基于结合空间信息的FCM聚类的分水岭图像分割

提出了一种分水岭变换和结合空间信息的FCM聚类相结合的图像分割方法。方法采用基于图论的结合区域特征信息和空间信息的距离度量,以分水岭变换得到的图像分割小区域为节点构建一个连通加权图,通过计算图上不同节点之间的最短路径来度量不同区域之间的相似程度,从而实现过分割小区域的合并。该方法综合考虑了区域的特征之间的差异和空间位置的差异,与传统的FCM聚类方法在特征空间进行聚类相比,具有较强的噪声抑制能力。图像分割的实验结果证明了该算法的可行性和有效性。     

Fast implementation of scale-space by interpolatory subdivisionscheme

While the scale-space approach has been widely used in computer vision, there has been a great interest in fast implementation of scale-space filtering. We introduce an interpolatory subdivision scheme (ISS) for this purpose. In order to extract the geometric features in a scale-space representation, discrete derivative approximations are usually needed. Hence, a general procedure is also introduced to derive exact formulae for numerical differentiation with respect to this ISS. Then, from ISS, an algorithm is derived for fast approximation of scale-space filtering. Moreover, the relationship between the ISS and the Whittaker-Shannon sampling theorem and the commonly used spline technique is discussed. As an example of the application of ISS technique, we present some examples on fast implementation of λτ-spaces as introduced by Gokmen and Jain (1997), which encompasses various famous edge detection filters. It is shown that the ISS technique demonstrates high performance in fast implementation of the scale-space filtering and feature extraction     

Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levellings

Edge preserving smoothing and image simplification is of fundamental importance in a variety of remote sensing applications during feature extraction and object detection procedures. The construction of a pre‐processing filtering tool for edge detection and segmentation tasks is still an open matter. Towards this end, this paper brings together two advanced nonlinear scale space representations, anisotropic diffusion filtering and morphological levellings, forming a processing scheme by their combination. The proposed scheme was applied to edge detection and watershed segmentation tasks. The experimental results showed that the developed scheme generated an effective pre‐processing tool for automatic olive tree detection and solving watershed over‐segmentation problems.     

基于Gabor滤波器和聚类的图像配准算法

提出一种基于多尺度、多方向Gabor滤波器提取图像局部不变特征并用AP聚类进行约束的配准算法。该方法首先利用Gabor尺度空间核函数对图像进行尺度空间分解,在每一层尺度图像的不同方向上提取Harris角点,在以Harris角点为中心的固定大小的搜索窗内搜索三维尺度空间的极值点作为局部特征点的位置和特征尺度;在特征子区域内用梯度描述特征点;将得到的两幅图像的特征点AP聚类分析,实现m:n的粗匹配,最终通过各类之间的欧式距离实现对应点的匹配,通过AP聚类可有效排除多相似内容的图像之间的误匹配。实验结果表明,该算法能够提取稳健的精确特征点,并且可以有效去除多相似内容图像带来的匹配误差,实现图像的配准。     

基于中心定位算法的细胞双光子显微图像分割

细胞双光子显微图像中存在边界模糊不清、噪声严重以及背景复杂等问题,使用现有方法提取边缘的结果不够理想。为此,提出了一种有效定位细胞核、提取边缘的新方法。采取由粗到精的分割策略,逐步提取出细胞核的边缘:首先,用C-均值聚类算法对图像进行分类,大致分出细胞核、细胞质和细胞间质三部分;然后,对分类结果的Canny边缘图进行圆形度计算,准确定位细胞核;最后,采用改进的水平集方法提取出细胞核的边缘。实验结果表明:对于背景复杂、干扰较多的细胞双光子显微图像,该方法可以精确地定位细胞核,所提取的细胞核边缘也较为精确。     

SIFER: Scale-Invariant Feature Detector with Error Resilience

We present a new method to extract scale-invariant features from an image by using a Cosine Modulated Gaussian (CM-Gaussian) filter. Its balanced scale-space atom with minimal spread in scale and space leads to an outstanding scale-invariant feature detection quality, albeit at reduced planar rotational invariance. Both sharp and distributed features like corners and blobs are reliably detected, irrespective of various image artifacts and camera parameter variations, except for planar rotation. The CM-Gaussian filters are approximated with the sum of exponentials as a single, fixed-length filter and equal approximation error over all scales, providing constant-time, low-cost image filtering implementations. The approximation error of the corresponding digital signal processing is below the noise threshold. It is scalable with the filter order, providing many quality-complexity trade-off working points. We validate the efficiency of the proposed feature detection algorithm on image registration applications over a wide range of testbench conditions.     

