基于多特征组合的图像纹理分类

在对纹理图像进行特征提取的算法中,高斯马尔可夫随机场(GMRF)、局部二值模式(LBP)和灰度共生矩阵(GLCM)这三种算法应用的较为广泛.常见的图像纹理分类做法是取某一种特征提取算法得到各种纹理的特征空间,进而配合分类算法进行分类.然而,这种做法的不足之处在于未能充分利用各种特征之间的关联,且选取某一种特征提取算法建...     

基于多级纹理特征的深度信念网络人脸识别算法

为解决人脸特征提取过程中局部特征缺失的问题,借助局部二值模式(LBP)与方向梯度直方图(HOG)提出一种基于多级纹理特征融合的深度信念网络人脸识别算法。以提取局部纹理特征以及边缘纹理特征为出发点,对人脸图像进行三级纹理特征提取。使用MB-LBP提取初级纹理特征;在此基础上进行改进的CS-LBP图像特征提取作为二级纹理特征;使用HOG算子在二级纹理特征上完成三级纹理特征提取。将二级和三级纹理特征直方图顺序串联融合后输入到深度信念网络(DBN)逐层贪婪训练,优化网络参数,并用优化的网络在ORL、YELA人脸标准库中进行测试,识别率均在92%以上。该算法与传统算法(SVM、PCA)相比较拥有更好的人脸识别效果,同时也表明了局部纹理特征的改善为识别过程的特征提取提供强有力的保障,为人脸识别的进一步研究开拓新思路。     

基于CCA的图像语义特征提取的分析与研究

为了提高图像语义特征提取的精确度,克服目前大部分图像语义特征提取算法中,因图像特征提取不当,导致特征参数不能全面反映图像语义的问题,提出了一种基于典型相关分析(CCA)的特征融合的图像语义特征提取方法。该方法首先采用圆形对称邻域取代传统的矩形邻域的方法,对局部二值模式(LBP)纹理特征进行了改进,然后采用高维小样本下典型相关分析对可伸缩颜色描述算子的颜色特征和改进的LBP纹理特征进行特征融合。实验结果表明,所提出的方法明显提高了图像语义特征提取的精确度,能有效地建立图像的低层特征与语义特征间的一致性。     

小波域颜色和纹理特征提取及图像检索方法研究

对小波域图像颜色和纹理特征的提取方法进行研究,在图像颜色特征提取方面,提出一种基于分块的HSI分量低频子带颜色特征提取方法,该方法首先根据人眼对图像的关注度对图像进行分块,对每一块的HSI分量的小波分解低频子带的颜色特征进行提取,并通过加权获得图像的颜色特征;在图像纹理特征提取方面,提出一种基于高频子带灰度-差分基元共生矩阵的二阶统计量和各子带方向特征的纹理特征提取方法,增加了方向特性的纹理特征对图像纹理的刻画更加精细;在此基础上,提出一种综合利用所提出图像颜色和纹理特性的图像检索算法,提高了图像的检索精度.实验结果验证了所提出方法的有效性.     

融合多特征与随机森林的纹理图像分类方法

针对单一纹理特征与单一分类器对失真纹理图像分类识别率差的问题,提出了一种融合多特征与随机森林的纹理图像分类方法。利用改进的方向梯度直方图(HOG)特征提取方法以及局部二值模式(LBP)图像的灰度共生矩阵进行特征提取;将提取的特征矩阵级联组成一个新的特征矩阵,利用主成分分析法进行降维融合处理;降维融合后的特征矩阵输入随机森林,通过融合投票得到最终的识别率。在KTH-TIPS失真纹理图像库上进行对比实验,结果表明:采用融合多特征与随机森林的分类方法提高了失真纹理图像的分类正确率,且具有更好的实时性。     

改进FCM和LFP相结合的白细胞图像分类

研究白细胞图像分类识别中有效的图像分割与特征提取方法,以提高白细胞图像的正确识别率.由于某些白细胞(粒细胞)中颗粒的存在,严重影响细胞核与细胞质区域的正确分割,通过将空间信息与核函数融入模糊C-均值聚类(FCM)算法,提出一种改进的FCM算法.应用该算法对白细胞图像进行分割,并采用数学形态学方法对分割后的图像进行处理,获得了很好的分割效果,解决了粒细胞的质核分割难题.对于细胞的纹理特征提取,通过对局部二值模式(LBP)中阈值参数的模糊化,建立了基于局部模糊模式(LFP)的纹理特征提取算法.运用本文方法进行图像分割和纹理提取,以支持向量机作为分类器,对CellAtlas的100幅白细胞图像进行了分类识别的实验,结果表明白细胞的正确识别率达到93％.     

