In entity name disambiguation, performance evaluation of any approach is difficult. This is due to the fact that correct or actual results are often not known. Generally for evaluation purpose, three measures namely precision, recall and f-measure are used. They all are external validity indices because they need golden standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science, Google Scholar, etc., gold standard data is not easily available and it is very difficult to obtain this due to the overlapping nature of data. So, there is a need to use some other matrices for evaluation purpose. In this paper, some internal cluster validity index based schemes are proposed for evaluating entity name disambiguation algorithms when applied on bibliographic data without using any gold standard datasets. Two new internal validity indices are also proposed in the current paper for this purpose. Experimental results shown on seven bibliographic datasets reveal that proposed internal cluster validity indices are able to compare the results obtained by different methods without prior/gold standard. Thus the present paper demonstrates a novel way of evaluating any entity matching algorithm for bibliographic datasets without using any prior/gold standard information.
在实体名称消歧中，任何一种方法的性能评价都是困难的。这是因为正确或实际的结果往往是未知的。一般来说，为了评价目的，使用三个指标，即精度、召回率和f-measure。它们都是外部有效性指标，因为它们需要黄金标准数据。但在DBLP、Arnetminer、Scopus、Web of Science、Google Scholar等书目数据库中，黄金标准数据并不容易获得，而且由于数据的重叠性质，很难获得黄金标准数据。因此，有必要使用一些其他的矩阵来求值。本文在不使用任何金标准数据集的情况下，提出了一些基于内部聚类有效性索引的评价书目数据实体名消歧算法的方案。为此，本文还提出了两个新的内部效度指标。在7个文献数据集上的实验结果表明，本文提出的内部聚类效度指标能够比较不同方法的结果，而不需要先验标准或金标准。因此，本文展示了一种新的方法来评估书目数据集的任何实体匹配算法，而不使用任何先验/金标准信息。
{"title":"On Validation of Clustering Techniques for Bibliographic Databases","authors":"Sumit Mishra, S. Saha, S. Mondal","doi":"10.1109/ICPR.2014.543","DOIUrl":"https://doi.org/10.1109/ICPR.2014.543","url":null,"abstract":"In entity name disambiguation, performance evaluation of any approach is difficult. This is due to the fact that correct or actual results are often not known. Generally for evaluation purpose, three measures namely precision, recall and f-measure are used. They all are external validity indices because they need golden standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science, Google Scholar, etc., gold standard data is not easily available and it is very difficult to obtain this due to the overlapping nature of data. So, there is a need to use some other matrices for evaluation purpose. In this paper, some internal cluster validity index based schemes are proposed for evaluating entity name disambiguation algorithms when applied on bibliographic data without using any gold standard datasets. Two new internal validity indices are also proposed in the current paper for this purpose. Experimental results shown on seven bibliographic datasets reveal that proposed internal cluster validity indices are able to compare the results obtained by different methods without prior/gold standard. Thus the present paper demonstrates a novel way of evaluating any entity matching algorithm for bibliographic datasets without using any prior/gold standard information.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125519907","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}
In this work we present a system that enables automatic estimation of Pain from image sequences with frontal views of faces. The system uses facial characteristic points to characterize different Action Units (AU) of pain and is able to operate in cluttered and dynamic scenes. Geometric features are computed using 22 facial characteristic points. We use k-NN classifier for classifying AU. Only action units relevant to pain are classified. Validation studies are done on UNBC McMaster Shoulder Pain Archive Database [8]. We also classify action unit intensities for evaluating pain intensity on a 16 point scale. Our system is simpler in design compared to the already reported works in literature. Our system reports AU intensities on a standard scale and also reports pain intensity to assess pain. We have achieved more than 84% accuracy for AU intensity levels and 87.4% area under ROC curve for pain assessment as compared to 84% of state-of-the-art scheme.
