Topographic effects on bidirectional and hemispherical reflectancescalculated with a geometric-optical canopy model

The effects of topography on both the bidirectional reflectance distribution function (BRDF) and the hemispherical reflectance (surface albedo) of a forested scene are investigated with the Li-Strahler geometric-optical model. The Li-Strahler geometric-optical model treats a vegetation canopy as an assemblage of partially illuminated tree crowns of spheroidal shape, and through geometric optics and Boolean set theory, models the proportion of sunlit or shadowed canopy and background as functions of view angle, illumination angle, and crown geometry. The model has been modified to accommodate a sloping surface in its computation of bidirectional and hemispherical reflectance. When the BRDF of a flat vegetated surface is compared to the BRDF of a sloping surface that is similarly vegetated, the interaction of the illumination angle and the slope distort the shape of the BRDF. A hemispherical integration of this distorted BRDF provides an albedo for the sloping surface     

基于灰度分布特征的层次掌纹识别方法

目前掌纹识别算法主要集中在对掌纹图像所切取的ROI区域的研究,而对原始手部图像的灰度分布特征则讨论较少。在一定光照条件下,掌部不同位置的主线、皱纹和表层皮肤颜色的深浅在灰度图像上各自对应了不同的灰度级。该文提出一种利用手部尺寸和角度信息完成粗分类,借助单元信息熵的概念来分析手部图像的灰度分布特征从而完成细分类的层次掌纹识别方法。该方法不同于传统的对ROI区域进行特征提取的方法,直接利用整幅人手图像完成分类识别。在99类共990幅手部图像的数据库上进行的实验结果与PCA和LDA算法的对比表明,该算法具有比传统算法更高的鲁棒性,识别率也得到了较大幅度的提高。     

复杂光照条件下的多姿状鲁棒性人脸识别方法

为改善复杂光照条件下的多姿状鲁棒性人脸识别的效果,提出了小波变换与LBP的多姿状鲁棒性人脸识别方法.通过二维离散小波变换对人脸图像进行二级小波分解提取到低频特征信息分量,并以重构初始图像的方式实现降噪滤波处理,滤除低频光照分量后完成复杂光照补偿;继续分解复杂光照补偿后的图像,采用LBP算子对子图像的鲁棒性部分纹理特征进...     

The bidirectional polarized reflectance model of soil

Soil albedo is a critical parameter affecting the Earth's climate and environment. In remote sensing data, analysis of the soil bidirectional reflectance distribution function (BRDF) has to be known. Several models for bidirectional reflectance over soil have been developed. The Hapke bidirectional reflectance model has been widely used for soil modeling. Polarization of radiation reflected by soil carries important information of soil properties. The polarized light always goes with the bidirectional reflectance. Therefore, polarization reflectance of a ground target carries equivalent important information as bidirectional reflectance. Detecting multiangle polarization information of soil becomes a new method in quantitative remote sensing. In this paper, we analyzed the existence of polarization on the soil surface in a 2/spl pi/ space and compared the bidirectional reflectance with the bidirectional polarized reflectance. We then developed a new polarized BRDF model of soil as the bidirectional polarization distribution function (BPDF) model. The BPDF model helps to improve classification and quantitative analysis of soil.     

Using Zernike moments for the illumination and geometry invariantclassification of multispectral texture

We develop a method for recognizing color texture independent of rotation, scale, and illumination. Color texture is modeled using spatial correlation functions defined within and between sensor bands. Using a linear model for surface spectral reflectance with the same number of parameters as the number of sensor classes, we show that illumination and geometry changes in the scene correspond to a linear transformation of the correlation functions and a linear transformation of their coordinates. A several step algorithm that includes scale estimation and correlation moment computation is used to achieve the invariance. The key to the method is the new result that illumination, rotation, and scale changes in the scene correspond to a specific transformation of correlation function Zernike moment matrices. These matrices can be estimated from a color image. This relationship is used to derive an efficient algorithm for recognition. The algorithm is substantiated using classification results on over 200 images of color textures obtained under various illumination conditions and geometric configurations.     

Illumination invariant unmixing of sets of mixed pixels

The authors propose some statistics of distributions of sets of pixels corresponding to rough surfaces, which are illumination invariant and therefore they can characterize the distributions irrespective of the solar angle. The illumination invariant statistics are used to solve the linear spectral unmixing problem for sets of mixed pixels, taking into consideration the distortion introduced to the statistics due to surface texture. The spectral unmixing of sets of mixed pixels is formulated in terms of the invariant statistics and the method is demonstrated using simulated data     

Why illuminant direction is fundamental to texture analysis

The author shows that directed illumination used in the image acquisition process can act as a directional filter of three-dimensional texture. An image model of texture is presented which, given the illuminant vector, may be used to predict the directional characteristics of image texture. Simulations and the results of laboratory experiments are presented that confirm the predicted directional filtering effects. The image model is used to predict the output of a directional texture measure: Laws' L5E5 operator (K.I. Laws, 1980), Empirical results using four samples of isotropic texture confirm that the operator's output is significantly affected by changes in the angle of tilt of the illuminant. They also show that the model provides a good basis for predicting the behaviour of such operators. Finally the effect of changes in illuminant tilt on the distributions of the operator for two isotropic textures are presented. These results show that considerable misclassification would result in using an L5E5-based classifier if the illuminant tilt angle were changed between training and classification sessions     