基于BEMD和LBP提取特征的纹理分类

对于纹理图像的分类,采用二维经验模式分解将图像分解成一系列的固有模态函数(IMF)和残差,并结合局部二值模式(LBP)对所提取到的各IMF图像和残差图像进行特征提取的方法。为了验证算法的有效性,对自然纹理进行特征提取,并结合支持向量机(SVM)算法对提取的特征向量进行分类,分类精确度达到98%以上。     

傅里叶频谱径角特征的植物相似性

提出一种基于图像傅里叶频谱径角变换的纹理特征提取方法,对藤茎类植物的显微图像进行了相似性度量测试．与常见的两种纹理特征提取方法进行比较,能更准确的定义和描述该类植物图像的纹理特征和进行相似性度量．     

一种基于混合模型的特征提取方法

图像特征提取是图像过滤的关键步骤之一.对自然的原始图像进行特征提取时,鉴于单一的图像特征不能很好地表征图像视觉信息,因此,对图像的颜色、纹理特征进行分析、处理是十分必要的.针对这一问题,提出了一种基于混合模型的特征提取方法,结合图像颜色及纹理特征的优点,建立混合模型,并采用PCA(Principal Component Analysis)方法对其高维的特征信息进行处理.实验结果表明这种综合方法能够以较好的特征信息来表示图像视觉内容,达到更准确分类过滤的目的.     

纹理特征提取方法综述

图像纹理特征提取是图像纹理分类与分割的基础,广泛应用于医学图像、遥感图像等领域。对近期的主流纹理特征提取方法以及融合方法进行了分析与总结,并探讨了纹理特征提取方法未来的研究方向。首先详细介绍了12个主流的公开纹理数据集的特点及其适用场景;接着总结了近几年出现的一些纹理特征提取方法,并根据特征融合方式从多个角度对纹理特征融合方法进行了分类介绍;最后总结了纹理特征提取存在的难点和挑战,并对未来的纹理特征提取方法研究方向进行思考和讨论。     

Image map correspondence for mobile robot self-location usingcomputer graphics

The use of computer graphics in estimating the position of an autonomous mobile robot navigating in an outdoor mountainous environment is discussed. A digital elevation map (DEM) of the area in which the robot is to navigate is given, and the robot is equipped with a camera that can be panned and tilted, a compass, and an altimeter. The position of the robot is estimated by establishing a correspondence between the images acquired by the camera on the robot (actual images) and the images generated from the DEM (predicted images) using computer graphics techniques. Features are extracted from the predicted images and the actual images that are used in establishing the correspondence. The features used are the horizon line contours (HLCs) in the images. To reduce the search space a constrained search paradigm is used. Geometric constraints help prune the search space significantly     

Acquiring linear subspaces for face recognition under variable lighting

Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.     

Rock unit discrimination on Landsat TM,SIR-C and Radarsat images using spectral and textural information

The paper presents results for spectral and textural analysis of the rock units in Landsat Thematic Mapper (TM) images, dual-band (L and C) and dual-polarization (HH and HV) Shuttle Imaging Radar (SIR)-C images, and C-band HH polarization Standard Beam 4 and Extended High Incidence Beam 3 Radarsat images from a study area between California and Arizona, USA. Fractal dimension, lacunarity and grey-level co-occurrence matrix (GLCM) textural feature images were created from the SIR-C and Radarsat images. Fractal dimensions were calculated using a differential box counting method and lacunarity measures were obtained using a new grey-scale lacunarity estimation method for 36 sample images extracted from the SIR-C and Radarsat images. The fractal dimension and lacunarity curves and class signature separability analysis show that, for rock unit discrimination using image textural features in the study area, the SIR-C L-HH image is more suitable than other SIR-C images and Radarsat images, and that co-polarization (HH) generally provides more textural information than cross-polarization (HV) in the study area. The study also shows that lacunarity measures can reveal the scaling properties of radar image textures for rock units. The combination of spectral information from Landsat TM images and textural information from radar images improves the image classification accuracy of rock units in the study area.     

Multi-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

Super-resolution (SR) methods are effective for generating a high-resolution image from a single low-resolution image. However, four problems are observed in existing SR methods. (1) They cannot reconstruct many details from a low-resolution infrared image because infrared images always lack detailed information. (2) They cannot extract the desired information from images because they do not consider that images naturally come at different scales in many cases. (3) They fail to reveal different physical structures of low-resolution patch because they extract features from a single view. (4) They fail to extract all the different patterns because they use only one dictionary to represent all patterns. To overcome these problems, we propose a novel SR method for infrared images. First, we combine the information of high-resolution visible light images and low-resolution infrared images to improve the resolution of infrared images. Second, we use multiscale patches instead of fixed-size patches to represent infrared images more accurately. Third, we use different feature vectors rather than a single feature to represent infrared images. Finally, we divide training patches into several clusters, and multiple dictionaries are learned for each cluster to provide each patch with a more accurate dictionary. In the proposed method, clustering information for low-resolution patches is learnt by using fuzzy clustering theory. Experiments validate that the proposed method yields better results in terms of quantization and visual perception than the state-of-the-art algorithms.     

