2017 Artificial Intelligence and Signal Processing Conference (AISP)最新文献_第2页

Comparison of different objects in multi-objective ensemble clustering 多目标集成聚类中不同目标的比较

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324110

Haleh Homayouni, E. Mansoori

引用次数: 1

A model of sequential prediction in the brain using an oscillatory network 一种利用振荡网络在大脑中进行顺序预测的模型

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324086

G. Baghdadi, R. Rostami, F. Towhidkhah

{"title":"A model of sequential prediction in the brain using an oscillatory network","authors":"G. Baghdadi, R. Rostami, F. Towhidkhah","doi":"10.1109/AISP.2017.8324086","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324086","url":null,"abstract":"The predictive brain is a term that is known because of the capability of our neural system to find a model of the environment and to use it for predicting the incoming stimulus. It has not been fully understood that how our neural system that consists of millions of oscillatory networks can create, update, and maintain a model for the sequential prediction. In the current paper, we have proposed an oscillatory network that its units connect to each other through synchronization mechanism. The network has been used to suggest a possible mechanism of extracting the regularities exist in a continuous performance test. The result of simulations has been compared with the recorded human experiment data. Outcomes showed that the proposed model can mimic the pattern of human behaviors. It can be concluded that brain may create and modified the model of the environment by updating the coupling weight or the level of synchronization between its different units. There are some parameters in the proposed network and the updating procedure that can be used to model different observations of normal or abnormal human behaviors in sequential prediction.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126295873","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

Combination of wavelet and contourlet transforms for PET and MRI image fusion 基于小波变换和轮廓波变换的PET与MRI图像融合

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324077

Fahim Shabanzade, H. Ghassemian

引用次数: 21

Speech enhancement by minimum mean-square error spectral amplitude estimation assuming weibull speech priors 假设威布尔语音先验的最小均方误差谱幅估计语音增强

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324079

M. Bahrami, N. Faraji

引用次数: 4

Kim: A new structure for optimization problems ▽金=解决最优化问题的新结构

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324102

A. Hamzeh, K. B. Lari

引用次数: 0

Panchromatic and multispectral images fusion using sparse representation 基于稀疏表示的全色与多光谱图像融合

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/aisp.2017.8324113

Mehdi Ghamchili, H. Ghassemian

{"title":"Panchromatic and multispectral images fusion using sparse representation","authors":"Mehdi Ghamchili, H. Ghassemian","doi":"10.1109/aisp.2017.8324113","DOIUrl":"https://doi.org/10.1109/aisp.2017.8324113","url":null,"abstract":"In this paper, we propose a new pansharpening method based on sparse representation theory to fuse panchromatic and multispectral images. In the proposed method, the high-resolution multispectral image is reconstructed by adding some details to the multispectral image. The details are achieved directly by a proper dictionary which is constructed using a high pass version of the panchromatic image, so-called ‘detail dictionary’, and proper sparse coefficients. The required atoms for generating the details are chosen by two objective functions. One of these functions chooses atoms having high spatial information and the other one selects atoms with high spectral information. Then, the details are made from a linear combination of these atoms. We use both sets of the atoms to increase the spatial details and decrease the spectral distortion. In order to investigate the efficiency of the proposed method, two datasets from Pleiades and WorldView-2 satellites are used. Based on the experimental results, it is found that the proposed method performs better than the state-of-the-art methods in maintaining of spectral information as well as increasing spatial details objectively and visually.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121376089","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: 5

Adapting google translate for English-Persian cross-lingual information retrieval in medical domain 适应谷歌翻译的英语-波斯语跨语种信息检索在医学领域

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324104

Amin Rahmani

{"title":"Adapting google translate for English-Persian cross-lingual information retrieval in medical domain","authors":"Amin Rahmani","doi":"10.1109/AISP.2017.8324104","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324104","url":null,"abstract":"Cross-lingual information retrieval (CLIR) systems enable users to search and find their information needs from sources written in languages other than the user's native language. Generally, these systems assist users to overcome the language barrier problem. Although, several techniques are used to develop such systems, query translation method has absorbed much attention due to its performance. In this paper, the author suggested a new approach for English-Persian CLIR. To do this, Google Translate's API was adapted for CLIR system to translate the queries. Using TREC dataset, 50 queries were selected to evaluate the system. Both English queries and their Persian equivalents were searched in RICeST's English and Persian E-articles databases. As black box evaluation, the researcher utilized 11 point interpolated average precision metric to gain the average precision (AP) score for each query after which the mean average precision measure (MAP) scores for English and Persian queries were calculated. The MAP score for monolingual and cross-lingual systems were 0.421 and 0.382 respectively. As glass box evaluation, the machine translation system's performance was measured based on the BLEU automatic metric. According to the results of this study, 90% similarity in IR was observed between the CLIR and the monolingual systems. The new approach was ideally suited for English and Persian CLIR task.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129024641","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

A new feature extraction based on local energy for hyperspectral image 基于局部能量的高光谱图像特征提取新方法

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324107

R. Marandi, H. Ghassemian

{"title":"A new feature extraction based on local energy for hyperspectral image","authors":"R. Marandi, H. Ghassemian","doi":"10.1109/AISP.2017.8324107","DOIUrl":"https://doi.org/10.1109/AISP.2017.8324107","url":null,"abstract":"In hyperspectral classification, as the number of training samples to classify are limited, the accuracy of classifier decreases. One of the reasons for this phenomenon is the variability of spin-off extraction spatial features. This means that when the scene is rotated a bit, these features also change. It should be noted that these features are a local feature and ruin this situation, because there may be a class in two parts of the scene that is rotated relative to another. For this purpose, a new method for extracting spatial features has been proposed in this paper that is unchangeable to rotation. In this study, local energy has been extracted by local Fourier transform and structural information has been extracted by morphological attribute profiles (APs) to complete the extraction features. Energy information and spectral information in a scenario are stacked. Energy information, structure information and spectral information are stacked in another scenario. Then they are classified by support vector machine (SVM) classifier. The results express that the first scenario is beneficial for images without structural data, and the second scenario is more useful for urban images, which includes a lot of structural information. The proposed method are applied on three famous data sets (Pavia University, Salinas and Indiana Pines). The results demonstrate that the proposed method is superior to the other competition methods.","PeriodicalId":386952,"journal":{"name":"2017 Artificial Intelligence and Signal Processing Conference (AISP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200095","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

Face verification in the wild using similarity in representations 在野外使用相似性表征的人脸验证

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324125

M. Miri

引用次数: 2

Impact of machine learning on improvement of user experience in museums 机器学习对博物馆用户体验提升的影响

2017 Artificial Intelligence and Signal Processing Conference (AISP) Pub Date : 2017-10-01 DOI: 10.1109/AISP.2017.8324080

M. Majd, R. Safabakhsh

引用次数: 13