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Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows 1

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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

作者： Draxler, Felix Schwarz, Jonathan Schnörr, Christoph Köthe, Ullrich Heidelberg Collaboratory for Image Processing Heidelberg University Heidelberg Germany Visual Learning Lab Heidelberg University Heidelberg Germany Image and Pattern Analysis Group Heidelberg University Heidelberg Germany

ISBN: (数字)9783030712785

ISBN: (纸本)9783030712778

Deep Affine Normalizing Flows are efficient and powerful models for high-dimensional density estimation and sample generation. Yet little is known about how they succeed in approximating complex distributions, given the seemingly limited expressiveness of individual affine layers. In this work, we take a first step towards theoretical understanding by analyzing the behaviour of a single affine coupling layer under maximum likelihood loss. We show that such a layer estimates and normalizes conditional moments of the data distribution, and derive a tight lower bound on the loss depending on the orthogonal transformation of the data before the affine coupling. this bound can be used to identify the optimal orthogonal transform, yielding a layer-wise training algorithm for deep affine flows. Toy examples confirm our findings and stimulate further research by highlighting the remaining gap between layer-wise and end-to-end training of deep affine flows. © 2021, Springer Nature Switzerland AG.

关键词： Maximum likelihood estimation

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Personalized k-fold Cross-Validation Analysis with Transfer from Phasic to Tonic Pain recognition on X-ITE Pain Database 25th

Personalized k-fold Cross-Validation Analysis with Transfer ...

引用

25th International Conference on pattern recognition Workshops, ICPR 2020

作者： Wally, Youssef Samaha, Yara Yasser, Ziad Walter, Steffen Schwenker, Friedhelm Ulm University Ulm89081 Germany German University in Cairo Main Entrance El-Tagamoa El-Khames New Cairo City Egypt

ISBN: (纸本)9783030687793

Automatic pain recognition is currently one of the most interesting challenges in affective computing as it has great potential to improve pain management for patients in a clinical environment. In this work, we analyse automatic pain recognition using binary classification with personalized k-fold cross-validation analysis which is an approach that focuses on training on the labels of specific subjects and validating on the labels of other subjects in the Experimentally Induced thermal and Electrical (X-ITE) Pain Database using both a random forest and a dense neural network model. the effectiveness of each approach is inspected on each of the phasic electro, phasic heat, tonic electro, and tonic heat datasets separately. therefore, the analysis of subsets of the X-ITE dataset (phasic electro, tonic electro, phasic heat, and tonic heat) was made individually. Afterward, phasic to tonic transfer was made by training models on the phasic electro dataset and testing them on the tonic electro dataset. Our outcomes and evaluations indicate that electro datasets always perform better than heat datasets. Also, personalized scores had better performance than normal scores. Moreover, dense neural networks performed better than randoms forests in transfer from phasic electro to tonic electro and showed promising performance in the personalized transfer. © 2021, Springer Nature Switzerland AG.

关键词： Decision trees

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Generalization of Fitness Exercise recognition from Doppler Measurements by Domain-Adaption and Few-Shot Learning 25th

Generalization of Fitness Exercise Recognition from Doppler ...

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25th International Conference on pattern recognition Workshops, ICPR 2020

作者： Fu, Biying Damer, Naser Kirchbuchner, Florian Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Fraunhoferstr. 5 Darmstadt64283 Germany Mathematical and Applied Visual Computing TU Darmstadt Darmstadt Germany

ISBN: (纸本)9783030687984

In previous works, a mobile application was developed using an unmodified commercial smartphone to recognize whole-body exercises. the working principle was based on the ultrasound Doppler sensing with the device built-in hardware. Applying such a lab environment trained model on realistic application variations causes a significant drop in performance, and thus decimate its applicability. the reason of the reduced performance can be manifold. It could be induced by the user, environment, and device variations in realistic scenarios. Such scenarios are often more complex and diverse, which can be challenging to anticipate in the initial training data. To study and overcome this issue, this paper presents a database with controlled and uncontrolled subsets of fitness exercises. We propose two concepts to utilize small adaption data to successfully improve model generalization in an uncontrolled environment, increasing the recognition accuracy by two to six folds compared to the baseline for different users. © 2021, Springer Nature Switzerland AG.

关键词： Ultrasonics

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Monitoring Current Variation using pattern recognition Algorithm Implemented in Automotive DC Driving Servomechanism 25

Monitoring Current Variation using Pattern Recognition Algor...

