基于Gabor滤波器的皮肤纹理图像增强

在疗效化妆品中,常常需要对护肤品的性能和效果进行分析。皮肤纹理的检测是客观衡量疗效化妆品的有效手段。基于计算机视觉技术的皮肤纹理分析,对拍摄的皮肤图像要进行图像预处理,增强图像,为后续的分析提供有效的数据。采用经过微调的定向的Gabor滤波器进行增强图像,通过实验得出Gabor滤波器不仅抑制噪声的效果好,还保留了皮肤图像的整体和局部特征。     

Gabor filters as texture discriminator

The present paper presents a model for texture discrimination based on Gabor functions. In this model the Gabor power spectrum of the micropatterns corresponding to different textures is calculated. A function that measures the difference between the spectrum of two micropatterns is introduced and its values are correlated with human performance in preattentive detection tasks. In addition, a two stage algorithm for texture segregation is presented. In the first stage the input image is transformed via Gabor filters into a representation image that allows discrimination between features by means of intensity differences. In the second stage the borders between areas of different textures are found using a Laplacian of Gaussian operator. This algorithm is sensitive to energy differences, rotation and spatial frequency and is insensitive to local translation. The model was tested by means of several simulations and was found to be in good correlation with known psychophysical characteristics as texton based texture segregation and micropattern density sensitivity. However, this simple model fails to predict human performance in discrimination tasks based on differences in the density of terminators. In this case human performance is better than expected.     

不同空间频率通道的深度知觉及其质地的影响

我们设计了四种不同质地的随机点立体图对(RDS),并按照Wilson的空间频率通道的频带,分别利用Butterworth滤波器及Gabor函数对之进行了数字滤波处理,然后进行各种左右眼配对观察.得结果如下:1、Gabor函数滤波后图对的匹配结果好于Butterworth滤波器滤波后的图对.2、滤波后图对的匹配效果与图象质地有关.3、不同滤波的图对匹配大多也能形成立体感.4、在体视不同通道图对的匹配中,右眼对图象的高频成分更为敏感.     

基于视觉编码的图象处理研究──Ⅰ．原理、算法及实现

提出Ｇａｂｏｒ小波表达的概念及其在图象处理中实现的数学模型和算法框架,主要解决了运算中非正交性问题、收敛性问题,使得该算法及模型可以实现图象的分解和重建,并且可以应用于对视觉编码理论的解释。     

基于视觉编码的图象处理研究──Ⅰ．原理、算法及实现

提出Ｇａｂｏｒ小波表达的概念及其在图象处理中实现的数学模型和算法框架,主要解决了运算中非正交性问题、收敛性问题,使得该算法及模型可以实现图象的分析和重建,并且可以应用于对视觉编码理论的解释。     

Configuration effects on texture transparency

This study examined the factors producing the perception of transparency between overlaid regions composed of Gabor micro-patterns as functions of their spatial frequency, separation of overlaid regions, and types of orientation modulation. The results showed that the likelihood of perceiving transparency was high both when (1) the difference in Gabor spatial frequency between regions was large, and (2) the region boundary, which was formed by short-range orientation differences in the Gabor micro-patterns, clearly emerged. We conclude that texture transparency appears to result from an interaction between a boundary-detection mechanism defining the shape of each region and a surface-detection mechanism assigning the boundary.     

Is slowness a learning principle of the visual cortex?

Slow feature analysis is an algorithm for extracting slowly varying features from a quickly varying signal. It has been shown in network simulations on one-dimensional stimuli that visual invariances to shift and other transformations can be learned in an unsupervised fashion based on slow feature analysis. More recently, we have shown that slow feature analysis applied to image sequences generated from natural images using a range of spatial transformations results in units that share many properties with complex and hypercomplex cells of the primary visual cortex. We find cells responsive to Gabor stimuli with phase invariance, sharpened or widened orientation or frequency tuning, secondary response lobes, end-stopping, and cells selective for direction of motion. These results indicate that slowness may be an important principle of self-organization in the visual cortex.     

