一种方向Gabor滤波纹理分割算法

结合人眼视觉特性，设计了一种方向Gabor滤波器，该滤波器顾及了纹理图像的方向特性；利用Gabor滤波器的带通技术，抑制次要纹理图像的主频率分量，增强目标纹理图像主频率分量，使滤波输出图像具有较大的类间离散度和较小的类内离散度，将纹理图像的分割转化为传统的图像分割，使图像的分割质量和算法效率都得到了提高。     

A random walk procedure for texture discrimination

We consider the problem of texture discrimination. Random walks are performed in a plain domain D bounded by an absorbing boundary ? and the absorption distribution is calculated. Measurements derived from such distributions are the features used for discrimination. Both problems of texture discrimination and edge segment detection can be solved using the same random walk approach. The border distributions and their differences with respect to a homogeneous image can classify two different images as having similar or dissimilar textures. The existence of an edge segment is concluded if the boundary distribution for a given window (subimage) differs significantly from the boundary distribution for a homogeneous (uniform grey level) window. The random walk procedure has been implemented and results of texture discrimination are shown. A comparison is made between results obtained using the random walk approach and the first-or second-order statistics, respectively. The random walk procedure is intended mainly for the texture discrimination problem, and its possible application to the edge detection problem (as shown in this paper) is just a by-product.     

一种新的图像纹理表示方法

该文基于纹理元直方图导出了纹理的新表示方法,并根据正交镜像滤波器的小波变换能量的转换和量化来定义纹理元。此种,给出了数种小波纹理特征集的实验评价,此方法具有将Brodatz纹理分类的极好性能。同时还研究了数种考虑近似旋转不变性或比例不变性的变换。最后,引入了生成纹理直方图和形成二进制纹理集的特征空间的过程,而且纹理直方图和二进制纹理集的特征空间与颜色直方图是对称的。获得纹理的这些表示旨在为度量纹理的相似性和从图像中抽取纹理区域。     

基于颜色模型和纹理特征的彩色水果图像分割方法

本文介绍了RGB和HIS两种颜色模型,采用色调值H和色差值R-G两个特征值,通过Otsu方法自动获取阈值对自然场景下彩色水果图像进行分割,并对实验结果进行了对比。然后,利用灰度共生矩阵分析了果实和背景的纹理特征,找到了区别果实和背景的纹理特征参数,对果实和背景颜色相似的图像进行分割。实验结果证明,纹理特征和颜色特征两者相辅相成,优势互补,对于包含复杂背景的青苹果图像分割效果比较好。     

面向RGBD图像的标记分水岭分割

目的 针对分水岭分割算法中存在的过分割现象及现有基于RGB图像分割方法的局限,提出了一种基于RGB图像和深度图像(RGBD)的标记分水岭分割算法。方法 本文使用物体表面几何信息来辅助进行图像分割,定义了一种深度梯度算子和一种法向量梯度算子来衡量物体表面几何信息的变化。通过生成深度梯度图像和法向量梯度图像,与彩色梯度图像进行融合,实现标记图像的提取。在此基础上,使用极小值标定技术对彩色梯度图像进行修正,然后使用分水岭算法进行图像分割。结果 在纽约大学提供的NYU2数据集上进行实验,本文算法有效抑制了过分割现象,将分割区域从上千个降至数十个,且获得了与人工标定的分割结果更接近的分割效果,分割的准确率也比只使用彩色图像进行分割提高了10%以上。结论 本文算法普遍适用于RGBD图像的分割问题,该算法加入了物体表面几何信息的使用,提高了分割的准确率,且对颜色纹理相似的区域获得了较好的分割结果。     

一种基于深度学习的多尺度深度网络的场景标注算法

针对场景标注中如何产生良好的内部视觉信息表达和有效利用上下文语义信息两个至关重要的问题,提出一种基于深度学习的多尺度深度网络监督模型。与传统多尺度方法不同,模型主要由两个深度卷积网络组成:首先网络考虑全局信息,提取大尺度图像低层特征;其次网络利用图像局部信息,结合低层特征获取一组稠密的、完备的图像特征,有效地捕获图像像素的纹理特征、颜色特征和上下文信息。对比许多经典方法,该算法不依赖图像分割技术和人工制作特征,在Stanford Background Dataset取得了很好的效果。     

