Pub Date : 2019-02-19DOI: 10.5220/0007389702100220
Arthur Chambon, F. Lardeux, F. Saubion, T. Boureau
The analysis of groups of binary data can be achieved by logical based approaches. These approaches identify subsets of relevant Boolean variables to characterize observations and may help the user to better understand their properties. In logical analysis of data, given two groups of data, patterns of Boolean values are used to discriminate observations in these groups. In this work, our purpose is to highlight that different techniques may be used to compute these patterns. We present a new approach to compute prime patterns that do not provide redundant information. Experiments are conducted on real biological data.
{"title":"Accelerated Algorithm for Computation of All Prime Patterns in Logical Analysis of Data","authors":"Arthur Chambon, F. Lardeux, F. Saubion, T. Boureau","doi":"10.5220/0007389702100220","DOIUrl":"https://doi.org/10.5220/0007389702100220","url":null,"abstract":"The analysis of groups of binary data can be achieved by logical based approaches. These approaches identify subsets of relevant Boolean variables to characterize observations and may help the user to better understand their properties. In logical analysis of data, given two groups of data, patterns of Boolean values are used to discriminate observations in these groups. In this work, our purpose is to highlight that different techniques may be used to compute these patterns. We present a new approach to compute prime patterns that do not provide redundant information. Experiments are conducted on real biological data.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"21 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":"128748613","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}
Pub Date : 2019-02-19DOI: 10.1007/978-3-030-40014-9_6
Marcus Valtonen Örnhag, Mårten Wadenbäck
{"title":"Enforcing the General Planar Motion Model: Bundle Adjustment for Planar Scenes","authors":"Marcus Valtonen Örnhag, Mårten Wadenbäck","doi":"10.1007/978-3-030-40014-9_6","DOIUrl":"https://doi.org/10.1007/978-3-030-40014-9_6","url":null,"abstract":"","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"1 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":"131131528","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}
Pub Date : 2019-02-19DOI: 10.5220/0007582208520859
Siavash Ghiasvand
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.
{"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}
Pub Date : 2019-02-19DOI: 10.5220/0007687508870893
Prakruti V. Bhatt, Sanat Sarangi, S. Pappula
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.
{"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}
Pub Date : 2019-02-19DOI: 10.5220/0007582008450851
Matija Buric, M. Pobar, Marina Ivasic-Kos
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.
{"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}
Pub Date : 2019-02-19DOI: 10.5220/0007411206560663
P. Maciag
The aim of this paper is to discuss a novel framework designed for discovering frequent event graphs from uncertain spatio-temporal data. We consider the problem of discovering hidden relations between event types and their set of uncertain spatio-temporal instances. For that purpose, we designed the following data mining framework: microclustering of uncertain instances, generating set of possible worlds according to the possible worlds semantic technique, creating a microclustering index for each world, generating a set of event graphs from created microclusters and defining apriori based algorithm mining frequent event graphs (EventGraph Miner). To the best of our knowledge this is the first approach to discover hidden patterns from event-type spatio-temporal data when dataset contains uncertain instances. While the paper does not present experimental results for the proposed framework, it presents its potential for futher studies in the topic.
{"title":"A Framework for Discovering Frequent Event Graphs from Uncertain Event-based Spatio-temporal Data","authors":"P. Maciag","doi":"10.5220/0007411206560663","DOIUrl":"https://doi.org/10.5220/0007411206560663","url":null,"abstract":"The aim of this paper is to discuss a novel framework designed for discovering frequent event graphs from uncertain spatio-temporal data. We consider the problem of discovering hidden relations between event types and their set of uncertain spatio-temporal instances. For that purpose, we designed the following data mining framework: microclustering of uncertain instances, generating set of possible worlds according to the possible worlds semantic technique, creating a microclustering index for each world, generating a set of event graphs from created microclusters and defining apriori based algorithm mining frequent event graphs (EventGraph Miner). To the best of our knowledge this is the first approach to discover hidden patterns from event-type spatio-temporal data when dataset contains uncertain instances. While the paper does not present experimental results for the proposed framework, it presents its potential for futher studies in the topic.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"128 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":"130632287","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}
Pub Date : 2019-02-19DOI: 10.1007/978-3-030-40014-9_3
Arthur Chambon, F. Lardeux, F. Saubion, T. Boureau
{"title":"Attributes for Understanding Groups of Binary Data","authors":"Arthur Chambon, F. Lardeux, F. Saubion, T. Boureau","doi":"10.1007/978-3-030-40014-9_3","DOIUrl":"https://doi.org/10.1007/978-3-030-40014-9_3","url":null,"abstract":"","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"59 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":"114573528","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}
Pub Date : 2019-02-19DOI: 10.5220/0007413906830690
J. Filip, R. Vávra, F. Maile, Bill Eibon
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.