Applications of tensor theory to object recognition and orientation determination

A method is developed by which images resulting from orthogonal projection of rigid planar-patch objects arbitrarily oriented in three-dimensional (3-D) space may be used to form systems of linear equations which are solved for the affine transform relating the images. The technique is applicable to complete images and to unlabeled feature sets derived from images, and with small modification may be used to transform images of unknown objects such that they represent images of those objects from a known orientation, for use in object identification. No knowledge of point correspondence between images is required. Theoretical development of the method and experimental results are presented. The method is shown to be computationally efficient, requiring O(N) multiplications and additions where, depending on the computation algorithm, N may equal the number of object or edge picture elements.     

基于GA的SAR图像中主干道路提取

从高分辨率合成孔径雷达(SAR)图像中提取道路及其他线性特征已成为目前遥感图像信息提取研究的热点。由于高分辨率SAR图像中，目标背景复杂，同时由于受相干斑噪声的影响，因此很难直接从原始图像数据中提取道路特征。为了能够从背景复杂，受斑点噪声干扰的高分辨率SAR图像中准确提取道路，提出了一种利用遗传算法提取主干道路的方法。该方法利用模糊C均值聚类法对滤波后的SAR图像进行无监督聚类，首先将图像分为林地、建筑物、道路等基本类，并将道路类像素从图像中分离出来，使问题得到简化；然后根据道路类像素的隶属度和道路像素灰度值的均匀特性来建立具体的道路模型；最后利用遗传算法搜索全局最优道路。实验结果表明，该方法可以很好地从SAR图像中提取各种主干道路。     

Single-frame super resolution of remote-sensing images by convolutional neural networks

Super resolution (SR) refers to generation of a high-resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single-frame or multi-frame that contains a collection of images acquired from slightly different views of the same observation area. In this study, two convolutional neural network (CNN)-based deep learning techniques are adapted in single-frame SR to increase the resolution of remote sensing (RS) images by a factor of 2, 3, and 4. In order to both preserve the colour information and speed up the algorithm, first an intensity hue saturation (IHS) transform is utilized and the SR techniques are only applied to the intensity channel of the images. Colour information is then restored with an inverse IHS transformation. We demonstrate the results of the proposed method on RS images acquired from Satellites Pour l’Observation de la Terre (SPOT) or Earth-observing satellites and Pleiades satellites with different spatial resolution. First synthetic LR images are created by downsampling, then structural similarity (SSIM) Index, peak signal-to-noise ratio (PSNR), Spectral Angle Mapper (SAM) and Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) values are calculated for a quantitative evaluation of the methods. Finally, the method, with better performance results, is tested within a real scenario, that is, with original LR images as the input. The obtained HR images demonstrated visible qualitative enhancements.     

Principal Angles Separate Subject Illumination Spaces in YDB and CMU-PIE

The theory of illumination subspaces is well developed and has been tested extensively on the Yale Face Database B (YDB) and CMU-PIE (PIE) data sets. This paper shows that if face recognition under varying illumination is cast as a problem of matching sets of images to sets of images, then the minimal principal angle between subspaces is sufficient to perfectly separate matching pairs of image sets from nonmatching pairs of image sets sampled from YDB and PIE. This is true even for subspaces estimated from as few as six images and when one of the subspaces is estimated from as few as three images if the second subspace is estimated from a larger set (10 or more). This suggests that variation under illumination may be thought of as useful discriminating information rather than unwanted noise.     

Invariants of six points and projective reconstruction from threeuncalibrated images

There are three projective invariants of a set of six points in general position in space. It is well known that these invariants cannot be recovered from one image, however an invariant relationship does exist between space invariants and image invariants. This invariant relationship is first derived for a single image. Then this invariant relationship is used to derive the space invariants, when multiple images are available. This paper establishes that the minimum number of images for computing these invariants is three, and the computation of invariants of six points from three images can have as many as three solutions. Algorithms are presented for computing these invariants in closed form. The accuracy and stability with respect to image noise, selection of the triplets of images and distance between viewing positions are studied both through real and simulated images. Applications of these invariants are also presented. Both the results of Faugeras (1992) and Hartley et al. (1992) for projective reconstruction and Sturm's method (1869) for epipolar geometry determination from two uncalibrated images with at least seven points are extended to the case of three uncalibrated images with only six points