An unsupervised texture segmentation algorithm with feature spacereduction and knowledge feedback

This paper presents an unsupervised texture segmentation algorithm based on feature extraction using multichannel Gabor filtering. It is shown that feature contrast, a criterion derived for Gabor filter parameter selection, is well suited for feature coordinate weighting in order to reduce the feature space dimension. The central idea of the proposed segmentation algorithm is to decompose the actual segmented image into disjunct areas called scrap images and use them after lowpass filtering as additional features for repeated k-means clustering and minimum distance classification. This yields a classification of texture regions with an improved degree of homogeneity while preserving precise texture boundaries.     

Unsupervised texture segmentation of images using tuned matchedGabor filters

Recent studies have confirmed that the multichannel Gabor decomposition represents an excellent tool for image segmentation and boundary detection. Unfortunately, this approach when used for unsupervised image analysis tasks imposes excessive storage requirements due to the nonorthogonality of the basis functions and is computationally highly demanding. In this correspondence, we propose a novel method for efficient image analysis that uses tuned matched Gabor filters. The algorithmic determination of the parameters of the Gabor filters is based on the analysis of spectral feature contrasts obtained from iterative computation of pyramidal Gabor transforms with progressive dyadic decrease of elementary cell sizes. The method requires no a priori knowledge of the analyzed image so that the analysis is unsupervised. Computer simulations applied to different classes of textures illustrate the matching property of the tuned Gabor filters derived using our determination algorithm. Also, their capability to extract significant image information and thus enable an easy and efficient low-level image analysis will be demonstrated.     

射频放电电参数的测量

通过自行研制的传感器,结合网络分析理论,采用数字与计算机技术实现对射频放电电压,电流,相位及功率的测量,除可广泛应用于射频放电的电压,电流,相位及功率测量外,还可对射频放电负载特性及等离子体的伏安特性进行研究与分析。     

容性耦合射频(CCRF)放电等离子体特性实验研究

利用自行研制的传感器和测量装置 ,通过对射频放电电压电流以及其相位角的测定 ,算出放电管的总阻抗 ,结合放电管的等效电路 ,对容性耦合射频 (CCRF)激励激光器放电特性进行研究 ,得出容性耦合射频激励激光器等离子体的伏安特性的曲线 ,以及等离子体电阻、容抗与气体压强、放电电流之间的实验曲线 ,在Godyak射频放电模型的基础上得出等离子体的电子密度 ,并同内置铜电极射频激励铜离子激光器阻抗特性进行了比较。     

射频激励铜离子激光器阻抗特性实验研究

利用自行研制的传感器和测量装置 ,通过对射频放电电压电流以及其相位角的测定 ,结合放电管的等效电路 ,对射频激励激光器阻抗特性进行研究 ,得出射频激励激光器等离子体的伏安特性曲线 ,以及等离子体电阻、容抗与气体压强、放电电流之间的实验曲线。     

射频激励的铜和金离子激光器中等离子体特性的实验研究

通过对射频 (RF)放电电压电流以及其相位角的精确测定 ,结合射频放电的物理模型与等效电路 ,对金属铜和金电极He气中的射频放电进行实验研究 ,得出射频放电溅射型铜和金离子等离子体电阻、容抗、鞘层厚度与电流密度的关系 ;在Goydak模型的基础上计算出电子密度 ,得出电子密度与气体压强、放电电流密度之间的实验曲线 ,并对两电极进行了比较。     

射频气体放电激励电压电流及相位传感器的研制

由于分布电容的存在而造成对仪器的干扰，使得对射频放电电压电流及相位的测量，到目前为止仍然是比较困难和昂贵的；使用简单的电压电流探头而无干扰地精确地测量出电压、电流和相位角，从而计算出复合阻抗几乎是不可能的。这里介绍一种自行研制的传感器，结合网络分析理论，采用数字与计算机技术实现对射频放电电压、电流及相位角精确的测量，经实际使用精度高，抗干扰能力强，价格低廉。     

射频放电激励激光器阻抗特性实验研究

利用自行研制的传感器＾「１」,通过对射频放电电压电源以及其相位角的精确测定,结合放电管的等效电路,对内置电极射频气体放电激励激光阻抗特性进行研究,得出内置电极射频气体放电激励激光器等离子体的伏安特性曲线,以及等离子体电阻和气体压强、气体放电电流之间的关系。     

Adaptive Gabor transformation for image processing

An algorithm that computes the Gabor coefficients of an image is presented. An adaptive filter that uses the complex least mean-square algorithm for their computation is proposed, and its numerical characteristics are discussed. It is shown that the filter is stable under certain conditions. Because the Gabor transformation seems to be an excellent tool for image compression, the efficiency of information coding using the Gabor coefficients is investigated and compared with coding that uses the coefficients of the discrete cosine transformation (DCT). Properties of both transformations are discussed.