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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8749卷 52-61页

作者： Dibeklioğlu, Hamdi Gevers, Theo Lucassen, Marcel Salah, Albert Ali Intelligent Systems Lab Amsterdam University of Amsterdam Netherlands Pattern Recognition and Bioinformatics Group Delft University of Technology Netherlands Department of Computer Engineering Boğaziçi University Istanbul Turkey

computer based methods for facial age estimation can be improved by incorporating experimental findings from human psychophysics. Moreover, the latter can be used in creating systems that are not necessarily more accurate in age estimation, but strongly resemble human age estimations. In this paper we investigate the perceptual hypothesis that contrast is a useful cue for estimating age from facial appearance. Using an extensive evaluation paradigm, we establish that using a perceptual color space improves computer’s age estimation, and more importantly, using contrast-enabled features results in estimations that are more correlated to human estimations. © Springer International Publishing Switzerland 2014.

关键词： Estimation

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Suppressing Model Overfitting for Image Super-Resolution Networks

Suppressing Model Overfitting for Image Super-Resolution Net...

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IEEE/CVF Conference on computer vision and pattern recognition Workshops

作者： Ruicheng Feng Jinjin Gu Yu Qiao Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The School of Science and Engineering The Chinese University of Hong Kong

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing biases of data. By combining these components - MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge.

关键词： super-resolution Network images Training data Data sampling Imagery (Psychotherapy) Image

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Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution

arXiv

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arXiv 2020年

作者： Feng, Ruicheng Guan, Weipeng Qiao, Yu Dong, Chao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Acedamy of Sciences China Chinese University of Hong Kong Hong Kong

Multi-scale techniques have achieved great success in a wide range of computer vision tasks. However, while this technique is incorporated in existing works, there still lacks a comprehensive investigation on variants of multi-scale convolution in image super resolution. In this work, we present a unified formulation over widely-used multi-scale structures. With this framework, we systematically explore the two factors of multi-scale convolution – feature propagation and cross-scale communication. Based on the investigation, we propose a generic and efficient multi-scale convolution unit – Multi-Scale cross-Scale Share-weights convolution (MS3-Conv). Extensive experiments demonstrate that the proposed MS3-Conv can achieve better SR performance than the standard convolution with less parameters and computational cost. Beyond quantitative analysis, we comprehensively study the visual quality, which show that MS3-Conv behave better to recover high-frequency details. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution

arXiv

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arXiv 2022年

作者： Xie, Liangbin Wang, Xintao Zhang, Honglun Dong, Chao Shan, Ying Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China ARC Lab Tencent PCG China

Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in this dataset are of low quality. As a consequence, the VFSR models trained on this dataset can not output visual-pleasing results. In this paper, we develop an automatic and scalable pipeline to collect a high-quality video face dataset (VFHQ), which contains over 16, 000 high-fidelity clips of diverse interview scenarios. To verify the necessity of VFHQ, we further conduct experiments and demonstrate that VFSR models trained on our VFHQ dataset can generate results with sharper edges and finer textures than those trained on VoxCeleb1. In addition, we show that the temporal information plays a pivotal role in eliminating video consistency issues as well as further improving visual performance. Based on VFHQ, by analyzing the benchmarking study of several state-of-the-art algorithms under bicubic and blind settings. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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Mobile robot localization under varying illumination

Mobile robot localization under varying illumination

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International Conference on pattern recognition

作者： M. Jogan A. Leonardis H. Wildenauer H. Bischof Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Pattern Recognition and Image Processing Group Institute for Computer Aided Automation University of Technology Vienna Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria

Methods for mobile robot localization that use eigenspaces of panoramic snapshots of the environment are in general sensitive to changes in the illumination of the environment. Therefore, we propose an approach which achieves a reliable localization under severe illumination conditions. The method uses gradient filtering of the eigenspace. After testing the approach on images obtained by a mobile robot, we show that it outperforms the standard eigenspace-based recognition method.

关键词： Mobile robots Lighting computer vision Filter bank Image recognition Indoor environments pattern recognition Image processing computer graphics Information science

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A New U-Net Based License Plate Enhancement Model in Night and Day Images 5th

A New U-Net Based License Plate Enhancement Model in Night a...

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5th Asian Conference on pattern recognition, ACPR 2019

作者： Chowdhury, Pinaki Nath Shivakumara, Palaiahnakote Raghavendra, Ramachandra Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Faculty of Information Technology and Electrical Engineering IIK NTNU Gjøvik Norway National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

ISBN: (纸本)9783030414030

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection. © Springer Nature Switzerland AG 2020.

关键词： Pixels

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2-D Evidential Grid Mapping with Narrow Vertical Field of View Sensors using Multiple Hypotheses and Spatial Neighborhoods

2-D Evidential Grid Mapping with Narrow Vertical Field of Vi...

