边界重叠图象的一种快速拼接算法

针对边界部分有重叠的图象,提出了一种基于网络的快速对准算法,并通过平滑因子对图象实现了无缝拼接。     

Restoration of binary images using stochastic relaxation with annealing

This paper investigates the application of variations of Stochastic Relaxation with Annealing (SRA) as proposed by Geman and Geman [1] to the Bayesian restoration of binary images corrupted by white noise. After a general review we present some specific prior models and show examples of their application. It appears that a proper selection of the prior model is critical for the success of the method. We obtained better results on artificial images which fitted the model closely than on real images for which there was no precise model.     

HD/SHD still image database system and image distribution in broadband network applications

This paper focuses on a practical design for an efficient scalable image database and retrieval system over broadband networks. It describes a concrete solution for the implementation of HD/SHD (high-definition/super-high-definition) still image retrieval services which can be used in different applications. The structure of the complete system, consisting of a directory server, an image server, and MMI (man-machine interface) devices, has been presented, detailing each element and their corresponding functions. The desired HD/SHD image is displayed on the HD-PDP (plasma display panel) with the aid of image matching. The proposed system generates image index automatically, eliminating special skills in preparing index images and crucially reducing the processing time (from 35 min to 110 s), and does not use keywords. It has been also shown that these indices can be used for quite accurate image retrieval, i.e., the system provides high precision rates (currently up to 98%) to the user, eliminating troubles encountered in the image retrieval operations due to limitation on the user’s age, culture, knowledge, and languages.The broadband IP networks currently have a number of issues from the viewpoint of practical system operations, and so the requirements and issues needed for the networks are discussed from the viewpoint of in-service performance, differentiation among different types of services, secure connections, and so on, focusing on handling of HD/SHD still images.     

基于异或-置乱框架的图像可逆数据隐藏仿真

针对因图像加密、解密过程中图像还原度较差,数据嵌入率较低,导致图像传输过程中图像质量差,安全性低的问题,提出一种基于异或-置乱框架的图像可逆数据隐藏方法。分析相邻像素间位异或-置乱法,使用位异或和像素位置置乱方式对初始图像做加密处理,得到初始加密图像,根据隐藏密钥特征选取一部分像素,利用替换方法把选取像素嵌入隐藏数据中,使用加密密钥将隐藏数据提取出来,最后通过邻域预测方式对加密图像做解密处理,并根据像素波动性算出各邻域模块最高位是否发生改变,还原初始图像。仿真结果表明,采用所提方法得到的图像还原性较好,数据嵌入率较高,可以有效保护图像传输中的安全,并保证图像质量,具有一定的实践价值。     

N维Hilbert编码的计算

针对高维Hilbert曲线的复杂性问题,给出了一种N维Hilbert码计算方法.其基本思想是面向一个称为基因的静态演化规则表,根据基因信息进行相应的坐标变换,编解码始终依照Hilbert单元的映射特征进行映射转换;在逐层编解码过程中,把不断变化的映射形态转成固定的Hilbert单元映射方式,同时采用二进制位操作进行计算,从而实现高效的N维Hilbert映射转换.     

三维超光谱图象数据变换编码压缩系统中量化器的设计

量化器在图象压缩系统中占有非常重要的地位,量化器结构与参数直接影响到恢复图象的质量和压缩比,在研制的三维超光谱图象压缩系统中,针对三维超光谱图象自身和应用的特点,设计出了一维反正切型映射量化器和二维具有图象边缘特征保持特性的量化器,实验结果表明,量化器的映射量化误差较小,并充分保持了在超光谱图象应用中较为重要的图象边缘特征。     

Improved phase unwrapping algorithm based on NVIDIA CUDA

The paper presents an improved version of the region-growing algorithm for the interferometric phase unwrapping of the radar images. A method for packing/unpacking indices of points in a photograph that is based on bitwise operations is developed. A fast algorithm for generating a growth ring of target pixels and an algorithm for merging regions are created. Software implementation of the algorithm can be run on platforms using graphics devices that support the NVIDIA CUDA technology.     

