Precision Livestock Farming

作者： Mayrhuber, E. Oczak, M. Iwersen, M. Brunner, D. Dorfer, V. Winkler, S.M. Bioinformatics Research Group PLFDoc University of Applied Sciences Upper Austria Hagenberg Austria Computer Vision Lab TU Wien Vienna Austria Precision Livestock Farming Hub The University of Veterinary Medicine Vienna Vienna Austria Center for Animal Nutrition and Welfare The University of Veterinary Medicine Vienna Vienna Austria Center for Veterinary Systems Transformation and Sustainability Clinical Unit for Herd Health Management The University of Veterinary Medicine Vienna Vienna Austria

ISBN: (纸本)9791221067361

The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival. This study investigates the potential of explainable artificial intelligence for precision livestock farming, focusing on acceleration sensor data from sows around the time of farrowing. Accurately predicting farrowing allows farmers to balance both, piglet safety by avoiding piglet crushing events and the mother sow's welfare, by avoiding early crating. Traditional machine learning models often lack interpretability ("closed-box" problem). Explainable artificial intelligence offers a solution by revealing the decision-making processes within the models. This work is split into two stages. First the detection of nest building behaviour and second the prediction of the remaining time to farrowing. The nest building detection was possible for 166 out of 180 sows and successful within 48 hours before farrowing for 149 sows. The nest building detection is purely based on the change of a sow's activity level and uses no machine learning techniques. Further, this study utilizes symbolic regression, an open-box modelling technique that generates interpretable mathematical formulas, to predict the remaining time until the onset of farrowing. The results are compared with closed-box models, like support vector regression and artificial neural networks, for both explainability and performance. Notably, symbolic regression achieved a mean absolute error of 9.28 hours for the time-to-farrowing prediction, outperforming the closed-box models in terms of performance and interpretability. This research demonstrates that explainable artificial intelligence, specifically symbolic regression, can offer both interpretable results and competitive performance in precision livestock farming. © 2024 11th European Conference on Precision Livestock Farming. All rights reserved.

关键词： Support vector regression

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GP-CONVCNP: Better generalization for convolutional conditional neural processes on time series data

arXiv

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arXiv 2021年

作者： Petersen, Jens Köhler, Gregor Zimmerer, David Isensee, Fabian Jäger, Paul F. Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIP Applied Computer Vision Lab Division of Medical Image Computing German Cancer Research Center Interactive Machine Learning Group German Cancer Research Center

Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points. A recent addition to this family, Convolutional Conditional Neural Processes (CONVCNP), have shown remarkable improvement in performance over prior art, but we find that they sometimes struggle to generalize when applied to time series data. In particular, they are not robust to distribution shifts and fail to extrapolate observed patterns into the future. By incorporating a Gaussian Process into the model, we are able to remedy this and at the same time improve performance within distribution. As an added benefit, the Gaussian Process reintroduces the possibility to sample from the model, a key feature of other members in the NP family. © 2021, CC BY.

关键词： Convolution

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Lifelogging Keyframe Selection using Image Quality Measurements and Physiological Excitement Features

Lifelogging Keyframe Selection using Image Quality Measureme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Photchara Ratsamee Yasushi Mae Amornched Jinda-apiraksa Jana Machajdik Kenichi Ohara Masaru Kojima Robert Sablatnig Tatsuo Arai Faculty of Systems Science and Applied Informatics at Osaka University Japan. Advanced Digital Sciences Center (ADSC) University of Illinois at Urbana-Champaign Singapore. Computer Vision Lab Institute of Computer Aided Automation Vienna University of Technology (TU Wien) Austria. Faculty of Science and Technology Meijo University Japan.

ISBN: (纸本)9781467363563

Keyframe selection is the process of finding a representative frame in an image sequence. Although mostly known from video processing, keyframe selection faces new challenges in the lifelog domain. To obtain a keyframe that is close to a user-selected frame, we propose a keyframe selection method based on image quality measurements and excitement features. Image quality measurements such as contrast, color variance, sharpness, noise and saliency are used to filter high quality images. However, high quality images are not necessarily keyframes because humans also use emotions in the selection process. In this study, we employ a biosensor to measure the excitement of humans. In previous investigation, keyframe selection using only image quality measurements yielded an acceptance rate of 79.70%. Our proposed method achieves an acceptance rate of 84.45%.

关键词： Quality Measurements Physiological Excitement Features lifelog domain Quality management excitement Image quality image sequences Process selection Human Emotions

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BasicVSR: The search for essential components in video super-resolution and beyond

arXiv

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arXiv 2020年

作者： Chan, Kelvin C.K. Wang, Xintao Yu, Ke Dong, Chao Loy, Chen Change S-Lab Nanyang Technological University Singapore Applied Research Center Tencent PCG CUHK – SenseTime Joint Lab Chinese University of Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Video super-resolution (VSR) approaches tend to have more components than the image counterparts as they need to exploit the additional temporal dimension. Complex designs are not uncommon. In this study, we wish to untangle the knots and reconsider some most essential components for VSR guided by four basic functionalities, i.e., Propagation, Alignment, Aggregation, and Upsampling. By reusing some existing components added with minimal redesigns, we show a succinct pipeline, BasicVSR, that achieves appealing improvements in terms of speed and restoration quality in comparison to many state-of-the-art algorithms. We conduct systematic analysis to explain how such gain can be obtained and discuss the pitfalls. We further show the extensibility of BasicVSR by presenting an information-refill mechanism and a coupled propagation scheme to facilitate information aggregation. The BasicVSR and its extension, IconVSR, can serve as strong baselines for future VSR approaches. © 2020, CC BY.