Integrating image quality in 2nu-SVM biometric match score fusion

This paper proposes an intelligent 2nu-support vector machine based match score fusion algorithm to improve the performance of face and iris recognition by integrating the quality of images. The proposed algorithm applies redundant discrete wavelet transform to evaluate the underlying linear and non-linear features present in the image. A composite quality score is computed to determine the extent of smoothness, sharpness, noise, and other pertinent features present in each subband of the image. The match score and the corresponding quality score of an image are fused using 2nu-support vector machine to improve the verification performance. The proposed algorithm is experimentally validated using the FERET face database and the CASIA iris database. The verification performance and statistical evaluation show that the proposed algorithm outperforms existing fusion algorithms.     

A neural network model for texture discrimination

A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.     

Stereo disparity computation using Gabor filters

A solution to the correspondence problem for stereopsis is proposed using the differences in the complex phase of local spatial frequency components. One-dimensional spatial Gabor filters (Gabor 1946; Marcelja 1980), at different positions and spatial frequencies are convolved with each member of a stereo pair. The difference between the complex phase at corresponding points in the two images is used to find the stereo disparity. Disparity values are combined across spatial frequencies for each image location. Three-dimensional depth maps have been computed from real images under standard lighting conditions, as well as from random-dot stereograms (Julesz 1971). The algorithm can discriminate disparities significantly smaller than the width of a pixel. It is possible that a similar mechanism might be used in the human visual system.     

基于Gabor小波和特征加权的红刺舌象分类

红刺为舌尖、边、根等处突出之刺状物。根据红刺类型,并结合其他颜色、纹理或形状特征,中医能够快速准确的识别出多种疾病,如阑尾炎,支气管炎等。Gabor小波是纹理分析领域一种应用较多也较为成功的方法,本文以这种方法提取特征,在此基础上使用特征加权的聚类方法对红刺舌象进行识别分类,取得了较好的结果。     

Saliency computation via whitened frequency band selection

Many saliency computational models have been proposed to simulate bottom-up visual attention mechanism of human visual system. However, most of them only deal with certain kinds of images or aim at specific applications. In fact, human beings have the ability to correctly select attentive focuses of objects with arbitrary sizes within any scenes. This paper proposes a new bottom-up computational model from the perspective of frequency domain based on the biological discovery of non-Classical Receptive Field (nCRF) in the retina. A saliency map can be obtained according to the idea of Extended Classical Receptive Field. The model is composed of three major steps: firstly decompose the input image into several feature maps representing different frequency bands that cover the whole frequency domain by utilizing Gabor wavelet. Secondly, whiten the feature maps to highlight the embedded saliency information. Thirdly, select some optimal maps, simulating the response of receptive field especially nCRF, to generate the saliency map. Experimental results show that the proposed algorithm is able to work with stable effect and outstanding performance in a variety of situations as human beings do and is adaptive to both psychological patterns and natural images. Beyond that, biological plausibility of nCRF and Gabor wavelet transform make this approach reliable.     

基于灰度共生矩阵的人体皮肤纹理分析

对纹理图像的分析及特征提取是近年来图像处理领域的研究热点,在现实中有广泛的应用价值。灰度共生矩阵已被理论证明是图像纹理分析的一个很好的方法。为了使灰度共生矩阵所提取的特征值能够更好地描述皮肤老化的灰度分布信息,以不同年龄层的女性的不同部位的皮肤纹理图像作为研究对象,对采集到的图像进行预处理,采用灰度共生矩阵法提取纹理的特征值,通过统计分析得出了特征值的变化规律。实验结果对皮肤老化研究及其纹理分析有参考意义。     

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.     

Iris Identification Technology Based on Multiwavelets

1IntroductionIris identification is reputed to be one of the mostreliable biometric identification technologies.Wavelettheory has been widely used in feature analysis of the irisimage-the key to this technology.The classical irisrecognition algorithms were developed by Daugman[1]and Wildes[2].Zero-crossings of the wavelet transformwas presented by Boles[3].Gabor filter optimizationdesign for iris texture analysis and a multi-matchingsystem based on a simplified deformable model of thehuman i…     