{"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}
Pub Date : 2019-02-19DOI: 10.5220/0007385701900199
Hiroki Fujie, Keiju Hirata, T. Horigome, H. Nagahashi, J. Ohya, M. Tamura, K. Masamune, Y. Muragaki
In order to realize automatic recognition of surgical processes in surgical brain tumor removal using microscopic camera, we propose a method of detecting and tracking surgical tools by video analysis. The proposed method consists of a detection part and tracking part. In the detection part, object detection is performed for each frame of surgery video, and the category and bounding box are acquired frame by frame. The convolution layer strengthens the robustness using data augmentation (central cropping and random erasing). The tracking part uses SORT, which predicts and updates the acquired bounding box corrected by using Kalman Filter; next, the object ID is assigned to each corrected bounding box using the Hungarian algorithm. The accuracy of our proposed method is very high as follows. As a result of experiments on spatial detection. the mean average precision is 90.58%. the mean accuracy of frame label detection is 96.58%. These results are very promising for surgical phase recognition.
{"title":"Detecting and Tracking Surgical Tools for Recognizing Phases of the Awake Brain Tumor Removal Surgery","authors":"Hiroki Fujie, Keiju Hirata, T. Horigome, H. Nagahashi, J. Ohya, M. Tamura, K. Masamune, Y. Muragaki","doi":"10.5220/0007385701900199","DOIUrl":"https://doi.org/10.5220/0007385701900199","url":null,"abstract":"In order to realize automatic recognition of surgical processes in surgical brain tumor removal using microscopic camera, we propose a method of detecting and tracking surgical tools by video analysis. The proposed method consists of a detection part and tracking part. In the detection part, object detection is performed for each frame of surgery video, and the category and bounding box are acquired frame by frame. The convolution layer strengthens the robustness using data augmentation (central cropping and random erasing). The tracking part uses SORT, which predicts and updates the acquired bounding box corrected by using Kalman Filter; next, the object ID is assigned to each corrected bounding box using the Hungarian algorithm. The accuracy of our proposed method is very high as follows. As a result of experiments on spatial detection. the mean average precision is 90.58%. the mean accuracy of frame label detection is 96.58%. These results are very promising for surgical phase recognition.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"12 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":"121995479","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}
Pub Date : 2019-02-19DOI: 10.5220/0007367405090516
R. Nakano, Seiya Satoh
This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mixture approach and use the Expectation-Maximization (EM) algorithm as a basic learning method. Our learning method goes in a double-looped manner; the outer loop is controlled by the EM and the inner loop by MLP learning method. Given data, we will have many models; thus, we need a criterion to select the best. Bayesian Information Criterion (BIC) is used here because it works nicely for MLP model selection. Our experiments showed that the proposed MoMR method found the expected MoMR model as the best for artificial data and selected the MoMR model having smaller error than any linear models for real noisy data.
{"title":"Mixture of Multilayer Perceptron Regressions","authors":"R. Nakano, Seiya Satoh","doi":"10.5220/0007367405090516","DOIUrl":"https://doi.org/10.5220/0007367405090516","url":null,"abstract":"This paper investigates mixture of multilayer perceptron (MLP) regressions. Although mixture of MLP regressions (MoMR) can be a strong fitting model for noisy data, the research on it has been rare. We employ soft mixture approach and use the Expectation-Maximization (EM) algorithm as a basic learning method. Our learning method goes in a double-looped manner; the outer loop is controlled by the EM and the inner loop by MLP learning method. Given data, we will have many models; thus, we need a criterion to select the best. Bayesian Information Criterion (BIC) is used here because it works nicely for MLP model selection. Our experiments showed that the proposed MoMR method found the expected MoMR model as the best for artificial data and selected the MoMR model having smaller error than any linear models for real noisy data.","PeriodicalId":410036,"journal":{"name":"International Conference on Pattern Recognition Applications and Methods","volume":"1 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":"129761452","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}
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