Robust skin-roughness estimation based on co-occurrence matrix

As the interest in one’s appearance has recently increased, the demand for diagnosing skin conditions has also increased. However, conventional specialized skin diagnostic devices are generally expensive, and people have to visit a skin-care shop to diagnose their skin condition. This is time consuming and troublesome. In this paper, we propose a skin-roughness estimation method that uses a mobile-phone camera in daily environments. In order to achieve accurate evaluation, the illumination variation is alleviated using texture components of the facial skin image. We also propose a new feature-extraction method based on the gray-level co-occurrence matrix, which effectively measures the skin roughness from the texture components. The performance of the proposed method is compared with the conventional commonly used features, and we verify the superiority of the proposed method.     

激光跟踪中目标卫星表面BRDF对回波信号的影响

通过双向反射分布函数(BRDF)公式,模拟了空间激光主动照明跟踪中,相同材料、不同粗糙度下卫星表面的BRDF,得出了随着卫星表面材料粗糙度的增加,镜面反射分量越小,漫反射分量越大,双向反射分布散射角越宽,接收到的回波信号对方向的敏感性减小。同时模拟了入射角度对卫星表面BRDF的影响,得出了照明光束小角度入射、接收信号方向与照明光束方向一致时,镜面反射分量的增加增强了反馈信号,当大角度入射时,反馈信号急剧减小。当入射角大于34°时,通过卫星表面BRDF计算得到的最小接收功率,比之前把卫星目标看成朗伯体,通过激光雷达公式计算得到的最小接收功率小。得出了增加照明光束的发射功率为原来的5倍,或者增大接收口径为原来的2.5倍,可以消除大入射角度带来的接收功率的减小,使得系统有4倍的功率余量。     

Coupling of light into mechanically textured silicon solar cells: A ray tracing study

Light coupling into mechanically textured, encapsulated silicon solar cells is studied by three-dimensional comprehensive ray tracing with our newly developed programme SUNRAYS. We analyse periodic textures with symmetric and non-symmetric pyramids and grooves. the facet angles of the textures are varied systematically. the study considers the apparent daily motion of the sun and diffuse illumination. SUNRAYS accounts for the dependence of reflection and transmission on the wavelength, the angle of incidence and the polarization of light. As a result of basic interest, we show that the maximum average path length theoretically possible under isotropic illumination is actually achieved by numerous trapping schemes. As a result of practical interest, we demonstrate the outstanding performance of the simple prism groove texture, which yields a large current density while preserving a small surface when compared to all other textures investigated.     

Multiscale color invariants based on the human visual system

This paper proposes a new representation for color texture using a set of multiscale illuminant invariant features. The approach was specifically developed to investigate the feasibility of using machine vision to automatically monitor populations of animal species in ecologically sensitive regions, such as the Amazon Forest. The approach uses a combination of Finlayson's (1994) color angle idea and Gabor multichannel filters and was inspired by the multichannel model of the human visual system (HVS). Using a database of color textures from three species of Amazonian monkey, and also a previously published reference database of color regions, we show that the approach performs better than methods based on color angles or Gabor filters alone. The Monkey database was compiled from texture segments extracted from a video of the Amazon Forest using a spatio-temporal segmentation algorithm. The approach is evaluated by applying two different classification tests in order to measure the quality of the recognition features root mean square (RMS) analysis and receiver operating characteristic (ROC) analysis.     

Gradient and texture analysis for the classification of mammographic masses

Computer-aided classification of benign and malignant masses on mammograms is attempted in this study by computing gradient-based and texture-based features. Features computed based on gray-level co-occurrence matrices (GCMs) are used to evaluate the effectiveness of textural information possessed by mass regions in comparison with the textural information present in mass margins. A method involving polygonal modeling of boundaries is proposed for the extraction of a ribbon of pixels across mass margins. Two gradient-based features are developed to estimate the sharpness of mass boundaries in the ribbons of pixels extracted from their margins. A total of 54 images (28 benign and 26 malignant) containing 39 images from the Mammographic Image Analysis Society (MIAS) database and 15 images from a local database are analyzed. The best benign versus malignant classification of 82.1%, with an area (Az) of 0.85 under the receiver operating characteristics (ROC) curve, was obtained with the images from the MIAS database by using GCM-based texture features computed from mass margins. The classification method used is based on posterior probabilities computed from Mahalanobis distances. The corresponding accuracy using jack-knife classification was observed to be 74.4%, with Az = 0.67. Gradient-based features achieved Az = 0.6 on the MIAS database and Az = 0.76 on the combined database. The corresponding values obtained using jack-knife classification were observed to be 0.52 and 0.73 for the MIAS and combined databases, respectively.     

