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42nd german Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th International symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

42nd German Conference on Pattern Recognition, DAGM GCPR 202...

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42nd german Conference on pattern recognition, DAGM GCPR 2020 held in parallel with 25th International symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

ISBN: (纸本)9783030712778

the proceedings contain 34 papers. the special focus in this conference is on pattern recognition. the topics include: Adversarial Synthesis of Human Pose from Text;long-Term Anticipation of Activities with Cycle Consistency;multi-stage Fusion for One-Click Segmentation;neural Architecture Performance Prediction Using Graph Neural Networks;discovering Latent Classes for Semi-supervised Semantic Segmentation;Riemannian SOS-Polynomial Normalizing Flows;automated Water Segmentation and River Level Detection on Camera Images Using Transfer Learning;Does SGD Implicitly Optimize for Smoothness?;Looking Outside the Box: the Role of Context in Random Forest Based Semantic Segmentation of PolSAR Images;semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations;haar Wavelet Based Block Autoregressive Flows for Trajectories;center3D: Center-Based Monocular 3D Object Detection with Joint Depth Understanding;constellation Codebooks for Reliable Vehicle Localization;towards Bounding-Box Free Panoptic Segmentation;proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks;unsupervised Part Discovery by Unsupervised Disentanglement;on the Lifted Multicut Polytope for Trees;conditional Invertible Neural Networks for Diverse Image-to-Image Translation;image Inpainting with Learnable Feature Imputation;4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving;bias Detection and Prediction of Mapping Errors in Camera Calibration;inline Double Layer Depth Estimation with Transparent Materials;a Differentiable Convolutional Distance Transform Layer for Improved Image Segmentation;PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images;self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models;multimodal Semantic Forecasting Based on Conditional Generation of Future Features;learning to Identify Physical Parameters from Video Using Differentiable

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Machine Learning pattern recognition for Online Monitoring and Visualization in the SpinQuest Experiment 25

Machine Learning Pattern Recognition for Online Monitoring a...

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25th International symposium on Spin Physics, SPIN 2023

作者： Roberts, J. Keller, D. University of Virginia CharlottesvilleVA United States

SpinQuest will measure the sea quarks’ Sivers Asymmetry, a left-right asymmetry, with a target transversely polarized to the incoming 120 GeV proton beam. An online monitoring system has been developed to scan the polarized target system and polarization data while integrating information from detectors and event reconstruction for near-continuous quality checking of the incoming data. Online monitoring of the target system and detector package will play a vital role in ensuring optimal performance of the target while achieving the highest figure of merit possible given the experimental circumstances. this novel monitoring system enhances the debugging process during commissioning and data acquisition through the use of machine learning pattern recognition techniques and anomaly detection. the scheme outlined promises to aid target operations by ensuring data quality and getting issues addressed soon via a system of alarms during the two-year-long production runs to begin 2024 at Fermilab. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Machine learning

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MIPPR 2023: pattern recognition and Computer Vision

MIPPR 2023: Pattern Recognition and Computer Vision

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SPIE 12th International symposium on Multispectral Image Processing and pattern recognition, MIPPR 2023

ISBN: (纸本)9781510674950

the proceedings contain 25 papers. the topics discussed include: accurate extraction of line-structured optical stripe centerlines under low exposure;research on grid inspection technology based on general knowledge enhanced multi-modal large language models;a comparison between deep-learning models for scene recognition;binary network design for dedicated hardware circuits;evaluation of training datasets for typical target detection networks;optimal exposure selection of high dynamic range-based reflection compensation for printed circuit board reconstruction using structured light 3D measurement system;fall detection model based on Alphapose combined with LSTM and Lightgbm;and an image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors.

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Uncertainty Guided recognition of Tiny Craters on the Moon 25

Uncertainty Guided Recognition of Tiny Craters on the Moon

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25th International Conference on pattern recognition (ICPR)

作者： Wilhelm, thorsten Woehler, Christian TU Dortmund Univ Image Anal Grp Dortmund Germany

ISBN: (纸本)9781728188089

Accurately detecting craters in remotely sensed images is an important task when analysing the properties of planetary bodies. Commonly, only large craters in the range of several kilometres are detected. In this work we provide the first example of automatically detecting tiny craters in the range of several meters with the help of a deep neural network by using only a small set of annotated craters. Additionally, we propose a novel way to group overlapping detections and replace the commonly used non-maximum suppression with a probabilistic treatment. As a result, we receive valuable uncertainty estimates of the detections and the aggregated detections are shown to be vastly superior.

关键词： object detection uncertainty estimation

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An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers 25

An Evaluation of DNN Architectures for Page Segmentation of ...

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25th International Conference on pattern recognition (ICPR)

作者： Liebl, Bernhard Burghardt, Manuel Univ Leipzig Computat Humanities Grp Leipzig Germany

ISBN: (纸本)9781728188089

One important and particularly challenging step in the optical character recognition of historical documents with complex layouts, such as newspapers, is the separation of text from non-text content (e.g. page borders or illustrations). this step is commonly referred to as page segmentation. While various rule-based algorithms have been proposed, the applicability of Deep Neural Networks for this task recently has gained a lot of attention. In this paper, we perform a systematic evaluation of 11 different published backbone architectures and 9 different tiling and scaling configurations for separating text, tables or table column lines. We also show the influence of the number of labels and the number of training pages on the segmentation quality, which we measure using the Matthews Correlation Coefficient. Our results show that (depending on the task) Inception-ResNetv2 and EfficientNet backbones work best, vertical tiling is generally preferable to other tiling approaches, and training data that comprises 30 to 40 pages will be sufficient most of the time.