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International IEEE Conference on Intelligent Transportation Systems

作者： Christoph Seeger Michael Manz Patrick Matters Joachim Hornegger BMW Group 80788 Munich Germany Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universitat Erlangen-Nürnberg 91058 Erlangen Germany

ISBN: (纸本)9781467365970

Environment sensors with a narrow vertical field of view often fail to detect obstacles with a small vertical extent from close range. Because an inverse beam sensor model infers free space when there was no measurement, those obstacles are deleted from an occupancy grid eventhough they have been observed in past measurements. This is extremely critical if the car is driving autonomously. Our approach explicitly models these errors using multiple hypotheses in an evidential grid mapping framework that neither needs a classification nor a height of obstacles. Furthermore, we extend the grid mapping framework, that usually assumes mutually independent cells, by including information from neighboring cells. The evaluation in a freeway and a challenging scenario with a boom barrier shows that our method is superior to evidential grid mapping with an inverse beam sensor model.

关键词： Environment sensors narrow vertical evaluation

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Structure Function Based Transform Features for Behavior-Oriented Social Media Image Classification 5th

Structure Function Based Transform Features for Behavior-Ori...

引用

5th Asian Conference on pattern recognition, ACPR 2019

作者： Krishnani, Divya Shivakumara, Palaiahnakote Lu, Tong Pal, Umapada Ramachandra, Raghavendra International Institute of Information Technology Naya Raipur Naya RaipurChhattisgarh India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9783030414030

Social media has become an essential part of people to reflect their day to day activities including emotions, feelings, threatening and so on. This paper presents a new method for the automatic classification of behavior-oriented images like Bullying, Threatening, Neuroticism-Depression, Neuroticism-Sarcastic, Psychopath and Extraversion of a person from social media images. The proposed method first finds facial key points for extracting features based on a face detection algorithm. Then the proposed method labels face regions as foreground and other than face region as background to define context between foreground and background information. To extract context, the proposed method explores Structural Function based Transform (SFBT) features, which study variations on pixel values. To increase discriminating power of the context features, the proposed method performs clustering to integrate the strength of the features. The extracted features are then fed to Support Vector Machines (SVM) for classification. Experimental results on a dataset of six classes show that the proposed method outperforms the existing methods in terms of confusion matrix and classification rate. © Springer Nature Switzerland AG 2020.

关键词： Classification (of information)

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Robust occupancy detection from stereo images

Robust occupancy detection from stereo images

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International Conference on Intelligent Transportation

作者： B. Alefs M. Clabian H. Bischof W. Kropatsch F. Khairallah Advanced Computer Vision GmbH-ACV Vienna Austria Advanced Computer Vision GmbH-ACV Austria Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Computer Science Department University of Technology Vienna Vienna Austria TRW Automotive GmbH Farmington Hills MI USA

Vehicle occupants that are out-of-position can be deadly injured by the deployment of the air bag in a crash situation. In recent years many different sensors and systems have been proposed to detect the type of occupant and the position of the occupant's head. This work presents a method for classification and occupant's head detection based on passive stereo vision. The proposed system uses depth surface analysis and scene statistics together with support vector machines for classification and selection of head candidates. Evaluation of the method shows 99% correct for classification and 98% correct for head detection, using large sets of image data, and image sequences recorded in a driving vehicle.

关键词： Robustness Magnetic heads Vehicle crash testing Sensor systems Stereo vision Layout Statistical analysis Support vector machines Support vector machine classification Vehicle detection

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Robust Text Line Detection in Equipment Nameplate Images*

Robust Text Line Detection in Equipment Nameplate Images*

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IEEE International Conference on Robotics and Biomimetics

作者： Jiangyu Lai Lanqing Guo Yu Qiao Xiaolong Chen Zhengfu Zhang Canping Liu Ying Li Bin Fu Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

Scene text detection for equipment nameplates in the wild is important for equipment inspection robot since it enables inspection robot to take specific actions for different equipment's. Although text detection in images has achieved great progress in recent years, the detection for equipment nameplates faces several challenges such as extreme illumination and distortion which significantly decrease the detection performance. In this paper, we propose a deep text detection model Robust Text Line Detection (RTLD) for locating word level text instances in equipment cards. Specifically, the proposed model first employs a corner detection module to determine the four corner points of each nameplate, and then a carefully designed image transformed module transforms the irregular nameplate region into a rectangular region. Finally, text detection module is introduced to locate every word level text instance in the transformed images. We conduct extensive experiments to examine our proposed methods on real equipment nameplate images. Our model achieves 91 . 2% precision and 92 . 6% recall on Equipment Nameplate Dataset. The experimental results demonstrate the effectiveness of our models.

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