Texture segmentation using 2-D Gabor elementary functions

Many texture-segmentation schemes use an elaborate bank of filters to decompose a textured image into a joint space/spatial-frequency representation. Although these schemes show promise, and although some analytical work has been done, the relationship between texture differences and the filter configurations required to distinguish them remain largely unknown. This paper examines the issue of designing individual filters. Using a 2-D texture model, we show analytically that applying a properly configured bandpass filter to a textured image produces distinct output discontinuities at texture boundaries; the analysis is based on Gabor elementary functions, but it is the bandpass nature of the filter that is essential. Depending on the type of texture difference, these discontinuities form one of four characteristic signatures: a step, ridge, valley, or a step change in average local output variation. Accompanying experimental evidence indicates that these signatures are useful for segmenting an image. The analysis indicates those texture characteristics that are responsible for each signature type. Detailed criteria are provided for designing filters that can produce quality output signatures. We also illustrate occasions when asymmetric filters are beneficial, an issue not previously addressed     

Texture segmentation using Gabor modulation/demodulation

Two-dimensional Gabor filters are used to segment images into regions of specific spatial frequency or orientation characteristic. The images are transformed into a modulated narrowband signal whose envelope coincides with the region(s) whose characteristics the filter is tured to.     

Dynamic coherency identification of generators using self-organising feature maps

An important step in constructing dynamic equivalents of large power systems is the coherency identification and grouping of generators. Self-organising feature maps can do this task, if the attribute vectors, which characterise the features of the generator dynamics inside the network are well chosen. It is shown in the paper that the principal components of the correlation matrix of the simulated time responses of the generators after faults are especially suitable to form the attribute vectors. The results are compared with the use of right eigenvectors and participation factors of the linearised system matrix as attribute vectors.     

Texture based segmentation using graph cut and Gabor filters

This paper describes a method for texture based segmentation. Texture features are extracted by applying a bank of Gabor filters using two-sided convolution strategy. Probability texture model is represented by Gaussian mixture that is trained with the Expectation-maximization algorithm. Texture similarity, obtained this way, is used like the input of a Graph cut method. We show that the combination of texture analysis and the Graph cut method produce good results.     

Visual feature analysis by the self-organising maps

The Self-Organising Map (SOM) is an Artificial Neural Network (ANN) model consisting of a regular grid of processing units. A model of some multidimensional observation, e.g. a class of digital images, is associated with each unit. The map attempts to represent all the available observations using a restricted set of models. In unsupervised learning, the models become ordered on the grid so that similar models are close to each other. We review here the objective functions and learning rules related to the SOM, starting from vector coding based on a Euclidean metric and extending the theory of arbitrary metrics and to a subspace formalism, in which each SOM unit represents a subspace of the observation space. It is shown that this Adaptive-Subspace SOM (ASSOM) is able to create sets of wavelet- and Gabor-type filters when randomly displaced or moving input patterns are used as training data. No analytical functional form for these filters is thereby postulated. The same kind of adaptive system can create many other kinds of invariant visual filters, like rotation or scale-invariant filters, if there exist corresponding transformations in the training data. The ASSOM system can act as a learning feature-extraction stage for pattern recognisers, being able to adapt to arbitrary sensory environments. We then show that the invariant Gabor features can be effectively used in face recognition, whereby the sets of Gabor filter outputs are coded with the SOM and a face is represented by the histogram over the SOM units.     

基于Gabor小波的几何活动围道纹理图像分割方法

用活动围道分割纹理图像时,纹理经常被分割为一个个独立的区域,影响了分割的质量。针对此问题,提出了一种基于Gabor小波的几何活动围道分割新方法。该方法先用Gabor小波对纹理图像进行特征提取,再用几何活动围道模型进行分割,模型求解时采用了无需初始化的曲线演化方法,减少了计算量。对自然界真实图像和合成纹理图像的分割实验结果说明,与传统几何模型分割法相比,提出的分割方法精度高、速度快。     

融合纹理特征的两阶段聚类分割算法

提出一种融合纹理特征的两阶段聚类分割算法。首先,选择纹理特征、差分均值和颜色分量这3个特征,组成一个分割所用的特征矢量;然后,使用直方图对特征矢量进行初始聚类中心和类别数的估算;最后,通过模糊C均值算法对特征矢量进行聚类。该算法有效地克服了模糊C均值(FCM)容易陷入局部最优的缺陷,使聚类结果更加精确。实验结果表明该方法比一些现存方法的分割效果要好。     

Unsupervised texture segmentation using Gabor filters

This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories.     