An experimental comparison of clustering methods for content-based indexing of large image databases

In recent years, the expansion of acquisition devices such as digital cameras, the development of storage and transmission techniques of multimedia documents and the development of tablet computers facilitate the development of many large image databases as well as the interactions with the users. This increases the need for efficient and robust methods for finding information in these huge masses of data, including feature extraction methods and feature space structuring methods. The feature extraction methods aim to extract, for each image, one or more visual signatures representing the content of this image. The feature space structuring methods organize indexed images in order to facilitate, accelerate and improve the results of further retrieval. Clustering is one kind of feature space structuring methods. There are different types of clustering such as hierarchical clustering, density-based clustering, grid-based clustering, etc. In an interactive context where the user may modify the automatic clustering results, incrementality and hierarchical structuring are properties growing in interest for the clustering algorithms. In this article, we propose an experimental comparison of different clustering methods for structuring large image databases, using a rigorous experimental protocol. We use different image databases of increasing sizes (Wang, PascalVoc2006, Caltech101, Corel30k) to study the scalability of the different approaches.     

基于自监督学习的病理图像层次分割

在苏木精-伊红(HE)染色病理图像中，细胞染色分布的不均匀和各类组织形态的多样性给病理图像的自动分割带来极大挑战。为解决该问题，提出了一种基于自监督学习的病理图像三步层次分割方法，对病理图像中各类组织进行由粗略到精细的全自动逐层分割。首先，根据互信息的计算结果在RGB色彩空间中进行特征选择；其次，采用K-means聚类将图像初步分割为各类组织结构的色彩稳定区域与模糊区域；然后，以色彩稳定区域为训练集采用朴素贝叶斯分类对模糊区域进行进一步分割，得到完整的细胞核、细胞质和胞外间隙这三类组织结构；最后，对细胞核部分进行结合形状和色彩强度的混合分水岭分割得到细胞核间的精确边界，进而量化计算细胞核个数、核占比、核质比等指标。对脑膜瘤HE染色病理图像的分割实验结果表明，所提方法对于染色和细胞形态差异保持较高的鲁棒性，各类组织区域分割误差在5%以内，在细胞核分割精度的对比实验中平均正确率在96%以上，满足临床自动图像分析的要求，其量化结果可以为定量病理分析提供依据。     

局部化数字水印算法

数字水印是一种嵌入到图象,视频或音频数据中的不可见标志,可以用于多媒体数字的版权保护,认证和标注等,为了提高在频率域嵌入水印抵抗裁剪攻击的能力,提出一种局部化的图象数字水印算法,该算法利用图象中相对稳定的特征点标示水印嵌入的位置,并在与每个特征点对应的局部区域中独立地嵌入水印,这样,当只有部分图象时,仍能通过这些特下点来定位并提取水印,此算法中,水印的嵌入是在局部图象的小波域中进行的,并采用对小波系数进行特殊量化的方法来隐藏水印比特,而水印的提取不需要原始图象参与,实验结果证明,算法对裁剪有很强的抵抗能力,同时对压缩,滤波,噪声,StirMark攻击等也有较好的鲁棒性。     

结合聚类与改进分水岭算法的彩色图像分割

针对传统分水岭算法产生严重的过分割问题,提出了一种聚类和改进分水岭算法结合的彩色图像分割算法.该算法首先利用聚类算法在HSV颜色空间将特征相似的像素归为一类,然后对分水岭算法产生的分割区域进行种子区域生长,并利用区域合并将剩余的小区域进行合并,从而完成了对彩色图像的分割.实验证明该算法减少了分水岭算法的过分割现象,提高...     

A New Digital Image Watermarking Algorithm Resilient to Desynchronization Attacks

Synchronization is crucial to design a robust image watermarking scheme. In this paper, a novel feature-based image watermarking scheme against desynchronization attacks is proposed. The robust feature points, which can survive various signal-processing and affine transformation, are extracted by using the Harris-Laplace detector. A local characteristic region (LCR) construction method based on the scale-space representation of an image is considered for watermarking. At each LCR, the digital watermark is repeatedly embedded by modulating the magnitudes of discrete Fourier transform coefficients. In watermark detection, the digital watermark can be recovered by maximum membership criterion. Simulation results show that the proposed scheme is invisible and robust against common signal processing, such as median filtering, sharpening, noise adding, JPEG compression, etc., and desynchronization attacks, such as rotation, scaling, translation, row or column removal, cropping, and random bend attack, etc.     

A feature-based robust digital image watermarking against desynchronization attacks

In this paper,a new content-based image watermarking scheme is proposed.The Harris-Laplace detector is adopted to extract feature points,which can survive a variety of attacks.The local characteristic regions (LCRs) are adaptively constructed based on scale-space theory.Then,the LCRs are mapped to geometrically invariant space by using image normalization technique.Finally, several copies of the digital watermark are embedded into the nonoverlapped LCRs by quantizing the magnitude vectors of discrete Fourier transform (DFT) coefficients.By binding a watermark with LCR,resilience against desynchronization attacks can be readily obtained.Simulation results show that the proposed scheme is invisible and robust against various attacks which includes common signals processing and desynchronization attacks.     

