音乐声的音色感知特征与图像的纹理特征的关联研究

本文主要研究音乐声的音色感知特征与图像的纹理之间的关联关系并建立音色与纹理的匹配模型.首先对Brodatz纹理库中的图片提取纹理特征,然后利用纹理特征对纹理图片进行系统聚类和多维尺度分析,挑选出18张用来做主观评价实验的纹理图片.通过主观评价实验,得到音色感知特征值以及音色和纹理的匹配关系数据,并基于此数据分别计算了匹配(或不匹配)的音色感知特征纹理特征之间的皮尔逊相关系数,相关系数绝对值最高达0.75,证明了二者之间存在强烈关联.最后使用逻辑回归、随机森林、多层感知神经网络对音色感知特征和纹理之间的匹配关系进行预测,得到了准确率较高的预测模型,说明通过音色感知特征和纹理特征预测二者匹配关系是可行的.

全谱直读发射光谱测定植物样中全硼的方法研究

采用全谱直读发射光谱法测定植物样品中硼,调整碳粉和硫粉比例改良缓冲剂,使用深孔石墨电极作为激发载体,优化激发条件,确立干扰扣除方式,建立有效地测定植物样品中全硼的方法。方法检出限为0.93 mg/kg,方法精密度3.77%~5.62%,通过生物成分分析标准物质验证,得到满意的结果,方法准确度高、实用高效、环保,可在植物样品的检测工作中起到积极作用。

边缘信息引导的伪装目标检测

伪装目标检测(camouflaged object detection,COD)旨在检测隐藏在复杂背景中的伪装目标。由于伪装目标的特点:前景与背景纹理相似、边缘对比度低,导致现有方法得到的预测图像边缘模糊、小目标区域缺失。因此,本文提出了边缘信息引导的伪装目标检测网络(edge information guided network,EIGNet)。首先,通过低层特征和高层特征对目标的边缘进行显式建模,充分提取目标的边缘特征指导后续特征表示。然后,通过双分支结构处理不同维度的伪装目标。其中,全局分支用以提取全局上下文信息强调大目标的全局贡献,局部分支用以挖掘丰富的局部低级线索增强小目标的特征表示。最后,采用自顶向下的方式实现相邻层特征的逐步融合,得到具有精细边缘和完整区域的预测图像。在3个伪装数据集上的实验结果表明本文方法优于其他15个模型,在NC4K数据集上平均绝对误差(mean absolute error,MAE)降至0.044。

Research on emotion space of film and television scene images based on subjective perception

The premise of image emotion recognition is to determine its representative emotional adjectives and establish the quantifiable emotion space. In this paper, focusing on aroused emotion from film and television(TV) scene images, a method of selecting emotional adjectives and establishing the emotion space based on subjective perception experiment is proposed. Firstly, a special data set about film and TV scene images was established and a set of initial emotional adjectives was collected. Then the subjective perception experiment was designed to let subjects to evaluate the affection of all the initial adjectives during watching these scene images. Then the method of principal basis analysis was used for variable selection. Finally, the factor analysis was applied to accomplish the second dimension reduction to form a 5-dimensional(5 D) orthogonal emotion space. The optimized emotion space can explain more than 94% of original emotional adjectives, which greatly reduces the dimension of emotional adjectives and lays a foundation for the further research on image content and emotion recognition.