Pub Date : 2018-09-01DOI: 10.23919/SPA.2018.8563392
Jakub Jurek, Mateusz Pelesz, Are Losnegård, L. Reisæter, A. Wojciechowski, A. Klepaczko, O. Halvorsen, C. Beisland, M. Kociński, A. Materka, J. Rørvik, A. Lundervold
Traditionally, analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE MRI) requires pharmacokinetic modelling to derive quantitative physiological parameters of the tissue. Modelling, however, is a complex task and many competing models of contrast agent kinetics and tissue structure were proposed. Alternatively, raw DCE data could be analysed to find correlation with pathology in the tissue or other desired effects, for example by clustering. In this paper, we propose a new method for DCE MRI timeseries clustering. We model the data space as a Conditional Random Field (CRF) and optimize the objective function in order to find cluster labels for all timeseries. The method is unsupervised and fully automatic. We also propose a strategy to speed up the clustering process using Support Vector Machines. We demonstrate the utility of our method on two distinct problems: prostate cancer localization and healthy kidney compartment segmentation.
{"title":"CRF-Based Clustering of Pharmacokinetic Curves from Dynamic Contrast-Enhanced MR Images","authors":"Jakub Jurek, Mateusz Pelesz, Are Losnegård, L. Reisæter, A. Wojciechowski, A. Klepaczko, O. Halvorsen, C. Beisland, M. Kociński, A. Materka, J. Rørvik, A. Lundervold","doi":"10.23919/SPA.2018.8563392","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563392","url":null,"abstract":"Traditionally, analysis of Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE MRI) requires pharmacokinetic modelling to derive quantitative physiological parameters of the tissue. Modelling, however, is a complex task and many competing models of contrast agent kinetics and tissue structure were proposed. Alternatively, raw DCE data could be analysed to find correlation with pathology in the tissue or other desired effects, for example by clustering. In this paper, we propose a new method for DCE MRI timeseries clustering. We model the data space as a Conditional Random Field (CRF) and optimize the objective function in order to find cluster labels for all timeseries. The method is unsupervised and fully automatic. We also propose a strategy to speed up the clustering process using Support Vector Machines. We demonstrate the utility of our method on two distinct problems: prostate cancer localization and healthy kidney compartment segmentation.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117347829","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 : 2018-09-01DOI: 10.23919/SPA.2018.8563288
Przemysław Falkowski-Gilski
The main goal of digital broadcasting is to deliver high-quality content with the lowest possible bitrate. This paper is focused on transmitting alarm information, such as emergency warning and alerting, in the DAB+ (Digital Audio Broadcasting plus) broadcasting system. These additional services should be available at the lowest possible bitrate, in order to provide a clear and understandable voice message to people. Furthermore, additional information should not put stress on the ensemble management process, nor affect full-time audio services.
{"title":"Transmitting Alarm Information in DAB+ Broadcasting System","authors":"Przemysław Falkowski-Gilski","doi":"10.23919/SPA.2018.8563288","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563288","url":null,"abstract":"The main goal of digital broadcasting is to deliver high-quality content with the lowest possible bitrate. This paper is focused on transmitting alarm information, such as emergency warning and alerting, in the DAB+ (Digital Audio Broadcasting plus) broadcasting system. These additional services should be available at the lowest possible bitrate, in order to provide a clear and understandable voice message to people. Furthermore, additional information should not put stress on the ensemble management process, nor affect full-time audio services.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123694460","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 : 2018-09-01DOI: 10.23919/SPA.2018.8563410
M. Miesikowska
The main purpose of this work was to obtain background sound levels and speech intelligibility as well as to evaluate classification of speech commands in the presence of an unmanned aerial vehicle (UAV) equipped with four rotating propellers. Speech intelligibility was assessed using speech interference level (SIL) parameter according to ISO 9921. The UAV background sound levels were recorded in laboratory conditions using Norsonic140 sound analyzer in the absence of the UAV and in the presence of the UAV. The classification of speech commands/left, right, up, down, forward, backward, start, stop/recorded with Olympus LS-11 was evaluated in laboratory condition based on Mel-frequency cepstral coefficients and discriminant function analysis. The UAV was hovering at 1.5m during recordings. The A-weighted sound level obtained in the presence of the UAV was 70.5 dB(A). Speech intelligibility rating was poor in the presence of the UAV. Discriminant analysis based on Mel-frequency cepstral coefficients showed very successful classification of speech commands equal to 100%. Evaluated speech intelligibility did not exclude verbal communication with the UAV. The successful classification of speech commands in the presence of the UAV can enable the control of the UAV using voice commands and general communication with the UAV using speech.
