The article adopted the multiscale Jensen - Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status (eyes closed, count, in a daze) ac...
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The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a ...
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The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a ...
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Human object classification is an important problem for smart video surveillance applications. In this paper we have proposed a method for human object classification, which classify the objects into two classes: huma...
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Owing to the thriving market of stereoscopic image based applications, efficient and effective 3D image quality assessment (IQA) techniques become colossally required these days. Consequently, we introduce a new reduc...
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Owing to the thriving market of stereoscopic image based applications, efficient and effective 3D image quality assessment (IQA) techniques become colossally required these days. Consequently, we introduce a new reduced-reference (RR) stereoscopic image quality metric to meet this demand, through measuring Structural degradation and Saliency based Parallax compensation Model (SSPM). Experimental results on the LIVE 3D image Quality Database, including both symmetrically and asymmetrically distorted stereoscopic images in different categories and quality levels, are provided to justify the effectiveness of the proposed SSPM model as compared to some existing progressive and popular stereoscopic IQA approaches. Meanwhile, it deserves broad attentions that only four number pairs, extracted from original image, are required as the key feature to be sent to the receiver terminal, thus making this procedure also efficient.
image denoising has been fanatically researched for a very long time in that it is a commonplace yet important subject. The task to testify the performance of different image de-noising methods always resorts to PSNR ...
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image denoising has been fanatically researched for a very long time in that it is a commonplace yet important subject. The task to testify the performance of different image de-noising methods always resorts to PSNR in the past, until the emergence of SSIM, a landmark image quality assessment (IQA) metric. Since then, a vast majority of IQA methods were introduced in terms of various kinds of models. But unfortunately, those IQA metrics are along with more or less deficiencies such as the requirement of original images, making them far less than the ideal approaches. To address this problem, in this paper we propose an effective and blind image quality assessment for noise (dubbed BIQAN) to approximate the human visual perception to noise. The BIQAN is realized with three important portions, namely the free energy based brain principle, image gradient extraction, and texture masking. We conduct and compare the proposed BIQAN and a large amount of existing IQA metrics on three largest and most popular image quality databases (LIVE, TID2013, CSIQ). Results of experiments prove that the BIQAN has acquired very encouraging performance, outperforming those competitors stated above.
With the quick acceleration of cultural exchange and globalization, multi-language annotation for in-theatre movie exhibition is gaining more attention. The existing solution overlays a stack of subtitles in different...
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ISBN:
(纸本)9781479943142
With the quick acceleration of cultural exchange and globalization, multi-language annotation for in-theatre movie exhibition is gaining more attention. The existing solution overlays a stack of subtitles in different languages over the movie, for example, the familiar combination of English + local tongue as seen in the cinema today. However, a major drawback of this straightforward piling-up type of solution is that as the required number of languages increases, the subtitle area grows and it may eventually span over the movie contents. An effective multi-language subtitling system without compromising the movie watching experience is therefore highly desired. In this paper, we propose a new Temporal Psychovisual Modulation (TPVM) based solution to the multi-language subtitling problem. TPVM is a new paradigm of information display exploiting the mismatch between high refresh rate of the modern optoelectronic displays and limited temporal resolution of the human visual system (HVS). In this work, we design a simultaneous triple subtitle exhibition system using 2 120 Hz stereoscopic DLP projectors with linear polarization filters and LCD shutter glasses. Wearing different pairs of glasses, the audience can enjoy the movie with 3 optional subtitles without interfering with the audience without the glasses and see the movie directly. Extended experimental results will be given in this paper to justify the effectiveness and robustness of the proposed multiple subtitling system.
Brain-tumor segmentation method is an important clinical requirement for the brain-tumor diagnosis and the radiotherapy *** the number of clusters is very difficult to define for high diversity in the appearance of tu...
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ISBN:
(纸本)9781467377249
Brain-tumor segmentation method is an important clinical requirement for the brain-tumor diagnosis and the radiotherapy *** the number of clusters is very difficult to define for high diversity in the appearance of tumor tissue among the different patients and the ambiguous boundaries about the *** our study,the nonparametric mixture of Dirichlet process (MDP) model is used to segment the tumor images automatically,which can be performed without initialization of the clustering ***,the anisotropic diffusion and Markov random field (MRF) smooth constraint are both proposed in our *** segmentation results for the multimodal MR glioma image sequences showed the properties,such as accuracy and computing speed about our algorithm demonstrates very impressive.
Based on the analysis of the local binary pattern (LBP) and its extensions, a novel method, called concave-convex partition (CCP), is proposed in this paper to improve the performance of the LBP-based methods for rota...
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Based on the analysis of the local binary pattern (LBP) and its extensions, a novel method, called concave-convex partition (CCP), is proposed in this paper to improve the performance of the LBP-based methods for rotation invariant texture classification. By the CCP, the neighborhoods of the image are divided into two categories firstly, the concave and convex categories, before computing the local binary codes. The reason is that the neighborhoods with different structures and visual perceptions may be set the same LBP code by the traditional LBP-based methods, which can reduce their discriminability inevitably. Then, two histograms are built on the concave and convex categories, respectively and concentrated into one as the texture image feature. Experimental results obtained from three widely used texture image databases demonstrate that the proposed method can greatly improve the performance of the traditional LBP-methods on texture classification.
For learning-based super-resolution reconstruction, the selection and training of dictionary play an important role in improving image reconstruction quality. A super-resolution algorithm based on two dictionary-pairs...
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