International Conference on Pattern Recognition Applications and Methods最新文献_第10页

Accelerated Algorithm for Computation of All Prime Patterns in Logical Analysis of Data 数据逻辑分析中所有素数模式计算的加速算法

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007389702100220

Arthur Chambon, F. Lardeux, F. Saubion, T. Boureau

引用次数: 3

Enforcing the General Planar Motion Model: Bundle Adjustment for Planar Scenes 执行一般平面运动模型:平面场景的束调整

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.1007/978-3-030-40014-9_6

Marcus Valtonen Örnhag, Mårten Wadenbäck

引用次数: 2

uPAD: Unsupervised Privacy-Aware Anomaly Detection in High Performance Computing Systems 高性能计算系统中的无监督隐私感知异常检测

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007582208520859

Siavash Ghiasvand

{"title":"uPAD: Unsupervised Privacy-Aware Anomaly Detection in High Performance Computing Systems","authors":"Siavash Ghiasvand","doi":"10.5220/0007582208520859","DOIUrl":"https://doi.org/10.5220/0007582208520859","url":null,"abstract":"Rapid growing complexity of HPC systems in response to demand for higher computing performance, results in higher probability of failures. Early detection of failures significantly reduces the damages caused by failure via impeding their propagation through system. Various anomaly detection mechanism are proposed to detect failures in their early stages. Insufficient amount of failure samples in addition to privacy concerns extremely limits the functionality of available anomaly detection approaches. Advances in machine learning techniques, significantly increased the accuracy of unsupervised anomaly detection methods, addressing the challenge of insufficient failure samples. However, available approaches are either domain specific, inaccurate, or require comprehensive knowledge about the underlying system. Furthermore, processing certain monitoring data such as system logs raises high privacy concerns. In addition, noises in monitoring data severely impact the correctness of data analysis. This work proposes an unsupervised and privacy-aware approach for detecting abnormal behaviors in general HPC systems. Preliminary results indicate high potentials of autoencoders for automatic detection of abnormal behaviors in HPC systems via analyzing anonymized system logs using fast-trainable noise-resistant models.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116431611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Unsupervised Image Segmentation using Convolutional Neural Networks for Automated Crop Monitoring 基于卷积神经网络的无监督图像分割用于作物自动监测

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007687508870893

Prakruti V. Bhatt, Sanat Sarangi, S. Pappula

{"title":"Unsupervised Image Segmentation using Convolutional Neural Networks for Automated Crop Monitoring","authors":"Prakruti V. Bhatt, Sanat Sarangi, S. Pappula","doi":"10.5220/0007687508870893","DOIUrl":"https://doi.org/10.5220/0007687508870893","url":null,"abstract":"Among endeavors towards automation in agriculture, localization and segmentation of various events during the growth cycle of a crop is critical and can be challenging in a dense foliage. Convolutional Neural Network based methods have been used to achieve state-of-the-art results in supervised image segmentation. In this paper, we investigate the unsupervised method of segmentation for monitoring crop growth and health conditions. Individual segments are then evaluated for their size, color, and texture in order to measure the possible change in the crop like emergence of a flower, fruit, deficiency, disease or pest. Supervised methods require ground truth labels of the segments in a large number of the images for training a neural network which can be used for similar kind of images on which the network is trained. Instead, we use information of spatial continuity in pixels and boundaries in a given image to update the feature representation and label assignment to every pixel using a fully convolutional network. Given that manual labeling of crop images is time consuming but quantifying an event occurrence in the farm is of utmost importance, our proposed approach achieves promising results on images of crops captured in different conditions. We obtained 94% accuracy in segmenting Cabbage with Black Moth pest, 81% in getting segments affected by Helopeltis pest on Tea leaves and 92% in spotting fruits on a Citrus tree where accuracy is defined in terms of intersection over union of the resulting segments with the ground truth. The resulting segments have been used for temporal crop monitoring and severity measurement in case of disease or pest manifestations.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123125450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Adapting YOLO Network for Ball and Player Detection 基于YOLO网络的球和球员检测

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007582008450851

Matija Buric, M. Pobar, Marina Ivasic-Kos

{"title":"Adapting YOLO Network for Ball and Player Detection","authors":"Matija Buric, M. Pobar, Marina Ivasic-Kos","doi":"10.5220/0007582008450851","DOIUrl":"https://doi.org/10.5220/0007582008450851","url":null,"abstract":"In this paper, we consider the task of detecting the players and sports balls in real-world handball images, as a building block for action recognition. Detecting the ball is still a challenge because it is a very small object that takes only a few pixels in the image but carries a lot of information relevant to the interpretation of scenes. Balls can vary greatly regarding color and appearance due to various distances to the camera and motion blur. Occlusion is also present, especially as handball players carry the ball in their hands during the game and it is understood that the player with the ball is a key player for the current action. Handball players are located at different distances from the camera, often occluded and have a posture that differs from ordinary activities for which most object detectors are commonly learned. We compare the performance of 6 models based on the YOLOv2 object detector, trained on an image dataset of publicly available sports images and images from custom handball recordings. The performance of a person and ball detection is measured on the whole dataset and the custom part regarding mean average precision metric.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130415880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

A Framework for Discovering Frequent Event Graphs from Uncertain Event-based Spatio-temporal Data 从不确定事件时空数据中发现频繁事件图的框架

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007411206560663

P. Maciag

引用次数: 0

Attributes for Understanding Groups of Binary Data 用于理解二进制数据组的属性

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.1007/978-3-030-40014-9_3

Arthur Chambon, F. Lardeux, F. Saubion, T. Boureau

引用次数: 1

Image-based Discrimination and Spatial Non-uniformity Analysis of Effect Coatings 基于图像的效果涂层空间非均匀性判别与分析

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007413906830690

J. Filip, R. Vávra, F. Maile, Bill Eibon

{"title":"Image-based Discrimination and Spatial Non-uniformity Analysis of Effect Coatings","authors":"J. Filip, R. Vávra, F. Maile, Bill Eibon","doi":"10.5220/0007413906830690","DOIUrl":"https://doi.org/10.5220/0007413906830690","url":null,"abstract":"Various industries are striving for novel, more reliable but still efficient approaches to coatings characterization. Majority of industrial applications use portable instruments for characterization of effect coatings. They typically capture a limited set of in-plane geometries and have limited ability to reliably characterize gonio-apparent behavior typical for such coatings. The instruments rely mostly on color and reflectance characteristics without using a texture information across the coating plane. In this paper, we propose image-based method that counts numbers of effective pigments and their active area. First, we captured appearance of eight effect coatings featuring four different pigment materials, in in-plane and out-of-plane geometries. We used a gonioreflectometer for fixed viewing and varying illumination angles. Our analysis has shown that the proposed method is able to clearly distinguish pigment materials and coating applications in both in-plane and out-of-plane geometries. Finally, we show an application of our method to analysis of spatial non-uniformity, i.e. cloudiness or mottling, across a coated panel.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Detecting and Tracking Surgical Tools for Recognizing Phases of the Awake Brain Tumor Removal Surgery 用于清醒脑肿瘤切除手术阶段识别的检测和跟踪手术工具

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007385701900199

Hiroki Fujie, Keiju Hirata, T. Horigome, H. Nagahashi, J. Ohya, M. Tamura, K. Masamune, Y. Muragaki

引用次数: 1

Mixture of Multilayer Perceptron Regressions 多层感知器回归的混合

International Conference on Pattern Recognition Applications and Methods Pub Date : 2019-02-19 DOI: 10.5220/0007367405090516

R. Nakano, Seiya Satoh

引用次数: 0