基于协同神经网络的仿射不变性检索相似性度量方法

根据协同神经网络 ,提出了一种新的控制参数———序矢量 ,并着重研究了其在图像检索方面的相关性质 .在此基础上 ,针对商标图像检索 ,提出了一种高效的基于元素的仿射不变性度量方法 .实验表明 ,该检索算法抗噪、抗缺损能力强 ,同时对于平移、旋转、缩放具有不变性 .     

An efficient signature representation for retrieval of spatially similar images

Multimedia applications involving image retrieval demand fast and efficient response. Efficiency of search and retrieval of information in a database system is index dependent. Generally, a two-level indexing scheme in an image database can help to reduce the search space against a given query image. In such type of indexing scheme, the first level is required to significantly reduce the search space for second stage of comparisons and must be computationally efficient. It is also required to guarantee that no false negatives may result. The second level of indexing involves more detailed analysis and comparison of potentially relevant images. In this paper, we present an efficient signature representation scheme for first level of a two-level image indexing scheme that is based on hierarchical decomposition of image space into spatial arrangement of image features. Experimental results demonstrate that our signature representation scheme results in fewer number of matching signatures in the first level and significantly improves the overall computational time. As this scheme relies on corner points as the salient feature points in an image to describe its contents, we also compare results using several different contemporary corner detection methods. Further, we formally prove that the proposed signature representation scheme not only results in fewer number of signatures but also does not result in any false negative.     

基于感兴趣区域多特征加权融合的图像检索算法

基于内容的图像检索有着广阔的应用前景,但存在检索性能不高的缺点.综合兴趣点和多特征融合的优点,提出一种基于感兴趣区域多特征加权融合的图像检索算法.采用Harris算法提取图像的兴趣点,确定感兴趣区域;再采用累积灰度直方图、共生矩阵和形状不变矩分别提取感兴趣区域的颜色、纹理和形状特征;经归一化后,最后采用距离函数级融合来度量图像的相似度,以检索图像.实验表明,算法有效地提高了图像的检索性能.     

Ultrasound image segmentation with shape priors: application to automatic cattle rib-eye area estimation.

Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts.     

基于电子稳像技术的CCD成像空间校正方法研究

针对彩色线阵CCD应用时,出现的成像三色错位问题进行了研究,提出了基于电子稳像技术的CCD成像空间校正方法。与传统方法相比,该方法引入了电子稳像技术的思想,巧妙运用帧间运动的处理手段解决了帧内运动的问题。首先对线阵CCD得到的图像进行彩色图像分离,再通过特征点提取,特征点匹配,错位参数估计,图像补偿,彩色图像合成等过程,最终获得校正后的彩色图像。仿真实验表明,该方法能有效解决三色错位问题,改善图像质量。     

综合颜色和形状特征的图像检索

提出了一种组合颜色和形状特征的图像检索方法，将彩色图像转变成灰度图象，计算查询图像和数据库图像的直方图距离，通过图像分割提取图像的形状特征，利用两特征的加权距离计算图像之间的相似度，而后进行图像检索。通过实验表明该组合方法优于单纯特征的图像检索。     

A spine X-ray image retrieval system using partial shape matching

In recent years, there has been a rapid increase in the size and number of medical image collections. Thus, the development of appropriate methods for medical information retrieval is especially important. In a large collection of spine X-ray images, maintained by the National Library of Medicine, vertebral boundary shape has been determined to be relevant to pathology of interest. This paper presents an innovative partial shape matching (PSM) technique using dynamic programming (DP) for the retrieval of spine X-ray images. The improved version of this technique called corner-guided DP is introduced. It uses nine landmark boundary points for DP search and improves matching speed by approximately 10 times compared to traditional DP. The retrieval accuracy and processing speed of the retrieval system based on the new corner-guided PSM method are evaluated and included in this paper.     

基于统计模型的三线阵CCD月球影像快速匹配算法

为实现快速鲁棒的同名像点匹配,基于概率统计特征,提出一种针对CCD影像进行快速匹配的方法。首先应用统计方法建立月表整体形状模型和特征形状模型并构建轮廓特征;然后提取轮廓上的关键点作为特征点,随机选择若干特征点对得到候选变换;最后对其变换参数进行检验和求精。实验结果表明,本文算法比传统匹配算法的速度有了较大的提高,能够在...     