{"title":"Pain Intensity Evaluation through Facial Action Units","authors":"Zuhair Zafar, N. Khan","doi":"10.1109/ICPR.2014.803","DOIUrl":"https://doi.org/10.1109/ICPR.2014.803","url":null,"abstract":"In this work we present a system that enables automatic estimation of Pain from image sequences with frontal views of faces. The system uses facial characteristic points to characterize different Action Units (AU) of pain and is able to operate in cluttered and dynamic scenes. Geometric features are computed using 22 facial characteristic points. We use k-NN classifier for classifying AU. Only action units relevant to pain are classified. Validation studies are done on UNBC McMaster Shoulder Pain Archive Database [8]. We also classify action unit intensities for evaluating pain intensity on a 16 point scale. Our system is simpler in design compared to the already reported works in literature. Our system reports AU intensities on a standard scale and also reports pain intensity to assess pain. We have achieved more than 84% accuracy for AU intensity levels and 87.4% area under ROC curve for pain assessment as compared to 84% of state-of-the-art scheme.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"388 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116011293","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}
A novel region based 3D semantic mapping method is proposed for urban scenes. The proposed Semantic Urban Maps (SUM) method labels the regions of segmented images into a set of geometric and semantic classes simultaneously by employing a Markov Random Field based classification framework. The pixels in the labeled images are back-projected into a set of 3D point-clouds using stereo disparity. The point-clouds are registered together by incorporating the motion estimation and a coherent semantic map representation is obtained. SUM is evaluated on five urban benchmark sequences and is demonstrated to be successful in retrieving both geometric as well as semantic labels. The comparison with relevant state-of-art method reveals that SUM is competitive and performs better than the competing method in average pixel-wise accuracy.
{"title":"Semantic Urban Maps","authors":"J. R. Siddiqui, S. Khatibi","doi":"10.1109/ICPR.2014.694","DOIUrl":"https://doi.org/10.1109/ICPR.2014.694","url":null,"abstract":"A novel region based 3D semantic mapping method is proposed for urban scenes. The proposed Semantic Urban Maps (SUM) method labels the regions of segmented images into a set of geometric and semantic classes simultaneously by employing a Markov Random Field based classification framework. The pixels in the labeled images are back-projected into a set of 3D point-clouds using stereo disparity. The point-clouds are registered together by incorporating the motion estimation and a coherent semantic map representation is obtained. SUM is evaluated on five urban benchmark sequences and is demonstrated to be successful in retrieving both geometric as well as semantic labels. The comparison with relevant state-of-art method reveals that SUM is competitive and performs better than the competing method in average pixel-wise accuracy.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928673","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}
Detection of camera anomaly and tampering have attracted increasing interest in video surveillance for real-time alert of camera malfunction. However, the anomaly detection for traffic cameras monitoring vehicles and recognizing license plates has not been formally studied and it cannot be solved by existing methods. In this paper, we propose a camera anomaly detection method for traffic scene that has distinct characteristics of dynamics due to traffic flow and traffic crowd, compared with normal surveillance scene. Image quality used as low-level features are measured by no-referenced metrics. Image dynamics used as mid-level features are computed by histogram distribution of optical flow. A two-stage classifier for the detection of anomaly is devised by the modeling of image quality and video dynamics with probabilistic state transition. The proposed approach is robust to many challenging issues in urban surveillance scenarios and has very low false alarm rate. Experiments are conducted on real-world videos recorded in traffic scene including the situations of high traffic flow and severe crowding. Our test results demonstrate that the proposed method is superior to previous methods on both precision rate and false alarm rate for the anomaly detection of traffic cameras.