关键词： Optical resolving power

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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps 38

Binding in hippocampal-entorhinal circuits enables compositi...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kymn, Christopher J. Mazelet, Sonia Thomas, Anthony Kleyko, Denis Frady, E. Paxon Sommer, Friedrich T. Olshausen, Bruno A. Redwood Center for Theoretical Neuroscience Helen Wills Neuroscience Institute UC Berkeley Berkeley United States Université Paris-Saclay ENS Paris-Saclay Gif-sur-Yvette France Department of Electrical and Computer Engineering UC Davis Davis United States Centre for Applied Autonomous Sensor Systems Örebro University Örebro Sweden Intelligent Systems Lab Research Institutes of Sweden Kista Sweden Intel Labs Santa Clara United States Herbert Wertheim School of Optometry & Vision Science UC Berkeley Berkeley United States

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions. © 2024 Neural information processing systems foundation. All rights reserved.

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Sensitive vPSA - Exploring Sensitivity in Visual Parameter Space Analysis

arXiv

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arXiv 2022年

作者： Fröhler, Bernhard Elberfeld, Tim Möller, Torsten Hege, Hans-Christian Maurer, Julia Heinzl, Christoph the Research Group X-Ray Computed Tomography University of Applied Sciences Upper Austria Austria the imec-Vision Lab University of Antwerp Belgium Data Science @ Uni Vienna the Faculty of Computer Science University of Vienna Austria Visual and Data-Centric Computing Department Zuse Institut Berlin Germany

The sensitivity of parameters in computational science problems is difficult to assess, especially for algorithms with multiple input parameters and diverse outputs. This work seeks to explore sensitivity analysis in the visualization domain, introducing novel techniques for respective visual analyses of parameter sensitivity in multi-dimensional algorithms. First, the sensitivity analysis background is revisited, highlighting the definition of sensitivity analysis and approaches analyzing global and local sensitivity as well as the differences of sensitivity analysis to the more common uncertainty analysis. We introduce and explore parameter sensitivity using visualization techniques from overviews to details on demand, covering the analysis of all aspects of sensitivity in a prototypical implementation. The respective visualization techniques outline the algorithmic in- and outputs including indications, on how sensitive an input is with regard to the outputs. The detailed sensitivity information is communicated through constellation plots for the exploration of input and output spaces. A matrix view is discussed for localized information on the sensitivity of specific outputs to specific inputs. A 3D view provides the link of the parameter sensitivity to the spatial domain, in which the results of the multi-dimensional algorithms are embedded. The proposed sensitivity analysis techniques are implemented and evaluated in a prototype called Sensitivity Explorer. We show that Sensitivity Explorer reliably identifies the most influential parameters and provides insights into which of the output characteristics these affect as well as to which extent. © 2022, CC BY-NC-SA.

关键词： Sensitivity analysis

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IEEE Transactions on Haptics: Editorial

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IEEE Transactions on Haptics 2008年第1期1卷 2-8页

作者： Edward Colgate, J. Lederman, Susan Prattichizzo, Domenico Barbagli, Federico Basdogan, Cagatay Buss, Martin Ernst, Marc Frisoli, Antonio Gillespie, Brent Grant, Danny Harders, Matthias Hayward, Vincent Jones, Lynette Kajimoto, Hiroyuki Kappers, Astrid Kheddar, Abderrahmane Lin, Ming C. MacLean, Karon Miller, Brian Noma, Haruo Okamura, Allison M. O'Malley, Mareia Ryu, Jeha Tan, Hong Z. Yokokohji, Yasuyoshi Department of Psychology School of Computing Queen's University Canada IEEE Robotics and Automation Society Tübingen Germany Stanford University's Computer Science Department Tübingen Germany Institute of Automatic Control Engineering Technische Universität München Germany Independent Research Group Multisensory Perception and Action Max Planck Institute for Biological Cybernetics Tübingen Germany Department of Applied Mechanics SSSA Japan Research Division Virtual Reality and Telerobotic Systems PERCRO Laboratory Japan Department of Mechanical Engineering Japan Department of Robotics McGill University Canada Computer Vision Lab ETH Zurich Japan Virtual Reality in Medicine Group Japan IEEE CS/RAS Technical Committee on Haptics Japan Department of Electrical and Computer Engineering McGill University Canada Department of Mechanical Engineering MIT Japan University of Electro-Communications Japan Department of Physics and Astronomy Utrecht University Netherlands University of Evry France Chapel Hill United States Department of Computer Science University of British Columbia Canada ATR Knowledge Science Lab United Kingdom Department of Mechanical Engineering Denmark Johns Hopkins University United States Laboratory for Computational Sensing and Robotics United Kingdom Engineering Research Center for Computer-Integrated Surgical Systems and Technology United States Mechanical Engineering and Materials Science Department Rice University United States Korea Republic of Department of Electrical and Computer Engineering Mechanical Engineering and Psychological Sciences Purdue University United States Department of Mechanical Engineering and Science Graduate School of Engineering Kyoto University Japan

No abstract available

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