结构光场光束直线跟踪的两步标定方法

为了实现结构光系统的三维重建,针对传统标定方 法结构复杂、标定过程繁琐等问题,提出一种基于光束直线跟踪 的两步标定方法。本文方法首先在前后两个平面上分别对投射侧模式上的每个检测点进行 标定,建立投射侧光线方程;然后在 前后两个平面上分别对平面上的每个目标点建立接收侧CCD的光线方程。在三维测量时,光 场中的一个点分别映射为投射 侧和接收侧前后平面上的两个点,连接前后平面上的两个点各得到一条空间直线,通过求解 两条空间直线的交点即可求得三 维空间点的坐标。分析了系统结构在标定时可能产生的误差,系统误差主要来自角度偏移和 水平偏移,并提出了误差解决方 案。实验结果表明,与传统的基于针孔模型的标定方法相比,本文方法无须考虑系统的成像 模型及其参数求解,简洁、高效。标定产生的景深相对误差小于0.2% ,能够改善结构光系统的几何标定精度。     

基于兴趣点局部分布特征的图像检索方法

提出了一种基于兴趣点颜色和空间分布特征的图像检索方法。该方法把图像内容看作为由若干兴趣点组成的集合,首先利用小波系数的空间方向树特性来检测兴趣点,然后利用基于兴趣点的环形颜色直方图和空间离散度来描述图像的特征,最后用加权特征距离来估计图像内容的相似度。同时,通过利用环形颜色直方图和空间离散度作为图像特征保证了该算法能够对图像的尺度变化、旋转变化和平移变化具有很好的抑制能力。在含有1000幅图像的数据库上所做的一系列实验表明,该算法与其它基于兴趣点的方法相比,能够更准确和高效地查找出用户所需的图像,明显地提高了检索精度。     

Indexing and retrieval of images by spatial constraints

Many multimedia applications require retrieval of spatially similar images against a given query image. Existing work on image retrieval and indexing either requires extensive low-level computations or elaborate human interaction. In this paper, we introduce a new symbolic image representation technique to eliminate repetitive tasks of image understanding and object processing. Our symbolic image representation scheme is based on the concept of hierarchical decomposition of image space into spatial arrangements of features while preserving the spatial relationships among the image objects. Quadtrees are used to manage the decomposition hierarchy and play an important role in defining the similarity measure. This scheme is incremental in nature, can be adopted to accommodate varying levels of details in a wide range of application domains, and provides geometric variance independence. While ensuring that there are no false negatives, our approach also discriminates against non-matching entities by eliminating them as soon as possible, during the coarser matching phases. A hierarchical indexing scheme based on the concept of image signatures and efficient quadtree matching has been devised. Each level of the hierarchy tends to reduce the search space, allowing more involved comparisons only for potentially matching candidate database images. For a given query image, a facility is provided to rank-order the retrieved spatially similar images from the image database for subsequent browsing and selection by the user.     

基于形状的小波变换系数广义高斯分布图像检索算法

本文提出一种基于小波变换系数的广义高斯密度分布形状检索算法.该算法充分利用小波变换变换系数的统计特性,形成描述形状特征的特征矢量.实验证明,算法提出的描述方式能够有效体现物体形状特征.同时,算法对于形状的尺度变化,平移变化以及旋转变化有着很好的抑制能力.该算法简单,有效,是一种有效的基于形状的图像检索算法.     

采用热核特征的SAR图像目标识别

为了解决SAR图像受相干斑噪声干扰和震后发生形变而识别率偏低的问题,提出了一种新的仿射、形变不变特征-热核特征,并将该特征用于SAR图像目标识别.首先采用推广的核模糊C-均值方法分割SAR图像,提取SAR图像目标形状;接着对目标形状进行Delaunay三角剖分,采用余切权重法对Laplace-Beltrami Operator离散化,通过离散化Laplace-Beltrami Operator特征值、特征向量求每一点热核特征;然后采用谱距离公式对点点间热核距离计算,转化为距离分布表示目标形状的热核特征;最后采用L1相似性准则对图像进行相似性度量,得到识别结果.实验表明:与经典的Hu不变矩方法相比,对于仿射变换和发生形变的SAR图像,该方法都具有更高的识别率.因此,基于热核特征的SAR图像识别方法是一种更加有效的识别方法.     