一种基于纹理特征融合的SAR图像分割方法

在由若干灰度共生矩阵纹理统计量进行特征融合后所生成的图像上,定义多分辨双Markov-GAR模型,采用多分辨MPM参数估计方法及相应的无监督分割算法,对SAR图像进行纹理分割。该方法既利用了像素的灰度信息,也利用了像素的空间位置信息,削弱了斑点噪声对分割的影响。实验表明对于一些高分辨SAR图像,该方法与单纯基于灰度图像上的多分辨双Markov-GAR模型纹理分割相比,分割精度得以提高。     

Texture segmentation using Voronoi polygons

Textures are defined in terms of primitives called tokens. A texture segmentation algorithm based on the Voronoi tessellation is discussed. The algorithm first builds the Voronoi tessellation of the tokens that make up the textured image. It then computes a feature vector for each Voronoi polygon. These feature vectors are used in a probabilistic relaxation labeling on the tokens, to identify the interior and the border regions of the textures. Some experimental results are shown     

Texture segmentation using fractal dimension

This paper deals with the problem of recognizing and segmenting textures in images. For this purpose the authors employ a technique based on the fractal dimension (FD) and the multi-fractal concept. Six FD features are based on the original image, the above average/high gray level image, the below average/low gray level image, the horizontally smoothed image, the vertically smoothed image, and the multi-fractal dimension of order two. A modified box-counting approach is proposed to estimate the FD, in combination with feature smoothing in order to reduce spurious regions. To segment a scene into the desired number of classes, an unsupervised K-means like clustering approach is used. Mosaics of various natural textures from the Brodatz album as well as microphotographs of thin sections of natural rocks are considered, and the segmentation results to show the efficiency of the technique. Supervised techniques such as minimum-distance and k-nearest neighbor classification are also considered. The results are compared with other techniques     

Texture segmentation using wavelet transform

Texture analysis such as segmentation and classification plays a vital role in computer vision and pattern recognition and is widely applied to many areas such as industrial automation, bio-medical image processing and remote sensing. This paper describes a novel technique of feature extraction for characterization and segmentation of texture at multiple scales based on block by block comparison of wavelet co-occurrence features. The performance of this segmentation algorithm is superior to traditional single resolution techniques such as texture spectrum, co-occurrences, local linear transforms, etc. The results of the proposed algorithm are found to be satisfactory.     

基于排列组合熵和灰度特征的纹理分割

提出了一种基于排列组合熵和灰度特征的纹理分割方法。该方法将不同方向上的排列组合熵与灰度均值、灰度方差结合起来构成一个多维特征向量,利用模糊C均值聚类算法进行聚类实现纹理图像的分割。实验结果表明该方法对纹理分布均匀的图像有着良好的分割效果。在保持较高纹理分割精度的前提下,该方法能减小计算复杂度,并且具有较强的鲁棒性和抗噪声能力。     

Integrating feature maps and competitive layer architectures for motion segmentation

We present a generic approach to integrate feature maps with a competitive layer architecture to enable segmentation by a competitive neural dynamics specified in terms of the latent space mappings constructed by the feature maps. We demonstrate the underlying ideas for the case of motion segmentation, using a system that employs Unsupervised Kernel Regression (UKR) for the creation of the feature maps, and the Competitive Layer Model (CLM) for the competitive layer architecture. The UKR feature maps hold learned representations of a set of candidate motions and the CLM dynamics, working on features defined in the UKR domain, implements the segmentation of observed trajectory data according to the competing candidates. We also demonstrate how the introduction of an additional layer can provide the system with a parametrizable rejection mechanism for previously unknown observations. The evaluation on trajectories describing four different letters yields improved classification results compared to our previous, pure manifold approach.     

Texture segmentation using hierarchical wavelet decomposition

This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.     

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.     

Infrared ship target segmentation through integration of multiple feature maps

We investigate the issue of ship target segmentation in infrared (IR) images, and propose an efficient method based on feature map integration. It consists of mainly two procedures: salient region detection based on multiple feature map integration and salient region segmentation based on locally adaptive thresholding. Firstly, a saliency map is constructed by integrating multiple features of IR ship targets, including gray level intensity, local contrast, salient linear structures, and edge strength. Secondly, we propose an adaptive thresholding method to segment each local salient region, and a target selection procedure based on shape features is used to remove background and obtain the true target. Experimental results show that the proposed method performs well for IR ship target segmentation. The advantage of the proposed method is demonstrated in both visual and quantitative comparisons, especially for IR images with a bright background or a ship target close to port.     

Palmprint feature extraction using 2-D Gabor filters

Biometric identification is an emerging technology that can solve security problems in our networked society. A few years ago, a new branch of biometric technology, palmprint authentication, was proposed (Pattern Recognition 32(4) (1999) 691) whereby lines and points are extracted from palms for personal identification. In this paper, we consider the palmprint as a piece of texture and apply texture-based feature extraction techniques to palmprint authentication. A 2-D Gabor filter is used to obtain texture information and two palmprint images are compared in terms of their hamming distance. The experimental results illustrate the effectiveness of our method.     

Monitoring of caliper sensor fouling in a board machine using self-organising maps

This paper presentes a process-monitoring scheme utilising adaptive self-organising maps (SOM) to detect process conditions that lead to the fouling of a caliper sensor in a board machine. The scheme is based on mapping on a SOM the process measurements and the calculated variables which provide insight into the chemical phenomena involved in fouling to classify faulty process conditions. The time-variant nature of the board making process was taken into account by regularly re-training the SOM. The monitoring scheme is demonstrated with industrial data, and the results are presented and discussed.