{"title":"Traffic Camera Anomaly Detection","authors":"Yuan-Kai Wang, Ching-Tang Fan, Jian-Fu Chen","doi":"10.1109/ICPR.2014.794","DOIUrl":"https://doi.org/10.1109/ICPR.2014.794","url":null,"abstract":"Detection of camera anomaly and tampering have attracted increasing interest in video surveillance for real-time alert of camera malfunction. However, the anomaly detection for traffic cameras monitoring vehicles and recognizing license plates has not been formally studied and it cannot be solved by existing methods. In this paper, we propose a camera anomaly detection method for traffic scene that has distinct characteristics of dynamics due to traffic flow and traffic crowd, compared with normal surveillance scene. Image quality used as low-level features are measured by no-referenced metrics. Image dynamics used as mid-level features are computed by histogram distribution of optical flow. A two-stage classifier for the detection of anomaly is devised by the modeling of image quality and video dynamics with probabilistic state transition. The proposed approach is robust to many challenging issues in urban surveillance scenarios and has very low false alarm rate. Experiments are conducted on real-world videos recorded in traffic scene including the situations of high traffic flow and severe crowding. Our test results demonstrate that the proposed method is superior to previous methods on both precision rate and false alarm rate for the anomaly detection of traffic cameras.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114145652","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}
Recognizing events in consumer videos is becoming increasingly important because of the enormous growth of consumer videos in recent years. Current researches mainly focus on learning from numerous labeled videos, which is time consuming and labor expensive due to labeling the consumer videos. To alleviate the labeling process, we utilize a large number of loosely labeled Web videos (e.g., from YouTube) for visual event recognition in consumer videos. Web videos are noisy and diverse, so brute force transfer of Web videos to consumer videos may hurt the performance. To address such a negative transfer problem, we propose a novel Multi-Group Adaptation (MGA) framework to divide the training Web videos into several semantic groups and seek the optimal weight of each group. Each weight represents how relative the corresponding group is to the consumer domain. The final classifier for event recognition is learned using the weighted combination of classifiers learned from Web videos and enforced to be smooth on the consumer domain. Comprehensive experiments on three real-world consumer video datasets demonstrate the effectiveness of MGA for event recognition in consumer videos.
{"title":"Multi-group Adaptation for Event Recognition from Videos","authors":"Yang Feng, Xinxiao Wu, Han Wang, Jing Liu","doi":"10.1109/ICPR.2014.671","DOIUrl":"https://doi.org/10.1109/ICPR.2014.671","url":null,"abstract":"Recognizing events in consumer videos is becoming increasingly important because of the enormous growth of consumer videos in recent years. Current researches mainly focus on learning from numerous labeled videos, which is time consuming and labor expensive due to labeling the consumer videos. To alleviate the labeling process, we utilize a large number of loosely labeled Web videos (e.g., from YouTube) for visual event recognition in consumer videos. Web videos are noisy and diverse, so brute force transfer of Web videos to consumer videos may hurt the performance. To address such a negative transfer problem, we propose a novel Multi-Group Adaptation (MGA) framework to divide the training Web videos into several semantic groups and seek the optimal weight of each group. Each weight represents how relative the corresponding group is to the consumer domain. The final classifier for event recognition is learned using the weighted combination of classifiers learned from Web videos and enforced to be smooth on the consumer domain. Comprehensive experiments on three real-world consumer video datasets demonstrate the effectiveness of MGA for event recognition in consumer videos.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114569751","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}
Recently, methods for the unsupervised learning of features from large data sets have been attracting much attention. These methods have been especially successful in the area of computer vision. However, there is a problem that it is difficult to determine what kind of features will result in a high classification performance. Indeed, the difficulty of determining the learning parameters is a widely known problem in the field of feature learning. To address this problem, this paper presents a feature-learning method which uses classification results to progressively learn multiple features of varied complexity. The proposed method enables the learning of both simple robust features and complex features which represents difficult patterns. In addition, we assign regularization weights that are based on the complexity of the features. This modification emphasizes simple representation and prevents over fitting. Experimental results with medical image classification show that the proposed method is superior to the conventional method, especially when classification is difficult.
{"title":"Learning Multiple Complex Features Based on Classification Results","authors":"Yoshikuni Sato, K. Kozuka, Y. Sawada, M. Kiyono","doi":"10.1109/ICPR.2014.580","DOIUrl":"https://doi.org/10.1109/ICPR.2014.580","url":null,"abstract":"Recently, methods for the unsupervised learning of features from large data sets have been attracting much attention. These methods have been especially successful in the area of computer vision. However, there is a problem that it is difficult to determine what kind of features will result in a high classification performance. Indeed, the difficulty of determining the learning parameters is a widely known problem in the field of feature learning. To address this problem, this paper presents a feature-learning method which uses classification results to progressively learn multiple features of varied complexity. The proposed method enables the learning of both simple robust features and complex features which represents difficult patterns. In addition, we assign regularization weights that are based on the complexity of the features. This modification emphasizes simple representation and prevents over fitting. Experimental results with medical image classification show that the proposed method is superior to the conventional method, especially when classification is difficult.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117085328","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}
Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural network models that can describe multi-modal nonnegative data. NNBMs form rectified Gaussian distributions that appear in biological neural network models, positive matrix factorization, nonnegative matrix factorization, and so on. In this paper, an effective inference method for NNBMs is proposed that uses the mean-field method, referred to as the Thou less-Anderson-Palmer equation, and the diagonal consistency method, which was recently proposed.