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IEEE 25th International symposium for Design and Technology in Electronic Packaging (SIITME)

作者： Nistor, N. Baicu, L. Dumitrascu, B. Dunarea de Jos Univ Galati Dept Elect & Telecommun Galati Romania

ISBN: (纸本)9781728133300

In this paper the authors consider the problem monitoring of the drawn current by the main power rail that powers multiple consumers (servomechanisms, actuators, electromagnets, stepper or DC motors) present in cars, in order to protect the equipment present on board a car or to increase the reliability of electromechanical systems. the idea is to scan the supply current of a device connected to the main power rail using a very sensitive shunt and to store this data using a development board with a microcontroller and implementing maps of the currents that will be supplied to specific consumers. the board operates in two stages. the first stage involves memorizing the shape currents and storing the data in a memory zone. the second stage assists the current utilization of the equipment by using a software block to recognize the shape of the current. recognition of the current shape and classification of the current event into nominal or faulty operation will be done using byte oriented comparisons initially and using a variable error filter afterwards using "running average" which will be presented below. Preliminary results were done on a 16-bit microcontroller.

关键词： Current monitoring running average signal synchronization prototype signal form recognition servomechanism microcontroller process

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Deep Learning-Based Semantic Segmentation for Touchless Fingerprint recognition 25th

Deep Learning-Based Semantic Segmentation for Touchless Fing...

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25th International Conference on pattern recognition Workshops, ICPR 2020

作者： Priesnitz, Jannis Rathgeb, Christian Buchmann, Nicolas Busch, Christoph da/sec - Biometrics and Internet Security Research Group Hochschule Darmstadt Schöfferstraße 8b Darmstadt64295 Germany Freie Universität Berlin Takustraße 9 Berlin14195 Germany

ISBN: (纸本)9783030687922

Fingerprint recognition is one of the most popular biometric technologies. Touchless fingerprint systems do not require contact of the finger with the surface of a capture device. For this reason, they provide an increased level of hygiene, usability, and user acceptance compared to touch-based capturing technologies. Most processing steps of the recognition workflow of touchless recognition systems differ in comparison to touch-based biometric techniques. Especially the segmentation of the fingerprint areas in a 2D capturing process is a crucial and more challenging task. In this work a proposal of a fingertip segmentation using deep learning techniques is presented. the proposed system allows to submit the segmented fingertip areas from a finger image directly to the processing pipeline. To this end, we adapt the deep learning model DeepLabv3+ to the requirements of fingertip segmentation and trained it on the database for hand gesture recognition (HGR) by extending it with a fingertip ground truth. Our system is benchmarked against a well-established color-based baseline approach and shows more accurate hand segmentation results especially on challenging images. Further, the segmentation performance on fingertips is evaluated in detail. the gestures provided in the database are separated into three categories by their relevance for the use case of touchless fingerprint recognition. the segmentation performance in terms of Intersection over Union (IoU) of up to 68.03% on the fingertips (overall: 86.13%) in the most relevant category confirms the soundness of the presented approach. © 2021, Springer Nature Switzerland AG.

关键词： Biometrics

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Tackling Contradiction Detection in german Using Machine Translation and End-to-End Recurrent Neural Networks

Tackling Contradiction Detection in German Using Machine Tra...

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International Conference on pattern recognition

作者： Maren Pielka Rafet Sifa Lars Patrick Hillebrand David Biesner Rajkumar Ramamurthy Anna Ladi Christian Bauckhage Fraunhofer Center for Machine Learning Fraunhofer IAIS University of Bonn Sankt Augustin Germany

Natural Language Inference, and specifically Contradiction Detection, is still an unexplored topic with respect to german text. In this paper, we apply Recurrent Neural Network (RNN) methods to learn contradiction-specific sentence embeddings. Our data set for evaluation is a machine-translated version of the Stanford Natural Language Inference (SNLI) corpus. the results are compared to a baseline using unsupervised vectorization techniques, namely tf-idf and Flair, as well as state-of-the art transformer-based (MBERT) methods. We find that the end-to-end models outperform the models trained on unsupervised embeddings, which makes them the better choice in an empirical use case. the RNN methods also perform superior to MBERT on the translated data set.

