2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第5页

Application of image retrieval for aesthetic evaluation and improvement suggestion of isolated Bangla handwritten characters 图像检索在孟加拉孤立手写体汉字美学评价中的应用及改进建议

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120596

Mithun Biswas, Rafiqul Islam, Gautam Kumar Shom, Nabeel Mohammed, S. Momen, N. Mansoor, Md. Anowarul Abedin

{"title":"Application of image retrieval for aesthetic evaluation and improvement suggestion of isolated Bangla handwritten characters","authors":"Mithun Biswas, Rafiqul Islam, Gautam Kumar Shom, Nabeel Mohammed, S. Momen, N. Mansoor, Md. Anowarul Abedin","doi":"10.1109/ICSIPA.2017.8120596","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120596","url":null,"abstract":"Bangla is one of the most widely used languages worldwide. This paper presents an application of image retrieval techniques to automatically judge the aesthetic quality of handwritten Bangla isolated characters. Retrieval techniques are also adapted to give improvement suggestions, with a plan to incorporate the methods in applications which can assist in learning/teaching handwriting. The proposed method borrows key concepts from content-based image retrieval. Our method was tested on the BanglaLekha-Isolated data set, which contains images of 84 Bangla characters, with nearly 2000 samples per character. The data set contains evaluation of the aesthetic quality of the handwriting judged on a scale of 1–5. For this work, the dataset was partitioned into a test set of 400 images and a database-set of ≈ 1600 images, per Bangla character. Assuming that a scoring difference of 1 is acceptable, the proposed method achieves an accuracy of 77.39% when using features extracted by a convolutional neural network based autoencoder. Experiments were also done with the popular HOG feature. However, the autoencoder-based results showed clear superiority compared the HOG-based results. Our proposed method for improvement suggestions also shows that it is possible to shows samples from the dataset which will help users improve their handwriting while requiring small changes to their own handwriting.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116048440","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

Classification of stress into emotional, mental, physical and no stress using electroencephalogram signal analysis 利用脑电图信号分析将应激分为情绪应激、精神应激、生理应激和无应激

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120571

Adrian Emiell U. Berbano, Hanz Niccole V. Pengson, Cedric Gerard V. Razon, Kristel Chloe G. Tungcul, Seigfred V. Prado

{"title":"Classification of stress into emotional, mental, physical and no stress using electroencephalogram signal analysis","authors":"Adrian Emiell U. Berbano, Hanz Niccole V. Pengson, Cedric Gerard V. Razon, Kristel Chloe G. Tungcul, Seigfred V. Prado","doi":"10.1109/ICSIPA.2017.8120571","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120571","url":null,"abstract":"The paper presents further research on neural engineering that focuses on the classification of emotional, mental, physical and no stress through the use of Electroencephalography (EEG) signal analysis. Stress is one of the leading causes of several health-related problems and diseases. Therefore, it becomes necessary for people to monitor their stress. The human body acquires and responds to stress in different ways resulting to two classifications of stress namely, mental and emotional stress. Traditional methods in classifying stress such as through questionnaires and self-assessment tests are said to be subjective since they rely on personal judgment. Thus, in this study, stress is classified through an objective measure which is EEG signal analysis. The features of the EEG recordings are then pre-processed, extracted, and selected using Discrete Wavelet Transform (DWT). These features are then ussed as inputs to classify stress using Artificial Neural Network (ANN) and validated using K-fold Cross Validation Method. Lastly, the results from the software assisted method is compared to the results of the traditional method.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134318501","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: 18

K-step ahead prediction models for dengue occurrences 登革热发病的k步预测模型

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120671

Loshini Thiruchelvam, V. Asirvadam, S. Dass, H. Daud, B. Gill

引用次数: 2

An extended architecture to optimize execution time of 3D image processing deflectometry algorithm using FPGA 基于FPGA的三维图像处理偏转算法执行时间优化扩展架构

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120617

Faraz Bhatti, Thomas Greiner, M. Heizmann, Mathias Ziebarth

{"title":"An extended architecture to optimize execution time of 3D image processing deflectometry algorithm using FPGA","authors":"Faraz Bhatti, Thomas Greiner, M. Heizmann, Mathias Ziebarth","doi":"10.1109/ICSIPA.2017.8120617","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120617","url":null,"abstract":"The use of image processing is being accelerated over the past years in areas, including artificial intelligence, medical field, remote sensing and microscopic imaging. For 3D reconstruction of the objects, deflectometry is used to collect topographic information of surfaces. Due to computationally intensive nature of the algorithm, the execution time is one of the challenges faced by the deflectometry. In this paper, an extended FPGA based architecture is proposed to execute and improve the performance of deflectometry algorithm. The whole process consists of several stages, including initialization, acquisition and processing of data. The main idea is to utilize the optimizations e.g., pipelining, parallelization, provided by an FPGA to improve the performance of the algorithm. However, the advantage of parallelization can only be utilized if the associated algorithm contains the number of tasks, which can run independent of each other. For this reason, the deflectometry algorithm is adapted to the architecture of an FPGA to improve the performance. After successful realization of proposed architecture, the results have shown that performance is significantly improved in terms of execution time. Moreover, a rapid design development methodology is employed to decrease the prototyping time.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123318627","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

Enhanced correlation coefficient as a refinement of image registration 增强的相关系数作为图像配准的细化