{"title":"Effective Mean-Field Inference Method for Nonnegative Boltzmann Machines","authors":"Muneki Yasuda","doi":"10.1109/ICPR.2014.619","DOIUrl":"https://doi.org/10.1109/ICPR.2014.619","url":null,"abstract":"Nonnegative Boltzmann machines (NNBMs) are recurrent probabilistic neural network models that can describe multi-modal nonnegative data. NNBMs form rectified Gaussian distributions that appear in biological neural network models, positive matrix factorization, nonnegative matrix factorization, and so on. In this paper, an effective inference method for NNBMs is proposed that uses the mean-field method, referred to as the Thou less-Anderson-Palmer equation, and the diagonal consistency method, which was recently proposed.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128157805","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}
Ye Liu, Xiangwei Kong, Haiyan Fu, Xingang You, Yunbiao Guo
Attribute based image retrieval has offered a powerful way to bridge the gap between low level features and high level semantic concepts. However, existing methods rely on manually pre-labeled queries, limiting their scalability and discriminative power. Moreover, such retrieval systems restrict the users to use only the exact pre-defined query words when describing the intended search targets, and thus fail to offer good user experience. In this paper, we propose a principled approach to automatically enrich the attribute representation by leveraging additional linguistic knowledge. To this end, an external semantic pool is introduced into the learning paradigm. In addition to modelling the relations between attributes and low level features, we also model the join interdependencies of pre-labeled attributes and semantically extended attributes, which is more expressive and flexible. We further propose a novel semantic relation measure for extended attribute learning in order to take user preference into account, which we see as a step towards practical systems. Extensive experiments on several attribute benchmarks show that our approach outperforms several state-of-the-art methods and achieves promising results in improving user experience.
{"title":"Model Semantic Relations with Extended Attributes","authors":"Ye Liu, Xiangwei Kong, Haiyan Fu, Xingang You, Yunbiao Guo","doi":"10.1109/ICPR.2014.440","DOIUrl":"https://doi.org/10.1109/ICPR.2014.440","url":null,"abstract":"Attribute based image retrieval has offered a powerful way to bridge the gap between low level features and high level semantic concepts. However, existing methods rely on manually pre-labeled queries, limiting their scalability and discriminative power. Moreover, such retrieval systems restrict the users to use only the exact pre-defined query words when describing the intended search targets, and thus fail to offer good user experience. In this paper, we propose a principled approach to automatically enrich the attribute representation by leveraging additional linguistic knowledge. To this end, an external semantic pool is introduced into the learning paradigm. In addition to modelling the relations between attributes and low level features, we also model the join interdependencies of pre-labeled attributes and semantically extended attributes, which is more expressive and flexible. We further propose a novel semantic relation measure for extended attribute learning in order to take user preference into account, which we see as a step towards practical systems. Extensive experiments on several attribute benchmarks show that our approach outperforms several state-of-the-art methods and achieves promising results in improving user experience.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133324038","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}
Tong Lu, Liang Wu, Xiaolin Ma, P. Shivakumara, C. Tan
A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering the fact that mutual interference of several human body parts potentially happen in the same block, we propose an atomic motion pattern representation using the Gaussian Mixture Model (GMM) to distinguish the motions inside each block in a refined way. Usual motion patterns can thus be defined as a certain type of steady motion activities appearing at specific scene positions. During the second stage, we further use the Markov Random Field (MRF) model to characterize the joint label distributions over all the adjacent local motion patterns inside the same crowded scene, aiming at modeling the severely occluded situations in a crowded scene accurately. By combining the determinations from the two stages, a weighted scheme is proposed to automatically detect anomaly events from crowded scenes. The experimental results on several different outdoor and indoor crowded scenes illustrate the effectiveness of the proposed algorithm.