关键词： Recurrent neural networks Art Natural languages pattern recognition Machine translation

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Decision Snippet Features

Decision Snippet Features

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International Conference on pattern recognition

作者： Pascal Welke Fouad Alkhoury Christian Bauckhage Stefan Wrobel University of Bonn University of Bonn Fraunhofer IAIS

Decision trees excel at interpretability of their prediction results. To achieve required prediction accuracies, however, often large ensembles of decision trees - random forests - are considered, reducing interpretability due to large size. Additionally, their size slows down inference on modern hardware and restricts their applicability in low-memory embedded devices. We introduce Decision Snippet Features, which are obtained from small subtrees that appear frequently in trained random forests. We subsequently show that linear models on top of these features achieve comparable and sometimes even better predictive performance than the original random forest, while reducing the model size by up to two orders of magnitude.

关键词： Performance evaluation Predictive models Hardware pattern recognition Decision trees Random forests

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Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches

Learning Embeddings for Image Clustering: An Empirical Study...

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International Conference on pattern recognition

作者： Kalun Ho Janis Keuper Franz-Josef Pfreundt Margret Keuper Data and Web Science Group University of Mannheim Germany Institute for Machine Learning and Analytics (IMLA) Offenburg University Germany Competence Center High Performance Computing Fraunhofer ITWM Kaiserslautern Germany

In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings. Specifically, we train a convolutional neural network to learn discriminative features by optimizing two popular versions of the Triplet Loss in order to study their clustering properties under the assumption of noisy labels. Additionally, we propose a new, simple Triplet Loss formulation, which shows desirable properties with respect to formal clustering objectives and outperforms the existing methods. We evaluate all three Triplet loss formulations for K-means and correlation clustering on the CIFAR-10 image classification dataset.

关键词： Correlation pattern recognition Noise measurement Convolutional neural networks Image classification

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Robust Skeletonization for Plant Root Structure Reconstruction from MRI

Robust Skeletonization for Plant Root Structure Reconstructi...

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International Conference on pattern recognition

作者： Jannis Horn Yi Zhao Nils Wandel Magdalena Landl Andrea Schnepf Sven Behnke Autonomous Intelligence Systems Group University of Bonn Porschungszentrum Jülich GmbH Agrosphere (IBG-3)

Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities and wrongly connected roots. We propose a two-stage approach for this task. the first stage is based on semantic root vs. soil segmentation and finds lowest-cost paths from any root voxel to the shoot. the second stage takes the largest fully connected component generated in the first stage and uses 3D skeletonization to extract a graph structure. We evaluate our method on 22 MRI scans and compare to human expert reconstructions.

关键词： three-dimensional displays Soil measurements Magnetic resonance imaging Semantics Soil pattern recognition Task analysis

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Development of Gait Classification System Using Six-Axis Inertial Sensors by CNN-LSTM

Development of Gait Classification System Using Six-Axis Ine...

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International symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Shotaro Ohno Kouki Nagamune Graduated School of Engineering University of Fukui Fukui Japan Graduated School of Engineering University of Hyogo Hyogo Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Restoration of walking function is the most important goal of rehabilitation. However, the evaluation of post-stroke rehabilitation lacks quantitative elements. In this study, we used an IMU to acquire the angles of three axes (rotation of x-axis, rotation of y-axis, and rotation of z-axis) of the knee during walking motion as gait parameters. Using this data, machine learning analysis is performed. In this article, we discuss the gait parameters, the angular data of the three axes of the knee, and examine which of the three angular parameters increases or de-creases the generalization ability of the model. As a result, we identify the gait parameters to be used as a dataset. Time series data of the knee angle of one step in the gait measurement was used as the dataset. CNN - LSTM algorithm was used to construct the model. Five subjects were measured with the knee joint motion measurement device attached to both knees. A total of four types of gait movements, including normal gait and three types of gait movements that reproduce hemiplegia, were meas-ured for analysis. Out of one hundred thirty-six steps of data from five subjects, four subjects were used as training data and one unknown subject's data, which was not used during training, was applied to the test data. the results of the prediction of the estimated data show that rotation of z-axis was a parameter that reduced the generalization ability of the model, while rotation of y-axis was a parameter that increased the generalization ability of the model. Prediction results for the rotation of x-axis data alone showed poor accuracy, but when combined with the rotation of y-axis data, the generalization ability was improved.

关键词： Knee Legged locomotion Training Inertial sensors Time series analysis Training data Predictive models Data models pattern recognition Motion measurement

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