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2019 5th International Conference on Frontiers of Signal Processing (ICFSP)最新文献

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Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images 研究深度cnn模型在面部图像亲属关系验证中的应用
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938055
A. Chergui, Salim Ouchtati, S. Mavromatis, Salah Eddine Bekhouche, J. Sequeira
{"title":"Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images","authors":"A. Chergui, Salim Ouchtati, S. Mavromatis, Salah Eddine Bekhouche, J. Sequeira","doi":"10.1109/icfsp48124.2019.8938055","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938055","url":null,"abstract":"The kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. The approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. The experimental results show that our approach is comparable with existed approaches.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116081621","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
LIDAR Data Compression Challenges and Difficulties 激光雷达数据压缩的挑战和困难
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938066
M. Abdelwahab, W. El-Deeb, A. Youssif
{"title":"LIDAR Data Compression Challenges and Difficulties","authors":"M. Abdelwahab, W. El-Deeb, A. Youssif","doi":"10.1109/icfsp48124.2019.8938066","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938066","url":null,"abstract":"Airborne light detection and ranging (LIDAR) system uses in many field such as military, agriculture, geology and transportation. LIDAR data acquisition consumes several gigabytes from the storage capacity of the system. So, it's necessary to reduce the on board storage capacity as well as its transmission rate. Compression of LIDAR data means reducing the amount of data required to represent the digital data acquired by LIDAR system. This is done by removing data redundancies to achieve compression. In this paper we explain LIDAR system theory and component, compare between lossy and lossless compression techniques then we discuss LIDAR data compression techniques in order to choose a suitable one for LIDAR system that gives high compression ratio with suitable processing time. In most LIDAR data compression techniques classification of LIDAR data is an important issue as a preprocessing step to simplify the data. Using more than one compression algorithms increase the compression ratio but increase the processing time as well.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123837907","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: 3
The Path Similarity Method for Phase Measurements Disambiguation 相位测量消歧的路径相似法
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938086
O. Chernoyarov, K. S. Kalashnikov, V. Ivanov, A. Makarov
{"title":"The Path Similarity Method for Phase Measurements Disambiguation","authors":"O. Chernoyarov, K. S. Kalashnikov, V. Ivanov, A. Makarov","doi":"10.1109/icfsp48124.2019.8938086","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938086","url":null,"abstract":"We introduce the technique for phase measurements disambiguation based on the paths similarity identification. To obtain these paths, we use phase and amplitude measurements. By simulation, the performance of the presented approach is established. Its application for the phase measurements disambiguation is demonstrated by the example of the phase correlative direction finder for which the amplitude correlative direction finding mode is activated without any hardware changes.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125333379","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: 1
Low Complexity Pruned DCT Approximation for Image Compression in Wireless Multimedia Sensor Networks 无线多媒体传感器网络图像压缩的低复杂度剪枝DCT逼近
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938063
Nesrine Zidani, N. Kouadria, N. Doghmane, S. Harize
{"title":"Low Complexity Pruned DCT Approximation for Image Compression in Wireless Multimedia Sensor Networks","authors":"Nesrine Zidani, N. Kouadria, N. Doghmane, S. Harize","doi":"10.1109/icfsp48124.2019.8938063","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938063","url":null,"abstract":"In this paper we propose a novel efficient low-complexity Discrete Cosine Transform (DCT) approximation using only 14 additions. The proposed transformation is based upon a pruned version of the Signed DCT (SDCT). Associated with a JPEG compression chain, this DCT approximation ensures a very good rate-distortion compromise, a very low computational complexity and a significant compatibility with the exact DCT. The proposed transformation is very suitable for the limited energy resource wireless multimedia sensor networks (WMSNs) requiring very low bitrates and other embedded systems. Simulation results show the superiority of the proposed fast algorithm compared to any existing pruned DCT approximations.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122635471","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: 6
Monitoring Crop Phenology Using NDVI Time Series from Sentinel 2 Satellite Data 利用哨兵2号卫星数据NDVI时间序列监测作物物候
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938078
M. Boori, K. Choudhary, R. Paringer, A. Sharma, A. Kupriyanov, S. Corgne
{"title":"Monitoring Crop Phenology Using NDVI Time Series from Sentinel 2 Satellite Data","authors":"M. Boori, K. Choudhary, R. Paringer, A. Sharma, A. Kupriyanov, S. Corgne","doi":"10.1109/icfsp48124.2019.8938078","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938078","url":null,"abstract":"Monitoring crop phenology is a great importance to yield estimation and agriculture field management for food security, sustainable development of agriculture and to improve productivity. It's also important to understand how climate and local factors are effect on crop growth. The increasing spatial and temporal resolution of globally available satellites such as Sentinel-2 gives a unique opportunity to monitor crops systematically by vegetation index time-series. To explore the applicability of Sentinel-2 for crop phenology monitoring, we analyzed the Normalized Differential Vegetation Index (NDVI) time series with crop calendar for the year 2018 from crop fields in Samara airport area, Russia. This work has great potential to provide valuable support for monitoring crop phenology and providing precise management strategy. The dates of active tillering, jointing and maturity detected from NDVI could be useful in supporting crop modeling, extension irrigation, fertilization management, harvest determination and in last enhance food security.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908827","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: 10
Target Detection of Optimum Frequencies Selection Based on Time Reversal 基于时间反转的最优频率选择目标检测
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938037
Zhaoming Zhang, Baixiao Chen, Minglei Yang, Zihao Yuan