{"title":"Anomaly Detection through Spatio-temporal Context Modeling in Crowded Scenes","authors":"Tong Lu, Liang Wu, Xiaolin Ma, P. Shivakumara, C. Tan","doi":"10.1109/ICPR.2014.383","DOIUrl":"https://doi.org/10.1109/ICPR.2014.383","url":null,"abstract":"A novel statistical framework for modeling the intrinsic structure of crowded scenes and detecting abnormal activities is presented in this paper. The proposed framework essentially turns the anomaly detection process into two parts, namely, motion pattern representation and crowded context modeling. During the first stage, we averagely divide the spatiotemporal volume into atomic blocks. Considering the fact that mutual interference of several human body parts potentially happen in the same block, we propose an atomic motion pattern representation using the Gaussian Mixture Model (GMM) to distinguish the motions inside each block in a refined way. Usual motion patterns can thus be defined as a certain type of steady motion activities appearing at specific scene positions. During the second stage, we further use the Markov Random Field (MRF) model to characterize the joint label distributions over all the adjacent local motion patterns inside the same crowded scene, aiming at modeling the severely occluded situations in a crowded scene accurately. By combining the determinations from the two stages, a weighted scheme is proposed to automatically detect anomaly events from crowded scenes. The experimental results on several different outdoor and indoor crowded scenes illustrate the effectiveness of the proposed algorithm.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505829","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}
Development of new stochastic super-resolution methods together with fluorescence microscopy imaging enables visualization of biological processes at increasing spatial and temporal resolution. Quantitative evaluation of such imaging experiments call for computational analysis methods that localize the signals with high precision and recall. Furthermore, it is desirable that the methods are fast and possible to parallelize so that the ever increasing amounts of collected data can be handled in an efficient way. We herein address signal detection in super-resolution microscopy by approaches based on compressed sensing. We describe how a previously published approach can be parallelized, reducing processing time at least four times. We also evaluate the effect of a greedy optimization approach on signal recovery at high noise and molecule density. Furthermore, our evaluation reveals how previously published compressed sensing algorithms have a performance that degrades to that of a random signal detector at high molecule density. Finally, we show the approximation of the imaging system's point spread function affects recall and precision of signal detection, illustrating the importance of parameter optimization. We evaluate the methods on synthetic data with varying signal to noise ratio and increasing molecular density, and visualize performance on real super-resolution microscopy data from a time-lapse sequence of living cells.
{"title":"An Evaluation of the Faster STORM Method for Super-resolution Microscopy","authors":"O. Ishaq, J. Elf, Carolina Wählby","doi":"10.1109/ICPR.2014.759","DOIUrl":"https://doi.org/10.1109/ICPR.2014.759","url":null,"abstract":"Development of new stochastic super-resolution methods together with fluorescence microscopy imaging enables visualization of biological processes at increasing spatial and temporal resolution. Quantitative evaluation of such imaging experiments call for computational analysis methods that localize the signals with high precision and recall. Furthermore, it is desirable that the methods are fast and possible to parallelize so that the ever increasing amounts of collected data can be handled in an efficient way. We herein address signal detection in super-resolution microscopy by approaches based on compressed sensing. We describe how a previously published approach can be parallelized, reducing processing time at least four times. We also evaluate the effect of a greedy optimization approach on signal recovery at high noise and molecule density. Furthermore, our evaluation reveals how previously published compressed sensing algorithms have a performance that degrades to that of a random signal detector at high molecule density. Finally, we show the approximation of the imaging system's point spread function affects recall and precision of signal detection, illustrating the importance of parameter optimization. We evaluate the methods on synthetic data with varying signal to noise ratio and increasing molecular density, and visualize performance on real super-resolution microscopy data from a time-lapse sequence of living cells.","PeriodicalId":142159,"journal":{"name":"2014 22nd International Conference on Pattern Recognition","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123476738","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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