Image saliency detection using Gabor texture cues

Image saliency analysis plays an important role in various applications such as object detection, image compression, and image retrieval. Traditional methods for saliency detection ignore texture cues. In this paper, we propose a novel method that combines color and texture cues to robustly detect image saliency. Superpixel segmentation and the mean-shift algorithm are adopted to segment an original image into small regions. Then, based on the responses of a Gabor filter, color and texture features are extracted to produce color and texture sub-saliency maps. Finally, the color and texture sub-saliency maps are combined in a nonlinear manner to obtain the final saliency map for detecting salient objects in the image. Experimental results show that the proposed method outperforms other state-of-the-art algorithms for images with complex textures.     

一种融合纹理信息与图论的图像分割方法

针对未成熟苹果图像果实同背景色彩差距较小导致难以分割效果的问题,提出一种融合纹理信息与图论的图像分割方法。该方法先将图像划分为图像块并计算每个图像块的纹理信息,以图像块为节点利用纹理信息构造权函数得到无向带权图,用基于区域合并的图论图像方法进行分割并二次吸收合并。实验结果表明,在对象和背景色彩接近时,该方法比传统分割法效果好,且改善了分割过细的情况。     

A methodology for extracting objective color from images

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.     

骨髓细胞显微图像的分形特征分析

为了更好地完成骨髓细胞图像的分类识别,在对临床血液学及细胞学图谱中的细胞图像进行切分的基础上,提出了一种基于分形维数和图像重心的算法,用来提取每一细胞图像的分维特征.由于图像的纹理不仅表现在图像结构上具有某种统计意义的相似性,还反映在彩色分布方面,因此,可从骨髓细胞的真彩色图像中提取一个新的彩色识别分量图像C,并与HIS彩色空间的饱和度分量图像S一起进行分维特征提取.实验数据表明,由于不同类型骨髓细胞图像的分形参数具有不同程度的差异,因此分形参数对于某些类别的骨髓细胞具有较好的区分能力.     

结合分水岭的纹理梯度各向异性图像分割

目的 目前,许多图像分割算法对含有丰富纹理信息的图像的分割效果并不理想,尤其是在不同纹理的边缘信息的保持方面。为了解决这一问题,提出一种基于连续纹理梯度信息的各向异性图像分割算法。方法 在分水岭算法的基础上,引入纹理梯度各向异性算法,能够在避免纹理信息影响分割效果的前提下,最大限度地保证纹理边缘信息的完整。针对纹理特征数据敏感的特性,本文将离散的图像高度信息映射到连续的纹理梯度空间,能够有效减少由细小差异造成的过分割现象。结果 本文方法在BSD500 Dataset和Stanford Background Dataset中选择了大量的纹理信息丰富的图片与最新的分割算法进行了实验与对比。本文方法在分割效果（降低过分割现象）、保持边缘信息和分割准确率等方面均获得明显改进,并在图像分割的平均准确率方面与最新算法进行比较发现,本文算法的平均分割准确率达到90.9%,明显超过了其他最新算法,验证了本文方法的有效性。结论 本文提出的基于分水岭的纹理梯度各向异性算法对纹理图像的分割具有保边和准确的特点,采用连续梯度空间的方法能够有效地减少传统分水岭算法的过分割现象。本文方法主要适用于纹理信息丰富（自然纹理和人工纹理）的图片。     

Markov random field models for unsupervised segmentation oftextured color images

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation     

Optimal spatial filter selection for illumination-invariant colortexture discrimination

Color textures contain a large amount of spectral and spatial structure that can be exploited for recognition. Recent work has demonstrated that spatial filters offer a convenient means of extracting illumination-invariant spatial information from a color image. In this paper, we address the problem of deriving optimal filters for illumination-invariant color texture discrimination. Color textures are represented by a set of illumination-invariant features that characterize the color distribution of a filtered image region. Similar features have been used in previous studies. Given a pair of color textures, we derive a spatial filter that maximizes the distance between these textures in feature space. We provide a method for using the pairwise result to obtain a filter that maximizes discriminability among multiple classes. A set of experiments on a database of deterministic and random color textures obtained under different illumination conditions demonstrates the improved discriminatory power achieved by using an optimized filter.     