{"title":"Target Detection of Optimum Frequencies Selection Based on Time Reversal","authors":"Zhaoming Zhang, Baixiao Chen, Minglei Yang, Zihao Yuan","doi":"10.1109/icfsp48124.2019.8938037","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938037","url":null,"abstract":"Target detection based on time reversal has been studied for many years, but these researches paied a little attention to the characteristics of target frequency response. In this paper, we propose a method to select optimum frequencies based on time reversal. After time reversal, there always be some frequency points that lead to the amplitude of time reversal signal higher than conventional signal, while other frequency points are opposite. We extract signal data of these special frequency points to detect the target. And we consider two scenarios that the target response is known or unknown. The extracted data is used in binary hypothesis test of target detection. Although the number of points by optimum frequencies selection is less than the non-selection, the detection probability is significantly higher than non-selection.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"17 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133923871","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: 1
An Incremental Noise Constrained Least Mean Square Algorithm 一种增量噪声约束最小均方算法
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938040
Usman Hameed, S. G. Khawaja, M. O. B. Saeed
{"title":"An Incremental Noise Constrained Least Mean Square Algorithm","authors":"Usman Hameed, S. G. Khawaja, M. O. B. Saeed","doi":"10.1109/icfsp48124.2019.8938040","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938040","url":null,"abstract":"This work proposes a distributed estimation algorithm for wireless sensor network, based on the incremental scheme. The proposed algorithm utilizes the noise variance in order to improve performance. The derivation and mean analysis are shown. The mean analysis of the algorithm is performed which show the range of step size and the stability of the algorithm. Under different scenarios experimental results show the superiority of the proposed algorithm.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127582649","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
Long Short-Term Memory and Convolutional Neural Networks for Active Noise Control 长短期记忆与卷积神经网络在主动噪声控制中的应用
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938042
S. Park, E. Patterson, Carl Baum
{"title":"Long Short-Term Memory and Convolutional Neural Networks for Active Noise Control","authors":"S. Park, E. Patterson, Carl Baum","doi":"10.1109/icfsp48124.2019.8938042","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938042","url":null,"abstract":"Active noise control, or adaptive noise cancellation, techniques (ANC) attempt to reduce sound pressure level by generation and superposition of an anti-noise signal in order to improve human safety or comfort in noisy environments. The least mean squares (LMS) algorithm and variants as well as some architectures of neural networks have been employed successfully for active control of noise. This work presents the novel use of long short-term memory (LSTM) and convolutional neural network (CNN) architectures for this task. Testing on a selection of commonly used noises, improved results are demonstrated when compared to both the traditional LMS approach and previously published neural-network approaches.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126893091","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
OCAE: Organization-Controlled Autoencoder for Unsupervised Speech Emotion Analysis 组织控制的无监督语音情感分析自动编码器
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938073
Siwei Wang, Catherine Soladié, R. Séguier
{"title":"OCAE: Organization-Controlled Autoencoder for Unsupervised Speech Emotion Analysis","authors":"Siwei Wang, Catherine Soladié, R. Séguier","doi":"10.1109/icfsp48124.2019.8938073","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938073","url":null,"abstract":"One of the severe obstacles to speech emotion analysis is the lack of reasonable labelled speech signal. Thus, an important issue to be considered is applying an unsupervised method to generate a representation in low dimension to analyze emotions. Such a representation coming from data needs to be stable and meaningful, just like the 2D or 3D representation of emotions elaborated by psychology. In this paper, we propose a fully unsupervised approach, called Organization-Controlled AutoEncoder (OCAE), combining autoencoder with PCA to build an emotional representation. We utilize the result of PCA on speech features to control the organization of the data in the latent space of autoencoder, through adding an organization loss to the classical objective function. Indeed, PCA can keep the organization of the data, whereas autoencoder leads to better discrimination of the data. By combining both, we can take advantage of each method. The results on Emo-DB and SEMAINE database show that our representation generated in an unsupervised manner is meaningful and stable.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126739925","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
Graph-Based Segmentation for Diabetic Macular Edema Selection in OCT Images 基于图分割的糖尿病黄斑水肿OCT图像选择
2019 5th International Conference on Frontiers of Signal Processing (ICFSP) Pub Date : 2019-09-01 DOI: 10.1109/icfsp48124.2019.8938047
N. Ilyasova, A. Shirokanev, N. Demin, R. Paringer
{"title":"Graph-Based Segmentation for Diabetic Macular Edema Selection in OCT Images","authors":"N. Ilyasova, A. Shirokanev, N. Demin, R. Paringer","doi":"10.1109/icfsp48124.2019.8938047","DOIUrl":"https://doi.org/10.1109/icfsp48124.2019.8938047","url":null,"abstract":"Diabetic macular edema results in severe complications leading to blindness and is characterized by specific areas in the optical coherent tomography images (OCT). We propose a technique for diabetic macular edema selection, which is based on the pre-processing of OCT images using the edge detection method and graph-based image segmentation. In the course of study, the value of $sigma=3.5$ was demonstrated to be an optimal value of the $sigma$ parameter of a filter kernel utilized at a preprocessing stage. The image binarization threshold in the Canny algorithm was chosen based on a criterion of reduction of spurious edges in the resulting image. The best result was attained at a threshold of 0.6. It has been experimentally demonstrated that when the percentage of minimum cluster size equals 2.5% it is possible to attain a retinal segmentation error of 2%.","PeriodicalId":162584,"journal":{"name":"2019 5th International Conference on Frontiers of Signal Processing (ICFSP)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126454099","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: 0
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