基于局部边缘二值模式的图像检索

在定义局部边缘的基础上提出了局部边缘二值模式(LEBP),并结合Gabor滤波器将其扩展到多分辨率LEBP(MLEBP)。对传统的中心对称局部二值模式(CS-LBP)和方向局部二值模式(D-LBP)进行了改进,新描述符在不增加计算复杂度和提高特征维数的基础上,进一步融入了局部边缘信息。为验证新描述符的性能,采用3个通用的纹理图像库进行图像检索实验。结果表明,结合本文方法,明显提高了传统描述符的分辨能力。     

Coral reef image classification employing Improved LDP for feature extraction

This paper presents a scheme for feature extraction that can be applied for classification of corals in submarine coral reef images. In coral reef image classification, texture features are extracted using the proposed Improved Local Derivative Pattern (ILDP). ILDP determines diagonal directional pattern features based on local derivative variations which can capture full information. For classification, three classifiers, namely Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN) with four distance metrices, namely Euclidean distance, Manhattan distance, Canberra distance and Chi-Square distance, and Support Vector Machine (SVM) with three kernel functions, namely Polynomial, Radial basis function, Sigmoid kernel are used. The accuracy of the proposed method is compared with Local Binary pattern (LBP), Local Tetra Pattern (LTrP), Local Derivative Pattern (LDP) and Robust Local Ternary Pattern (RLTP) on five coral data sets and four texture data sets. Experimental results indicate that ILDP feature extraction method when tested with five coral data sets, namely EILAT, RSMAS, EILAT2, MLC2012 and SDMRI and four texture data sets, namely KTH-TIPS, UIUCTEX, CURET and LAVA achieves the highest overall classification accuracy, minimum execution time when compared to the other methods.     

一种基于二维局部二值模式的纹理图像分类方法

局部二值模式（Local Binary Pattern,LBP）在纹理分类中受到越来越多的关注,传统的基于局部二值模式的图像识别方法在LBP直方图统计时仅仅考虑到LBP模式值本身的数量统计,却忽略了模式值之间的相关性.针对这一问题,本文提出一种二维局部二值模式（Two Dimensional Local Binary Pattern,2DLBP）方法,并用于纹理图像识别.首先以旋转不变均匀LBP特征图为基础,引入滑动窗口和LBP模式对的概念,统计LBP模式图的上下文信息,构造出2DLBP特征;然后改变LBP中的半径参数,构造图像的多分辨率2DLBP特征,并利用支持向量机（SVM）的分类方法进行纹理分类;最后选取Brodatz、CUReT、UIUC、FMD四个公开纹理库分别进行纹理分类测试.理论验证表明该方法具有良好的通用性,可以与LBP的其他变型结合成为新的图像特征构造方法.同时,实验结果表明,本文提出方法具有较好的纹理图像分类能力.     

基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

针对传统局部二值模式(LBP)的特征鉴别力有限和噪声敏感性问题,该文提出一种基于金字塔分解和扇形局部均值二值模式的纹理特征提取方法。首先,将原始图像进行金字塔分解,得到对应于不同分解级别的低频和高频(差分)图像。为提取兼具鉴别力和稳健性的特征,进一步采用阈值化处理技术将高频图像转化为正、负高频图。然后,基于局部均值操作提出一种扇形局部均值二值模式(SLMBP),用于计算各级分解图像的纹理特征码。最后,对纹理特征码进行跨频带的联合编码和跨级别的直方图加权,从而获得最终的纹理特征。在公开的3个纹理数据库(Outex, Brodatz和UIUC)上进行分类实验,结果表明该文所提方法能够有效地提高纹理图像在无噪声环境和含高斯噪声环境下的分类精度。     

MLDNet: Multi-level dense network for multi-focus image fusion

In this study, we propose a new deep learning architecture named Multi-Level Dense Network (MLDNet) for multi-focus image fusion (MFIF). We introduce shallow and dense feature extraction in our feature extraction module to extract images features in a more robust way. In particular, we extracted the features from a mixture of many distributions from prior to the complex distribution through densely connected convolutional layers, then the extracted features are fused to form dense local feature maps. We added global feature fusion into the proposed architecture in order to merge the dense local feature maps of each source image into a fused image representation for the reconstruction of the final fused image. Our proposed MLDNet learns feature extraction, feature fusion and reconstruction within the same network to provide an end-to-end solution for MFIF. Experimental results demonstrate that our proposed method achieved significant performance against different state-of-the-art MFIF methods.     

Center symmetric local binary co-occurrence pattern for texture,face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.     

Local tetra patterns: a new feature descriptor for content-based image retrieval

In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the local derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP.     

