目的现有显著性检测方法大多只关注显著目标的中心信息,使得算法只能得到中心清晰、边缘模糊的显著目标,丢失了一些重要的边界信息,而使用核范数约束进行低秩矩阵恢复,运算过程冗余。为解决以上问题,本文提出一种无监督迭代重加权最小...目的现有显著性检测方法大多只关注显著目标的中心信息,使得算法只能得到中心清晰、边缘模糊的显著目标,丢失了一些重要的边界信息,而使用核范数约束进行低秩矩阵恢复,运算过程冗余。为解决以上问题,本文提出一种无监督迭代重加权最小二乘低秩恢复算法,用于图像视觉显著性检测。方法将图像分为细中粗3种尺度的分割,从细粒度和粗粒度先验的融合中得到分割先验信息;将融合后的分割先验信息通过迭代重加权最小二乘法求解平滑低秩矩阵恢复,生成粗略显著图;使用中粒度分割先验对粗略显著图进行平滑,生成最终的视觉显著图。结果实验在MSRA10K(Microsoft Research Asia 10K)、SOD(salient object detection dataset)和ECSSD(extended complex scene saliency dataset)数据集上进行测试,并与现有的11种算法进行对比。结果表明,本文算法可生成边界清晰的显著图。在MSRA10K数据集上,本文算法实现了最高的AUC(area under ROC(receiver operating characteristic)curve)和F-measure值,MAE(mean absolute error)值仅次于SMD(structured matrix decomposition)算法和RBD(robust back ground detection)算法,AUC和F-measure值比次优算法RPCA(robust principal component analysis)分别提高了3.9%和12.3%;在SOD数据集上,综合AUC、F-measure和MAE值来看,本文算法优于除SMD算法以外的其他算法,AUC值仅次于SMD算法、SC(smoothness constraint)算法和GBVS(graph-based visual salieney)算法,F-measure值低于最优算法SMD 2.6%;在ECSSD数据集上,本文算法实现了最高的F-measure值75.5%,AUC值略低于最优算法SC 1%,MAE值略低于最优算法HCNs(hierarchical co-salient object detection via color names)2%。结论实验结果表明,本文算法能从前景复杂或背景复杂的显著图像中更准确地检测出边界清晰的显著目标。展开更多
Objective: Most of the western music consists of a melody and an accompaniment. The melody is referred to as the foreground, with the accompaniment the background. In visual processing, the lateral occipital complex (...Objective: Most of the western music consists of a melody and an accompaniment. The melody is referred to as the foreground, with the accompaniment the background. In visual processing, the lateral occipital complex (LOC) is known to participate in foreground and background segregation. We investigated the role of LOC in music processing with use of positron emission tomography (PET). Method: Musically na?ve subjects listened to unfamiliar novel melodies with (accompaniment condition) and without the accompaniment (melodic condition). Using a PET subtraction technique, we studied changes in regional cerebral blood flow (rCBF) during the accompaniment condition compared to the melodic condition. Results: The accompanyment condition was associated with bilateral increase of rCBF at the lateral and medial surfaces of both occipital lobes, medial parts of fusiform gyri, cingulate gyri, precentral gyri, insular cortices, and cerebellum. During the melodic condition, the activation at the anterior and posterior portions of the temporal lobes, medial surface of the frontal lobes, inferior frontal gyri, orbitofrontal cortices, inferior parietal lobules, and cerebellum was observed. Conclusions: The LOC participates in recognition of melody with accompaniment, a phenomenon that can be regarded as foreground and background segregation in auditory processing. The fusiform cortex which was known to participate in the color recognition might be activated by the recognition of flourish sounds by the accompaniment, compared to melodic condition. It is supposed that the LOC and fusiform cortex play similar functions beyond the difference of sensory modalities.展开更多
文摘目的现有显著性检测方法大多只关注显著目标的中心信息,使得算法只能得到中心清晰、边缘模糊的显著目标,丢失了一些重要的边界信息,而使用核范数约束进行低秩矩阵恢复,运算过程冗余。为解决以上问题,本文提出一种无监督迭代重加权最小二乘低秩恢复算法,用于图像视觉显著性检测。方法将图像分为细中粗3种尺度的分割,从细粒度和粗粒度先验的融合中得到分割先验信息;将融合后的分割先验信息通过迭代重加权最小二乘法求解平滑低秩矩阵恢复,生成粗略显著图;使用中粒度分割先验对粗略显著图进行平滑,生成最终的视觉显著图。结果实验在MSRA10K(Microsoft Research Asia 10K)、SOD(salient object detection dataset)和ECSSD(extended complex scene saliency dataset)数据集上进行测试,并与现有的11种算法进行对比。结果表明,本文算法可生成边界清晰的显著图。在MSRA10K数据集上,本文算法实现了最高的AUC(area under ROC(receiver operating characteristic)curve)和F-measure值,MAE(mean absolute error)值仅次于SMD(structured matrix decomposition)算法和RBD(robust back ground detection)算法,AUC和F-measure值比次优算法RPCA(robust principal component analysis)分别提高了3.9%和12.3%;在SOD数据集上,综合AUC、F-measure和MAE值来看,本文算法优于除SMD算法以外的其他算法,AUC值仅次于SMD算法、SC(smoothness constraint)算法和GBVS(graph-based visual salieney)算法,F-measure值低于最优算法SMD 2.6%;在ECSSD数据集上,本文算法实现了最高的F-measure值75.5%,AUC值略低于最优算法SC 1%,MAE值略低于最优算法HCNs(hierarchical co-salient object detection via color names)2%。结论实验结果表明,本文算法能从前景复杂或背景复杂的显著图像中更准确地检测出边界清晰的显著目标。
文摘Objective: Most of the western music consists of a melody and an accompaniment. The melody is referred to as the foreground, with the accompaniment the background. In visual processing, the lateral occipital complex (LOC) is known to participate in foreground and background segregation. We investigated the role of LOC in music processing with use of positron emission tomography (PET). Method: Musically na?ve subjects listened to unfamiliar novel melodies with (accompaniment condition) and without the accompaniment (melodic condition). Using a PET subtraction technique, we studied changes in regional cerebral blood flow (rCBF) during the accompaniment condition compared to the melodic condition. Results: The accompanyment condition was associated with bilateral increase of rCBF at the lateral and medial surfaces of both occipital lobes, medial parts of fusiform gyri, cingulate gyri, precentral gyri, insular cortices, and cerebellum. During the melodic condition, the activation at the anterior and posterior portions of the temporal lobes, medial surface of the frontal lobes, inferior frontal gyri, orbitofrontal cortices, inferior parietal lobules, and cerebellum was observed. Conclusions: The LOC participates in recognition of melody with accompaniment, a phenomenon that can be regarded as foreground and background segregation in auditory processing. The fusiform cortex which was known to participate in the color recognition might be activated by the recognition of flourish sounds by the accompaniment, compared to melodic condition. It is supposed that the LOC and fusiform cortex play similar functions beyond the difference of sensory modalities.