关键词： image segmentation neural networks optical character recognition software

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Self-supervised Detection and Pose Estimation of Logistical Objects in 3D Sensor Data 25

Self-supervised Detection and Pose Estimation of Logistical ...

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25th International Conference on pattern recognition (ICPR)

作者： Mueller, Nikolas Stenzel, Jonas Chen, Jian-Jia Plan Based Robot Control German Res Ctr Artificia Osnabruck Germany Fraunhofer Inst Mat Flow & Logist Automat & Embedded Syst Dortmund Germany Tech Univ Dortmund Inst Comp Sci Design Automat Embedded Syst Dortmund Germany

ISBN: (纸本)9781728188089

Localization of objects in cluttered scenes with machine learning methods is a fairly young research area. Despite the high potential of object localization for full process automation in Industry 4.0 and logistical environments, 3D data sets for such applications to train machine learning models are not openly available and only few publications have been made on that topic. To the authors knowledge, this is the first publication that describes a self-supervised and fully automated deep learning approach for object pose estimation using simulated 3D data. the solution covers the simulated generation of training data, the detection of objects in point clouds using a fully convolutional voting network and the computation of the pose for each detected object instance.

关键词： object detection pose estimation computer vision pattern recognition 3D vision learning-based vision

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Exploiting the Logits: Joint Sign Language recognition and Spell-Correction 25

Exploiting the Logits: Joint Sign Language Recognition and S...

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25th International Conference on pattern recognition (ICPR)

作者： Runkel, Christina Dorenkamp, Stefan Bauermeister, Hartmut Moeller, Michael Univ Siegen Dept Comp Sci & Elect Engn Siegen Germany

ISBN: (纸本)9781728188089

Machine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of videos remains challenging due to the significantly higher dimensionality of the input data, respectively, the significantly higher need for annotated training examples. By studying the automatic recognition of german sign language videos, we demonstrate that on the relatively scarce training data of 2.800 videos, modern deep learning architectures for video analysis (such as ResNeXt) along with transfer learning on large gesture recognition tasks, can achieve about 75% character accuracy. Considering that this leaves us with a probability of under 25% that a 5 letter word is spelled correctly, spell-correction systems are crucial for producing readable outputs. the contribution of this paper is to propose a convolutional neural network for spell-correction that expects the softmax outputs of the character recognition network (instead of a misspelled word) as an input. We demonstrate that purely learning on softmax inputs in combination with scarce training data yields overfitting as the network learns the inputs by heart. In contrast, training the network on several variants of the logits of the classification output i.e. scaling by a constant factor, adding of random noise, mixing of softmax and hardmax inputs or purely training on hardmax inputs, leads to better generalization while benefitting from the significant information hidden in these outputs (that have 98% top-5 accuracy), yielding a readable text despite the comparably low character accuracy.

关键词： Character recognition

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PWS: Potential Wafermap Scratch Defect pattern recognition with Machine Learning Techniques

PWS: Potential Wafermap Scratch Defect Pattern Recognition w...

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25th IEEE European Test symposium (ETS)

作者： Li, Katherine Shu-Min Liao, Peter Yi-Yu Chou, Leon Chen, Ken Chau-Cheung Huang, Andrew Yi-Ann Wang, Sying-Jyan Han, Gus Chang-Hung Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan NXP Semicond Taiwan Ltd Kaohsiung Taiwan Natl Chung Hsing Univ Dept Comp Sci & Engn Taichung Taiwan

ISBN: (数字)9781728143125

ISBN: (纸本)9781728143125

Wafermap defect pattern detection and diagnosis provide useful clue to yield learning. However, most wafermaps have no special spatial patterns and are full of noises, which make pattern recognition difficult. Specially, recognizing scratch and line types of defect patterns is a challenging problem for process and test engineers and it takes a lot of manpower to identify such patterns, as potential defective dies may exist on the scratch contour and become discontinuity points. However, such potential defective dies may suffer from latent and leakage faults, which usually deteriorate quickly and need to be screened by burn-in test to improve quality. A possible solution is to locate the obscure defective dies in potential scratch patterns and mark them as faulty. As a result, the quality and reliability of products can be significantly improved and cost of final test can be reduced. In this paper, we propose a systematic methodology to search for potential scratch/line defect types in wafers. A five-phase method is developed to enhance wafermaps such that automatic defect pattern recognition can be carried with high accuracy. Experimental results show the proposed method can achieve more than 89% prediction accuracy for scratch/line types, and higher than 94% for all common wafer defect types.

关键词： wafermap defect pattern yield learning clustering machine learning

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Cross-Lingual Text Image recognition via Multi-Task Sequence to Sequence Learning 25

Cross-Lingual Text Image Recognition via Multi-Task Sequence...

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25th International Conference on pattern recognition (ICPR)

作者： Chen, Zhuo Yin, Fei Zhang, Xu-Yao Yang, Qing Liu, Chena-Lin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit NLPR 95 Zhongguan East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781728188089

this paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a Bidirectional Long Short-Term Memory (BLSTM) model with Connectionist Temporal Classification (CTC), are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.

关键词： Learning systems

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Detection of Makeup Presentation Attacks based on Deep Face Representations 25

Detection of Makeup Presentation Attacks based on Deep Face ...

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25th International Conference on pattern recognition (ICPR)

作者： Rathgeb, C. Drozdowski, P. Busch, C. Hsch Darmstadt Da Sec Biometr & Internet Secur Res Grp Darmstadt Germany

ISBN: (纸本)9781728188089

Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spooling (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. the proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.

关键词： Biometrics face recognition presentation attack detection makeup attack detection deep face representation

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