Real-time hand posture recognition using range data

A hand posture recognition system using 3D data is described. The system relies on a novel 3D sensor that generates a dense range image of the scene. The main advantage of the proposed system, compared to other gesture recognition techniques, is the capability for robust unconstrained recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly utilizing 3D hand geometry. Moreover, the proposed approach does not rely on color information, and guarantees robust segmentation of the hand under varying illumination conditions, and scene content. Several novel 3D image analysis algorithms are presented, covering the complete processing chain: 3D image acquisition, arm segmentation, hand–forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is extensively evaluated.     

A Generative Probabilistic Oriented Wavelet Model for Texture Segmentation

This Letter addresses image segmentation via a generative model approach. A Bayesian network (BNT) in the space of dyadic wavelet transform coefficients is introduced to model texture images. The model is similar to a Hidden Markov model (HMM), but with non-stationary transitive conditional probability distributions. It is composed of discrete hidden variables and observable Gaussian outputs for wavelet coefficients. In particular, the Gabor wavelet transform is considered. The introduced model is compared with the simplest joint Gaussian probabilistic model for Gabor wavelet coefficients for several textures from the Brodatz album [1]. The comparison is based on cross-validation and includes probabilistic model ensembles instead of single models. In addition, the robustness of the models to cope with additive Gaussian noise is investigated. We further study the feasibility of the introduced generative model for image segmentation in the novelty detection framework [2]. Two examples are considered: (i) sea surface pollution detection from intensity images and (ii) image segmentation of the still images with varying illumination across the scene.     

Mesh-guided optimized retexturing for image and video

This paper presents an approach of replacing textures of specified regions in the input image and video using stretch-based mesh optimization.. The retexturing results have the similar distortion and shading effects conforming to the underlying geometry and lighting conditions. For replacing textures in single image,two important steps are developed: the stretch-based mesh parametrization incorporating the recovered normal information is deduced to imitate perspective distortion of the region of interest; the Poisson-based refinement process is exploited to account for texture distortion at fine scale.The luminance of the input image is preserved through color transfer in YCbCr color space. Our approach is independent of the replaced textures. Once the input image is processed, any new texture can be applied to efficiently generate the retexturing results. For video retexturing, we propose key-frame-based texture replacement extended and generalized from the image retexturing. Our approach repeatedly propagates the replacement result of key frame to the rest of the frames. We develop the local motion optimization scheme to deal with the inaccuracies and errors of robust optical flow when tracking moving objects. Visibility shifting and texture drifting are effectively alleviated using graphcut segmentation algorithm and the global optimization to smooth trajectories of the tracked points over temporal domain. Our experimental results showed that the proposed approach can generate visually pleasing results for both image and video.     

结合目标色彩特征的基于注意力的图像分割

提出一种基于注意力的图像分割算法,在视觉场景选择机制基础上结合目标色彩特征的任务驱动机制,形成了自下而上和自上而下的注意力集成分割机理。该算法在图像的多尺度空间中,把视觉场景的亮度、颜色和方向特征与任务目标色彩特征同时进行提取,生成场景和目标相结合的显著图,然后在基于视觉注意力图像空间中对“场景-目标” 显著图进行归一化的跨尺度融合,最后通过双线性插值和显著图连通区域二值化分割出图像目标注意力焦点。应用该算法对自然场景与室内场景图像进行实验,结果表明该方法在各种环境中尤其是干扰物体较显著的情形下都能成功地分割提取出目标物体。