Time-invariant orthonormal wavelet representations

A simple construction of an orthonormal basis starting with a so-called mother wavelet, together with an efficient implementation gained the wavelet decomposition easy acceptance and generated a great research interest in its applications. An orthonormal basis may not, however, always be a suitable representation of a signal, particularly when time (or space) invariance is a required property. The conventional way around this problem is to use a redundant decomposition. We address the time-invariance problem for orthonormal wavelet transforms and propose an extension to wavelet packet decompositions. We show that it,is possible to achieve time invariance and preserve the orthonormality. We subsequently propose an efficient approach to obtain such a decomposition. We demonstrate the importance of our method by considering some application examples in signal reconstruction and time delay estimation     

Deep multi query image retrieval

There exist few studies investigating the multi-query image retrieval problem. Existing methods are not based on hash codes. As a result, they are not efficient and fast. In this study, we develop an efficient and fast multi-query image retrieval method when the queries are related to more than one semantic. Image hash codes are generated by a deep hashing method. Consequently, the method requires lower storage space, and it is faster compared to the existing methods. The retrieval is based on the Pareto front method. Reranking performed on the retrieved images by using non-binary deep-convolutional features increase retrieval accuracy considerably. Unlike previous studies, the method supports an arbitrary number of queries. It outperforms similar multi-query image retrieval studies in terms of retrieval time and retrieval accuracy.     

CCD noise removal in digital images.

In this work, we propose a denoising scheme to restore images degraded by CCD noise. The CCD noise model, measured in the space of incident light values (light space), is a combination of signal-independent and signal-dependent noise terms. This model becomes more complex in image brightness space (normal camera output) due to the nonlinearity of the camera response function that transforms incoming data from light space to image space. We develop two adaptive restoration techniques, both accounting for this nonlinearity. One operates in light space, where the relationship between the incident light and light space values is linear, while the second method uses the transformed noise model to operate in image space. Both techniques apply multiple adaptive filters and merge their outputs to give the final restored image. Experimental results suggest that light space denoising is more efficient, since it enables the design of a simpler filter implementation. Results are given for real images with synthetic noise added, and for images with real noise.     

Query-by-shape in meteorological image archives using the pointdiffusion technique

The authors work on meteorological satellite image archives and provide a novel and useful query-by-shape tool. To this aim, they first present the point diffusion technique (PDT), a fast and efficient method for shape similarity evaluation. Thanks to its very structure, this approach is suitable to handle objects whose shape is not well defined and can be represented by a set of sparse points. PDT is thus suitable for application to similarity-based retrieval from remotely sensed image archives, where shapes are hardly defined but are still among the major features of interest. Moreover, they prove here that PDT is almost as effective as more standard procedures for shape-based database queries, although significantly faster. In other words, it manages to combine retrieval speed and precision, the features of greatest importance for a first remote sensing data prescreening in many applications. Archives of meteorological satellite images are typical examples of very large-sized, remote sensing-based databases with a special attention for shape features. Each meteorological satellite produces terabytes of data every day, a large part of which is not immediately analyzed and ends being stored in archives. The application of PDT to such a database is presented and discussed, and a comparison with a standard method developed for meteorological shape analysis is provided     

Image retrieval using both color and texture features

In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.     

A shape-based image retrieval method using salient edges

Content-based image retrieval is emerging as an important research area with applications in digital libraries and multimedia databases. In this paper, we present a novel five-stage image retrieval method based on salient edges. In the first stage, the Canny operator is performed to detect edge points. Then, the Water-Filling algorithm is employed to extract edge curves. In the third stage, salient edges are selected and the shape features in terms of the salient edges are yielded. In the fourth stage, a similarity measure, namely the integrated salient edge matching, that integrates properties of all the salient edges, is introduced, and used to compare the similarity of the query image with the images in the database. Finally, the best matches are returned in similarity order. The presented approach is easy to implement and can be efficiently applied to retrieve images with clear edges. Preliminary experimental results on a database containing 6500 images are very promising.