Probabilistic pseudo-morphology for grayscale and color images

Mathematical morphology offers popular image processing tools, successfully used for binary and grayscale images. Recently, its extension to color images has become of interest and several approaches were proposed. Due to various issues arising from the vectorial nature of the data, none of them imposed as a generally valid solution. We propose a probabilistic pseudo-morphological approach, by estimating two pseudo-extrema based on Chebyshev inequality. The framework embeds a parameter which allows controlling the linear versus non-linear behavior of the probabilistic pseudo-morphological operators. We compare our approach for grayscale images with the classical morphology and we emphasize the impact of this parameter on the results. Then, we extend the approach to color images, using principal component analysis. As validation criteria, we use the estimation of the color fractal dimension, color textured image segmentation and color texture classification. Furthermore, we compare our proposed method against two widely used approaches, one morphological and one pseudo-morphological.     

Improving cache locality for GPU-based volume rendering

We present a cache-aware method for accelerating texture-based volume rendering on a graphics processing unit (GPU). Because a GPU has hierarchical architecture in terms of processing and memory units, cache optimization is important to maximize performance for memory-intensive applications. Our method localizes texture memory reference according to the location of the viewpoint and dynamically selects the width and height of thread blocks (TBs) so that each warp, which is a series of 32 threads processed simultaneously, can minimize memory access strides. We also incorporate transposed indexing of threads to perform TB-level cache optimization for specific viewpoints. Furthermore, we maximize TB size to exploit spatial locality with fewer resident TBs. For viewpoints with relatively large strides, we synchronize threads of the same TB at regular intervals to realize synchronous ray propagation. Experimental results indicate that our cache-aware method doubles the worst rendering performance compared to those provided by the CUDA and OpenCL software development kits.     

纸·质的设计表情

纸张不只是设计作品的介质,它亦属于设计的一部分。善于利用纸张的不同形态、质感,结合对受众人群的判断,将纸张的不同特性巧妙的融入适宜的设计之中,使纸张本身成为设计的元素之一,从而丰富我们的设计语言与设计体验,使默默无语的纸张成为具有情感与生命力的设计载体。在设计中分析和利用纸的不同质感所触及的受众心理感觉和意向,来点化作品的情感表达,引导人们去想象和体味。     

面泊松融合结合色彩变换的无缝纹理辐射处理

目的：消除三维模型纹理接缝上的辐射差异,实现接缝处色彩的自然、连续的过渡。方法：本文在标准的泊松融合（Poisson Blending）基础上,结合色彩变换技术（Color Transfer）,提出了一种新的以面为基元的泊松融合无缝纹理辐射处理方法。首先以影像上的纹理三角形为处理单元,以色彩变换参数为未知数,根据模型的三角网边邻接拓扑关系建立泊松方程组,然后全局最小二乘解求局部色彩变换参数,最后进行全局最优解下的局部色彩变换。结果：既保证了色彩的均匀过渡,又能更好的改正影像间的整体色差。结论：相比使用标准的像素级泊松融合方法进行无缝纹理处理,本文的方法拥有计算上的优势,更适合于复杂模型的处理,且有更好的整体色差改正与过渡效果。理论分析和大量试验证明了本文方法的优越性。     

基于PCA和SVM的遥感影像水体提取方法及验证

针对内陆湖泊水质及光谱特性空间差异性大、支流水系结构复杂而导致的遥感影像水体提取精度低的问题,提出了结合光谱主成分分析(PCA)及支持向量机(SVM)的PCA-SVM水体提取算法。基于GF-1卫星遥感影像,对原始影像光谱波段特征进行PCA降维,从中优选熵、方差、差异性纹理特征向量,结合原始波段及归一化差分水体指数(NDWI),构建了8维特征向量,并基于SVM算法提取湖泊水体。以巢湖洪水期与非洪水期影像为研究实例,分别采用NDWI法、传统SVM算法及PCA-SVM算法对水体进行提取,并进一步基于PCA-SVM算法对2020年汛期巢湖洪水期淹没演变过程进行反演和跟踪,定量解析特征向量组合及SVM惩罚系数C对水体提取性能的影响。结果表明：PCA-SVM算法提取的湖泊完整、支流连续,显著改善了含蓝藻水体漏提、建筑物误提等问题;洪水期和非洪水期提取结果的F1分数分别为95.08%和97.95%,虚警率分别为5.43%和1.13%,提取精度显著高于NDWI法和SVM算法。     

Preparation and characterization of biomorphic SiC hollow fibers from wood by chemical vapor infiltration

Biomorphic SiC hollow fibers were prepared by the reactive infiltration of SiO vapor into basswood-derived charcoal. Gaseous SiO was produced from a SiO₂/Si powder mixture in Ar at elevated temperatures. Scanning electron microscopy, energy dispersive X-ray spectroscopy, X-ray diffraction and Fourier transform-infrared spectroscopy were employed to characterize the structural morphology and phase compositions of the final products. The results show that the tubular cells in bulk charcoal are converted into lots of SiC hollow fibers with pore diameters of 10–50 μm and lengths ranging from hundreds of μm to several mm. Resulting SiC hollow fibers consist of β-SiC with a minute amount of α-SiC. The formation mechanism of SiC hollow fibers is based on the gas–solid reaction between SiO and carbon.     

基于纹理特征与随机森林的生态声音识别

针对真实环境中普遍存在的非平稳噪声,提出一种基于纹理特征与随机森林的生态声音识别方法。该方法首先使用一种基于噪声估计的音频增强算法,即短时谱估计对输入端声音信号进行前端处理,得到增强信号功率谱;然后根据得到的增强信号功率谱图的纹理信息,采用和差统计法对其进行纹理特征提取;最后,利用基于决策树的组合分类器,即随机森林进行识别和分类。设计了两组对比实验,结果表明该方法不仅有良好的识别性能,而且具有噪声鲁棒性。     

基于关系型发散的黎曼流形分类图像识别

针对图像识别中传统的嵌入流形算法通常只考虑切线空间却忽略了流形结构以致不能准确建模的问题,提出一种基于关系型发散的黎曼流形分类(RMC)算法。首先从底层黎曼流形建立训练集,并在流形上建立一组表示黎曼点的参考点;然后,借助于最近提出的斯坦因发散计算黎曼点与各个类之间的相似性,从而有效地将流形分类问题转换成寻找合适的相似空间问题;最后,利用经典的线性判别分析进行特征提取,最近邻分类器完成最终的分类、识别。通过纹理分类、人脸识别、人体识别实验验证了所提算法的有效性及高效性。实验结果表明,相比其他几种较为先进的流形算法,提出的算法不仅提高了识别率,同时大大减少了训练、分类所耗时间,有望应用于实时图像识别系统。     

基于逻辑回归模型的木片和树皮的图像识别

为了提高木片生物能的利用率,需要除去木片中的树皮杂质。识别木片和树皮的图像对于实现二者分离具有重要作用。提出一种基于逻辑回归模型来对木片和树皮进行图像识别的算法。算法流程包括计算各种可以描述木片和树皮纹理的参数,利用SAS统计分析出最佳参数,将最佳参数计算所得的样本数据用于SAS LOGISTIC过程得到预测方程,再将预测方程用于逻辑回归模型来实现木片和树皮的图像识别。通过实验可知,最佳参数为灰度方差,该图像识别算法的正确率能达到97.4%。