基于Gabor滤波器和局部纹理特征提取的SAR目标识别算法

该文提出了一种基于Gabor滤波器和Three-Patch Local Binary Patterns(TPLBP)局部纹理特征提取的合成孔径雷达(Synthetic Aperture Rader, SAR)图像目标识别算法。首先, 利用Gabor滤波器对SAR图像在不同方向上进行滤波, 增强SAR图像中目标及其阴影的关键特征;然后, 利用TPLBP算法对Gabor滤波之后的图像进行局部纹理特征提取, 该算法克服了Local Binary Patterns(LBP)算法无法描述大范围领域纹理特征的缺陷, 并且保持了LBP旋转不变的特性, 减少了SAR图像目标方位变化对识别效果的影响;最后利用极限学习机(Extreme Learning Machine, ELM)分类器实现目标识别。该文通过MSTAR数据库中的3类SAR目标识别实验验证了该算法的有效性。      

多尺度Gabor小波变换在图像检索中的应用

在基于内容的图像检索中,纹理特征是一种重要而又难以描述的特征。为提高图像检索中纹理特征的提取效率,通过对Gabor滤波器滤波特点的研究,提出一种基于多尺度Gabor小波纹理的图像检索方法。设计了一组具有多种尺度和多个方向的滤波器组,选择并优化滤波器组的各参数,对图像进行滤波和特征提取。设计并实现了一个基于Gabor纹理...     

基于局部模式的加权估计纹理分析在人脸识别中的应用

传统的纹理分析方法仅以每个脸部区域的相对贡献来标记全局相似度,针对这种以局部表示全局而导致不能很好地进行特征提取的问题,提出了基于局部模式的加权估计纹理分析(Weighting Estimation for Texture Analysis, WETA)方法。首先使用局部二值模式（Local Binary Pattern, LBP）或者局部相位量化（Local Phase Quantization, LPQ）对图像进行纹理编码,并将其划分成各个大小相等且不重叠的局部小块;然后从相似空间中提取出最具识别力的坐标轴,利用编码与数据库的不同组合估算出权值;最后,通过权值优化给出了最佳解决方案,并采用相似性度量距离转换完成人脸的识别。在FERET和ORL两大通用人脸数据库上的实验验证了所提方法的有效性,实验结果表明,与最先进的纹理方法相比,所提方法取得了更好的识别性能。     

Texture features for classification of ultrasonic liver images

The classification of ultrasonic liver images is studied, making use of the spatial gray-level dependence matrices, the Fourier power spectrum, the gray-level difference statistics, and the Laws texture energy measures. Features of these types are used to classify three sets of ultrasonic liver images-normal liver, hepatoma, and cirrhosis (30 samples each). The Bayes classifier and the Hotelling trace criterion are employed to evaluate the performance of these features. From the viewpoint of speed and accuracy of classification, it is found that these features do not perform well enough. Hence, a new texture feature set (multiresolution fractal features) based on multiple resolution imagery and the fractional Brownian motion model is proposed to detect diffuse liver diseases quickly and accurately. Fractal dimensions estimated at various resolutions of the image are gathered to form the feature vector. Texture information contained in the proposed feature vector is discussed. A real-time implementation of the algorithm produces about 90% correct classification for the three sets of ultrasonic liver images.     

SAR图像的自动分割方法研究

由于存在相干斑噪声的影响,给SAR图像分割造成很大的困难,该文提出了一种SAR图像的自动分割方法。首先在特征提取阶段,通过计算小波能量提取纹理信息,用邻域统计量提取灰度信息,用保边缘平均灰度提取边缘信息,以确保边缘准确。然后提出一种改进的完全无监督的聚类算法进行图像分割,该算法可以自动确定分割的类型数目。由于该方法充分考虑了SAR图像的纹理、灰度和边缘信息,因而极大地提高了其最终分割性能。实验结果证明了该方法的有效性。     

An image processing system using Image Signal Multiprocessors (ISMPs)

This paper describes an image processing system using Image Signal Multiprocessors (ISMPs) adapted to gray-level image preprocessing for image analysis and image enhancement. It is composed of four ISMPs, five 1H-delay-lines, two 512×512×8-bit frame memories, a video timing controller (VTC), two 256-word ×8-bit ×8-table Look Up Tables (LUTs) and 80 nsec/sampling A/D and D/A converters. This multiprocessor system performs convolution operations such as spatial filters, contrast enhancement, and binarization for gray-level images, thinning, thickening, pattern matching etc. for binary images, and image quality improvement for moving images such as T.V. images. Otherwise, it performs feature extraction operations such as area calculations, fillet coordination, and moment calculations for objective image data. Moreover, this system is capable of applying color image processing by using a multiboard system.     

基于过零检测的虹膜特征提取算法

该文针对虹膜图像不理想、噪声影响较大情况下,虹膜识别率下降的问题,提出了一种基于过零检测的虹膜特征提取算法,利用过零检测算子与信号局部的相关性提取虹膜纹理特征,并根据所得的系数进行符号编码形成二值特征模板,最后采用相似度进行模式分类。仿真结果表明,该算法能够提取不理想虹膜图像的稳定特征,提高识别率。     

针对皮肤图像清晰度评价方法的研究

离焦模糊图像清晰度评价函像是采用数字处理技术实现自动调焦的一个关键。需要不断地提高评价函数的准确性和有效性。通过深入研究各种皮肤图像梯度分布情况以及灰度共生矩阵的纹理特征函数．本文提出基于梯度和灰度共生矩阵阈值法．建立了上述评价函数的数学模型。给出了实验结果和分析。这样可以突出清晰皮肤图像的优势。易于准确判断。