{"title":"Speech Intelligibility in the presence of X4 Unmanned Aerial Vehicle","authors":"M. Miesikowska","doi":"10.23919/SPA.2018.8563410","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563410","url":null,"abstract":"The main purpose of this work was to obtain background sound levels and speech intelligibility as well as to evaluate classification of speech commands in the presence of an unmanned aerial vehicle (UAV) equipped with four rotating propellers. Speech intelligibility was assessed using speech interference level (SIL) parameter according to ISO 9921. The UAV background sound levels were recorded in laboratory conditions using Norsonic140 sound analyzer in the absence of the UAV and in the presence of the UAV. The classification of speech commands/left, right, up, down, forward, backward, start, stop/recorded with Olympus LS-11 was evaluated in laboratory condition based on Mel-frequency cepstral coefficients and discriminant function analysis. The UAV was hovering at 1.5m during recordings. The A-weighted sound level obtained in the presence of the UAV was 70.5 dB(A). Speech intelligibility rating was poor in the presence of the UAV. Discriminant analysis based on Mel-frequency cepstral coefficients showed very successful classification of speech commands equal to 100%. Evaluated speech intelligibility did not exclude verbal communication with the UAV. The successful classification of speech commands in the presence of the UAV can enable the control of the UAV using voice commands and general communication with the UAV using speech.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124342337","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 : 2018-09-01DOI: 10.23919/SPA.2018.8563383
Marcin Matlacz, G. Sarwas
This paper is focused on the problem of counting people in crowd. For solving this issue a complex valued convolutional neural network has been proposed. The network training and evaluation have been processed using datasets ShanghaiTech and UCF_CC_50, respectively. Achieved results have been compared with other algorithms for crowd counting based on the deep neural network architecture, mainly “CrowdNet” algorithm. Proposed model achieved better results than equivalent real-valued model.
{"title":"Crowd counting using complex convolutional neural network","authors":"Marcin Matlacz, G. Sarwas","doi":"10.23919/SPA.2018.8563383","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563383","url":null,"abstract":"This paper is focused on the problem of counting people in crowd. For solving this issue a complex valued convolutional neural network has been proposed. The network training and evaluation have been processed using datasets ShanghaiTech and UCF_CC_50, respectively. Achieved results have been compared with other algorithms for crowd counting based on the deep neural network architecture, mainly “CrowdNet” algorithm. Proposed model achieved better results than equivalent real-valued model.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129268028","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 : 2018-09-01DOI: 10.23919/SPA.2018.8563409
E. Canayaz, Veysel Gokhan Bocekci
The growing vehicle numbers in urban and national road networks emerged the need for effective monitoring and management of road traffic. Especially detecting vehicles with break average speed limits rules and trespassing a heavy vehicle is essential to constitute safety traffic flow. In the proposed study, the main goal was detecting heavy vehicles using surveillance videos by using interframe difference, approximate median filtering and Gaussian mixture models for background subtraction and compare their performance. Moreover, after removing the background image from original videos, on binary image morphological opening and blob analysis processes were applied and with minimum blob area of the detected object in a frame, heavy vehicle detection was achieved. Different background subtraction methods produce varying results, and these results were discussed. Our results were consistent with performance comparison studies which indicated the Gaussian mixture model was stable, real-time outdoor tracker in any varying outdoor condition.
{"title":"Comparison of Performance of Different Background Subtraction Methods for Detection of Heavy Vehicles","authors":"E. Canayaz, Veysel Gokhan Bocekci","doi":"10.23919/SPA.2018.8563409","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563409","url":null,"abstract":"The growing vehicle numbers in urban and national road networks emerged the need for effective monitoring and management of road traffic. Especially detecting vehicles with break average speed limits rules and trespassing a heavy vehicle is essential to constitute safety traffic flow. In the proposed study, the main goal was detecting heavy vehicles using surveillance videos by using interframe difference, approximate median filtering and Gaussian mixture models for background subtraction and compare their performance. Moreover, after removing the background image from original videos, on binary image morphological opening and blob analysis processes were applied and with minimum blob area of the detected object in a frame, heavy vehicle detection was achieved. Different background subtraction methods produce varying results, and these results were discussed. Our results were consistent with performance comparison studies which indicated the Gaussian mixture model was stable, real-time outdoor tracker in any varying outdoor condition.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121772983","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 : 2018-09-01DOI: 10.23919/SPA.2018.8563424
J. Kotus
The method for determining the speed of vehicles using acoustic vector sensor and sound intensity measurement technique was presented in the paper. First, the theoretical basis of the proposed method was explained. Next, the details of the developed algorithm of sound intensity processing both in time domain and in frequency domain were described. Optimization process of the method was also presented. Finally, the proposed measurement method was tested in real conditions. The obtained results confirm that the proposed method may complement the currently used vehicle speed measurement techniques.
{"title":"Determination of the Vehicles Speed Using Acoustic Vector Sensor","authors":"J. Kotus","doi":"10.23919/SPA.2018.8563424","DOIUrl":"https://doi.org/10.23919/SPA.2018.8563424","url":null,"abstract":"The method for determining the speed of vehicles using acoustic vector sensor and sound intensity measurement technique was presented in the paper. First, the theoretical basis of the proposed method was explained. Next, the details of the developed algorithm of sound intensity processing both in time domain and in frequency domain were described. Optimization process of the method was also presented. Finally, the proposed measurement method was tested in real conditions. The obtained results confirm that the proposed method may complement the currently used vehicle speed measurement techniques.","PeriodicalId":265587,"journal":{"name":"2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121633279","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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