Improved semi-arid community type differentiation with the NOAAAVHRR via exploitation of the directional signal

Mapping semi-arid vegetation types at the community level is extremely difficult for optical sensors with large ground footprints such as the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Attempts to use solar wavelength AVHRR data in community type differentiation have often resulted in unacceptable classification errors which are usually attributed to noise from topographic and soil background variations, inaccurate reflectance retrieval and poor registration. One source of variation which is rarely accounted for adequately is the directional signal resulting from the combined effects of the surface bidirectional reflectance distribution function (BRDF) and the variation of viewing and illumination geometry as a function of scan angle, season, latitude and orbital overpass time. In this study, a linear semiempirical kernel-driven BRDF model is used to examine the utility:of the directional signal in community and cover type differentiation over discontinuous but statistically homogeneous semi-arid canopies in Inner Mongolia Autonomous Region, China, and New Mexico, USA. This research shows that the directional signal resulting from the physical structure of the canopy-soil complex can be retrieved to provide information which is highly complementary to that obtained in the spectral domain     

Low dimensional adaptive texture feature vectors from class distance and class difference matrices

In many popular texture analysis methods, second or higher order statistics on the relation between pixel gray level values are stored in matrices. A high dimensional vector of predefined, nonadaptive features is then extracted from these matrices. Identifying a few consistently valuable features is important, as it improves classification reliability and enhances our understanding of the phenomena that we are modeling. Whatever sophisticated selection algorithm we use, there is a risk of selecting purely coincidental "good" feature sets, especially if we have a large number of features to choose from and the available data set is limited. In a unified approach to statistical texture feature extraction, we have used class distance and class difference matrices to obtain low dimensional adaptive feature vectors for texture classification. We have applied this approach to four relevant texture analysis methods. The new adaptive features outperformed the classical features when applied to the most difficult set of 45 Brodatz texture pairs. Class distance and difference matrices also clearly illustrated the difference in texture between cell nucleus images from two different prognostic classes of early ovarian cancer. For each of the texture analysis methods, one adaptive feature contained most of the discriminatory power of the method.     

HSV色彩空间中的肤色特征及其新的识别参量

研究了色调(H)饱和度(S)亮度(V)色彩空间的各分量随光照变化的规律.实验发现:当光照强度发生变化或待测表面的曲度发生变化时,(S V)/2能够保持相对稳定;当皮肤的色调发生变化时,(S V)/2随之改变,但与H的变化方向相反.研究结果表明,可以将(S V)/H作为一个新的识别参量,不仅对光照度和照射角度有较好的鲁棒性,而且增强了HSV空间对人类皮肤的分辨能力.     

基于SIFT算法的异源遥感影像自动匹配研究

由于不同传感器、多时相、多分辨率、多波段的遥感图像的光谱特征、空间特征、纹理特征等存在较大差异,为遥感图像的匹配带来了困难。主要利用图像特征点提取方法,使用具有尺度不变特性的SIFT(Scale Invariance Feature Transform)方法,对异源遥感图像进行配准和图像进行拼接操作,并进一步对SIFT算法进行优化,采用双向匹配策略。实验证明该算法具有稳定、可靠、快速等特点,适用于存在光谱特征、空间特征、纹理特征差异的异源遥感图像的精确配准,同时实验验证了双向匹配算法用于SIFT特征点匹配中的优越性,证明其为一种好的匹配测度。     

Three-Dimensional Assessment of Skin Wounds Using a Standard Digital Camera

In this paper, after an overview of the literature concerning the imaging technologies applied to skin wounds assessment, we present an original approach to build 3-D models of skin wounds from color images. The method can deal with uncalibrated images acquired with a handheld digital camera with free zooming. Compared with the cumbersome imaging systems already proposed, this novel solution uses a low-cost and user-friendly image acquisition device suitable for widespread application in health care centers. However, this method entails the development of a robust image processing chain. An original iterative matching scheme is used to generate a dense estimation of the surface geometry from two widely separated views. The best configuration for taking photographs lies between 15^deg and 30^deg for the vergency angle. The metric reconstruction of the skin wound is fully automated through self-calibration. From the 3-D model of the skin wound, accurate volumetric measurements are achieved. The accuracy of the inferred 3-D surface is validated by registration to a ground truth and repetitive tests on volume. The global precision around 3% is in accordance with the clinical requirement of 5% for assessing the healing process.     

大视场高分辨率数字全息成像技术综述

数字全息作为一种干涉成像方式,能够准确记录物体的相位信息,具有快速、无损、三维成像等优势,被广泛应用于生物成像与材料科学等领域。与其他光学成像方式相同,数字全息也面临分辨率与成像视场互为限制而导致空间带宽积受限的问题。研究人员提出了计算照明、计算调制与计算探测等方法,通过牺牲成像系统的时间、偏振等自由度来扩展其空间带宽积。文中分析了光学系统信息承载能力的理论基础,总结了近年来大视场高分辨率的数字全息成像技术,介绍了倾斜照明、结构光照明、随机调制照明、多位置综合孔径探测和像素超分辨等方法实现分辨率增强,以及基于角度复用的视场扩展的原理及具体实现,对不同方法进行了比较和分析,并对提高分辨率以及扩大视场的途径进行了展望。