Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum.However,due to sensor limitations and imperfections during ...Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum.However,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral unmixing.Noise in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and deadlines.An HSI restoration method for such a mixed noise model is proposed.First,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise levels.Second,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients.Third,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is used.Because the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)denoiser.To the best of authors'knowledge,this joint optimisation framework is the first attempt in this direction.To handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is employed.Finally,an efficient split-Bregman approach is used to solve the resulting optimisation problem.Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.展开更多
Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms.These lexicons play an important role in performing several differ...Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms.These lexicons play an important role in performing several different opinion mining tasks.The efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the text.Therefore,one has to explore the available sentiment lexicons and then select the most suitable resource.Among available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion mining.In SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of classifiers.In this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive way.This survey will be useful for the researchers contributing to the field of opinion mining.Following features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers used.In the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.展开更多
Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) to...Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience(1-4)Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data[5-8].展开更多
文摘Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic spectrum.However,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral unmixing.Noise in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and deadlines.An HSI restoration method for such a mixed noise model is proposed.First,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise levels.Second,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image gradients.Third,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is used.Because the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)denoiser.To the best of authors'knowledge,this joint optimisation framework is the first attempt in this direction.To handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is employed.Finally,an efficient split-Bregman approach is used to solve the resulting optimisation problem.Experimental results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
基金This work was supported by the Department of Computer Science&IT,The Islamia University of Bahawalpur,Pakistan in collaboration with Laboratoire Informatique,Image et Interaction(L3i),University of La Rochelle,France.
文摘Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms.These lexicons play an important role in performing several different opinion mining tasks.The efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the text.Therefore,one has to explore the available sentiment lexicons and then select the most suitable resource.Among available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion mining.In SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of classifiers.In this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive way.This survey will be useful for the researchers contributing to the field of opinion mining.Following features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers used.In the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.
文摘Automatic image analysis techniques applied to neuroimaging data in general, and magnetic resonance imaging (MRI), and functional MRI (fMRI) in particular, have proven to be effective computer-aided diagnosis (CAD) tools in neuroscience(1-4)Recently, the advancements in machine learning techniques combined with the wide availability of computational power have proven to be efficient in solving previously difficult problems in analyzing neuroimaging data. At the forefront of these advancements is the usage of deep (artificial) neural network architectures that led robust learning based techniques to attack challenging problems such as segmentation and classification in neuroimaging data[5-8].