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120609

Stephen, Wen Hwooi Khor, Aznul Qalid Md. Sabri

引用次数: 3

Object recognition and real-time spoken word recognition using two-fold dynamic time warping for autonomous arm manipulator 基于二次动态时间翘曲的自主机械臂目标识别与实时语音识别

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120613

Irfan Al-Hussaini, Zubayer Islam, Antik Mallick, M. E. Hoque

引用次数: 1

Fuzzy gain scheduled set-point weighted PID controller for unstable CSTR systems 不稳定CSTR系统的模糊增益预定设定点加权PID控制

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120623

Kishore Bingi, R. Ibrahim, M. N. Karsiti, S. Hassan

引用次数: 12

Translation and rotation invariant video stabilization for real time applications 平移和旋转不变视频稳定的实时应用

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120659

Babar Sultan, J. Ahmed, A. Jalil, H. Nazir, M. Abbasi, J. Shah, Ahmad Ali, Haider Ali

{"title":"Translation and rotation invariant video stabilization for real time applications","authors":"Babar Sultan, J. Ahmed, A. Jalil, H. Nazir, M. Abbasi, J. Shah, Ahmad Ali, Haider Ali","doi":"10.1109/ICSIPA.2017.8120659","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120659","url":null,"abstract":"Use of camera has increased among professionals and nonprofessionals in recent years and videos are being captured widely and wildly for information, knowledge, surveillance, adventures and memories. So these videos are highly vulnerable to suffer from translational and rotational noises. These noise are caused by multiple factors and it is difficult to remove all those causes. So digital video stabilization is a process of acquiring and minimizing/removing the undesired motion from the video. In this paper we have presented a method which utilizes existing algorithms and techniques in a novel fashion for digital video stabilization. The quality of feature extraction is improved by using Speeded Up Robust Features (SURF) and the process for the selection of extracted features, for global motion acquisition, is also refined. Actual motion of the camera and the undesired motion are separated by applying the moving average filter. Finally, stable frames are obtained through affine transformation to produce an out of phase motion. We have also presented a way to use interpolation for improving the quality of video stabilization. Our system has been successfully tested on various videos including VIRAT dataset, disaster videos, rush hour videos, mountain cycling, street walking, TV reports etc.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"198 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127314301","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

Impulsive noise suppression for robust iterative timing recovery in non-Gaussian channels 非高斯信道鲁棒迭代定时恢复的脉冲噪声抑制

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120612

Pu Chuan Hsian, Ezra Morris Abraham Gnanamuthu, Lo Fook Loong

{"title":"Impulsive noise suppression for robust iterative timing recovery in non-Gaussian channels","authors":"Pu Chuan Hsian, Ezra Morris Abraham Gnanamuthu, Lo Fook Loong","doi":"10.1109/ICSIPA.2017.8120612","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120612","url":null,"abstract":"Conventional iterative timing recovery is developed based on the widely used assumption of Additive White Gaussian noise (AWGN) interference. The Gaussian-based approach is excellent for timing recovery over AWGN channel with matched filtering approach but does not perform well in the presence of non-Gaussian noise. Overall performance of the conventional iterative timing recovery with matched filtering is significantly degraded in non-Gaussian channel. The root cause of the degradation is due to the received symbols are purged by the impulsive outliers from the non-Gaussian channels. Hence, this paper proposed a mitigation technique to address the issue for iterative timing recovery. In order to overcome this problem, a Matched Myriad filtering framework is proposed to be incorporated into iterative timing recovery as front-end receive filter. With the k tuning parameter of the robust Matched Myriad filter which caters for varying channel conditions, the iterative timing recovery can perform robustly and its performance is close to the benchmark of having its performance over the Gaussian channel. It is shown from simulations that more reliable received samples can be acquired to produce the accurate timing estimates and outputs.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123168320","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

Wavelet-based aortic annulus sizing of echocardiography images 超声心动图图像中基于小波的主动脉环大小

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120586

N. Mohammad, Z. Omar, U. U. Sheikh, A. Rahman, M. Sahrim

{"title":"Wavelet-based aortic annulus sizing of echocardiography images","authors":"N. Mohammad, Z. Omar, U. U. Sheikh, A. Rahman, M. Sahrim","doi":"10.1109/ICSIPA.2017.8120586","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120586","url":null,"abstract":"Aortic stenosis (AS) is a condition where the calcification deposit within the heart leaflets narrows the valve and restricts the blood from flowing through it. This disease is progressive over time where it may affect the mechanism of the heart valve. To alleviate this condition without resorting to surgery, which runs the risk of mortality, a new method of treatment has been introduced: Transcatheter Aortic Valve Implantation (TAVI), in which imagery acquired from real-time echocardiogram (Echo) are needed to determine the exact size of aortic annulus. However, Echo data often suffers from speckle noise and low pixel resolution, which may result in incorrect sizing of the annulus. Our study therefore aims to perform an automated detection and measurement of aortic annulus size from Echo imagery. Two stages of algorithm are presented — image denoising and object detection. For the removal of speckle noise, Wavelet thresholding technique is applied. It consists of three sequential steps; applying linear discrete wavelet transform, thresholding wavelet coefficients and performing linear inverse wavelet transform. For the next stage of analysis, several morphological operations are used to perform object detection as well as valve sizing. The results showed that the automated system is able to produce more accurate sizing based on ground truth.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125271126","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