Texture discrimination by Gabor functions

A 2D Gabor filter can be realized as a sinusoidal plane wave of some frequency and orientation within a two dimensional Gaussian envelope. Its spatial extent, frequency and orientation preferences as well as bandwidths are easily controlled by the parameters used in generating the filters. However, there is an uncertainty relation associated with linear filters which limits the resolution simultaneously attainable in space and frequency. Daugman (1985) has determined that 2D Gabor filters are members of a class of functions achieving optimal joint resolution in the 2D space and 2D frequency domains. They have also been found to be a good model for two dimensional receptive fields of simple cells in the striate cortex (Jones 1985; Jones et al. 1985).The characteristic of optimal joint resolution in both space and frequency suggests that these filters are appropriate operators for tasks requiring simultaneous measurement in these domains. Texture discrimination is such a task. Computer application of a set of Gabor filters to a variety of textures found to be preattentively discriminable produces results in which differently textured regions are distinguished by firstorder differences in the values measured by the filters. This ability to reduce the statistical complexity distinguishing differently textured region as well as the sensitivity of these filters to certain types of local features suggest that Gabor functions can act as detectors of certain texton types. The performance of the computer models suggests that cortical neurons with Gabor like receptive fields may be involved in preattentive texture discrimination.     

Curvelet Based Offline Analysis of SEM Images

Manual offline analysis, of a scanning electron microscopy (SEM) image, is a time consuming process and requires continuous human intervention and efforts. This paper presents an image processing based method for automated offline analyses of SEM images. To this end, our strategy relies on a two-stage process, viz. texture analysis and quantification. The method involves a preprocessing step, aimed at the noise removal, in order to avoid false edges. For texture analysis, the proposed method employs a state of the art Curvelet transform followed by segmentation through a combination of entropy filtering, thresholding and mathematical morphology (MM). The quantification is carried out by the application of a box-counting algorithm, for fractal dimension (FD) calculations, with the ultimate goal of measuring the parameters, like surface area and perimeter. The perimeter is estimated indirectly by counting the boundary boxes of the filled shapes. The proposed method, when applied to a representative set of SEM images, not only showed better results in image segmentation but also exhibited a good accuracy in the calculation of surface area and perimeter. The proposed method outperforms the well-known Watershed segmentation algorithm.     

Visual perception of texture regularity: Conjoint measurements and a wavelet response-distribution model

Texture regularity, such as the repeating pattern in a carpet, brickwork or tree bark, is a ubiquitous feature of the visual world. The perception of regularity has generally been studied using multi-element textures in which the degree of regularity has been manipulated by adding random jitter to the elements’ positions. Here we used three-factor Maximum Likelihood Conjoint Measurement (MLCM) for the first time to investigate the encoding of regularity information under more complex conditions in which element spacing and size, in addition to positional jitter, were manipulated. Human observers were presented with large numbers of pairs of multi-element stimuli with varying levels of the three factors, and indicated on each trial which stimulus appeared more regular. All three factors contributed to regularity perception. Jitter, as expected, strongly affected regularity perception. This effect of jitter on regularity perception is strongest at small element spacing and large texture element size, suggesting that the visual system utilizes the edge-to-edge distance between elements as the basis for regularity judgments. We then examined how the responses of a bank of Gabor wavelet spatial filters might account for our results. Our analysis indicates that the peakedness of the spatial frequency (SF) distribution, a previously favored proposal, is insufficient for regularity encoding since it varied more with element spacing and size than with jitter. Instead, our results support the idea that the visual system may extract texture regularity information from the moments of the SF-distribution across orientation. In our best-performing model, the variance of SF-distribution skew across orientations can explain 70% of the variance of estimated texture regularity from our data, suggesting that it could provide a candidate read-out for perceived regularity.     

Texture analysis of cervical cell nuclei by segmentation of chromatin patterns.

Texture parameters of the nuclear chromatin pattern can contribute to the automated classification of specimens on the basis of single cell analysis in cervical cytology. Current texture parameters are abstract and therefore hamper understanding. In this paper texture parameters are described that can be derived from the chromatin pattern after segmentation of the nuclear image. These texture parameters are more directly related to the visual properties of the chromatin pattern. The image segmentation procedure is based on a region grow algorithm which specifically isolates high chromatin density. The texture analysis method has been tested on a data set of images of 112 cervical nuclei on photographic negatives digitized with a step size of 0.125 micron. The preliminary results of a classification trial indicate that these visually interpretable parameters have promising discriminatory power for the distinction between negative and positive specimens.