基于语义的图像物体提取的新方法

在图像语义研究中,提取图像中的语义物体或区域是重要的.本文首先对图像预处理,通过颜色空间的转换,在空间对图像进行K-均值分类,提取出具有语义性质的物体和区域.实验结果表明该方法是可行的,而且很有效的.     

分水岭分割的方向迭代后处理算法

为了提取彩色图像中线目标,该文提出了一种新的彩色图像分割算法,首先对图像进行分水岭分割得到初始过分割图像,并通过模糊聚类方法得到区域分类概率,然后根据图像的边缘信息和空间特性,得到区域的线方向邻接区域,最后通过迭代的方法,利用线方向邻接区域信息更新区域的分类概率。实验结果显示了很好的提取效果。     

Histogram-Based Decision Support System for Extraction and Classification of Leukemia in Blood Smear Images

An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach. Through entropy-adaptive mask generation, WBCs accurately detect the circularity region for identification of the nucleus. The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations. Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions. The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images. The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier. The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes, monocytes, neutrophils, eosinophils, and abnormalities in the WBCs. The proposed HTsC identifies the abnormal activity in the WBC, considering the color and shape features. It exhibits a higher classification accuracy value of 99.6% when combined with the other classifiers. The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%, which is approximately 3%–12% higher than the conventional technique.     

A feature-based robust digital image watermarking against geometric attacks

Based on scale space theory and an image normalization technique, a new feature-based image watermarking scheme robust to general geometric attacks is proposed in this paper. First, the Harris–Laplace detector is utilized to extract steady feature points from the host image; then, the local feature regions (LFR) are ascertained adaptively according to the characteristic scale theory, and they are normalized by an image normalization technique; finally, according to the predistortion compensation theory, several copies of the digital watermark are embedded into the nonoverlapped normalized LFR by comparing the DFT mid-frequency magnitudes. Experimental results show that the proposed scheme is not only invisible and robust against common signals processing methods such as median filtering, sharpening, noise adding, and JPEG compression etc., but also robust against the general geometric attacks such as rotation, translation, scaling, row or column removal, shearing, local geometric distortion and combination attacks etc.     

视频序列中运动目标自动提取的研究

本文提出了一种从彩色视频序列中,有效分割运动目标的算法。首先将当前帧图像和背景参考图像在YUV彩色空间中进行距离差分得到距离差分图像,然后根据差分图像直方图的单峰聚集特性,提出了基于直方图的自适应聚类分割算法;对分割后的二值图像采用图像形态学方法去除无用的噪声斑点,并根据得到运动模板,提出了背景参考图像的更新策略。实验结果表明这是一种简单、有效的运动目标提取方法。     

基于SIFT的椭圆区域鲁棒数字水印方案

如何有效抵抗去同步攻击是数字图像水印研究领域的热点问题之一。利用图像仿射协变特征,提出一种可有效抵抗去同步攻击的鲁棒水印算法。对目前流行的利用多尺度Harris和SIFT描述算子来匹配图像的方法,后者有较好的匹配效果,对恢复同步水印更加稳定,并且能较好抵抗去同步攻击。该算法利用性能稳定的SIFT算子提取图像特征点,并通过基于最小生成树聚类算法的选择策略获得一组稳定且彼此独立的椭圆仿射协变特征区域,基于特征区域,利用椭圆归一化得到具有缩放和旋转不变性的圆形区域。将圆形区域进行非下采样轮廓变换（NSCT）,其中非下采样轮廓变换不仅克服了小波（Wavelet）变换的非奇异性最优基缺点,而且提供了优于轮廓（Contourlet）变换的平移不变性。将水印嵌入变换后的低频子带中。该算法实现盲提取,仿真实验结果表明,提出的算法是有效的且对常规图像处理、几何攻击以及组合攻击均具有较好的鲁棒性。     

基于LFRs的大容量抗几何攻击水印算法*

如何有效抵抗几何攻击是目前水印技术研究的难点之一,且已有抗几何攻击水印算法的嵌入容量有限。提出了一种利用尺度不变特征变换（SIFT）特征点确定的局部特征区域（LFRs）嵌入水印的大容量抗几何攻击的水印算法。算法首先从图像的非抽样的Contourlet域（NSCT）低频分量中提取出SIFT中等尺度的稳定特征点,并通过最小生成树聚类算法的选择策略获得一组分布均匀且彼此独立的圆形特征区域;然后对每个圆形区域进行等角度的扇形分割和等面积的同心圆环分割,划分成等面积的子块;最后采用奇偶量化将经过混沌加密的水印嵌入到