A new image prediction model based on spatio-temporal techniques

This paper addresses an image prediction problem focused on images with no identifiable objects. In it, we present several approaches to predict the next image of a given sequence, when the image lacks the well-defined objects, such as meteorological maps or satellite imagery. In these images no clear borders are present, and any object candidate moves, changes, appears and disappears in any image. Nevertheless, this evolution, though unrestricted, is gradual and, hence, prediction looks feasible. One of the approaches presented here, based on a spatio-temporal autoregressive (STAR) model, offers good results for these kinds of images. The main contribution of this paper is to adapt spatio-temporal models to an image prediction problem. As a byproduct of this research, we have achieved a new image compression method, suitable for images without defined shapes.     

基于位运算的固件代码变量区间分析法

变量区间分析是程序代码数据流分析的重要部分。固件反汇编代码存在字节运算和位运算两类运算,当变量取值范围较大时,依次迭代法不能高效地分析经位运算后的结果的取值区间。提出一种快速位运算方法,即根据变量取值区间范围,先将变量转化为不确定位形式再进行位运算;提出一种区间生成算法,即把不确定位形式转化为区间来实现字节运算。实验结果表明,该基于位运算的固件代码变量区间分析方法在程序变量取值范围较大时效率远远高于依次迭代法,而且在各种变量取值区间范围内所需时间稳定,并随着区间范围的增大,其有略微下降趋势。     

Automated segmentation of brain MR images

A simple, robust and efficient image segmentation algorithm for classifying brain tissues from dual echo Magnetic Resonance (MR) images is presented. The algorithm consists of a sequence of adaptive histogram analysis, morphological operations and knowledge based rules to accurately classify various regions such as the brain matter and the cerebrospinal fluid, and detect if there are any abnormal regions. It can be completely automated and has been tested on over hundred images from several patient studies. Experimental results are provided.     

Manipulation of multivalued images in image algebra

The operations between multivalued images defined hitherto in image algebra select the same stratum of each image. This paper defines a new operation that allows values from different strata of the two images to be combined. This operation is particularly useful for two-valued images, when the two values are the real and imaginary parts of a complex image.     

Combining intra-image and inter-class semantics for consumer image retrieval

Unconstrained consumer photos pose great challenge for content-based image retrieval. Unlike professional images or domain-specific images, consumer photos vary significantly. More often than not, the objects in the photos are ill-posed, occluded, and cluttered with poor lighting, focus and exposure. In this paper, we propose a cascading framework for combining intra-image and inter-class similarities in image retrieval, motivated from probabilistic Bayesian principles. Support vector machines are employed to learn local view-based semantics based on just-in-time fusion of color and texture features. A new detection-driven block-based segmentation algorithm is designed to extract semantic features from images. The detection-based indexes also serve as input for support vector learning of image classifiers to generate class-relative indexes. During image retrieval, both intra-image and inter-class similarities are combined to rank images. Experiments using query-by-example on 2400 genuine heterogeneous consumer photos with 16 semantic queries show that the combined matching approach is better than matching with single index. It also outperformed the method of combining color and texture features by 55% in average precision.     

Integer Representation and Counting in the Bit Probe Model

We examine the problem of integer representation in a nearly minimal number of bits so that the increment and the decrement (and indeed the addition and the subtraction) operations can be performed using few bit inspections and fewer bit changes. The model of computation we considered is the bit probe model, where the complexity measure counts only the bitwise accesses to the data structure. We present several efficient data structures to represent integer that use a logarithmic number of bit inspections and a constant number of bit changes per operation. The most space-efficient data structure uses only one extra bit. We also present an extension to our data structure to support efficient addition and subtraction, where the larger value is replaced by the result, while retaining the same asymptotic bounds for the increment and the decrement operations.     

A parallel algorithm for detection of linear structures in satellite images

To enhance linear structures in a gray level image, local operations with an additive score are normally used. Here a multiplicative score is used instead which gives better results than the additive one. The problem of segmenting the image of the multiplicative score is then dealt with where the threshold value can be automatically selected. The experimental results on some satellite images are reported.     

ANA HEp-2 cells image classification using number,size, shape and localization of targeted cell regions

The ANA HEp-2 medical test is a powerful tool in autoimmune disease diagnostics. The last step of this test, the interpretation of immunofluorescent images by trained experts, represents a potential source of errors and could theoretically be replaced by automated methods. Here we present a fully automatic method for recognition of types of immunofluorescent images produced by the ANA HEp-2 medical test. The proposed method makes use of the difference in number, size, shape and localization of cell regions that are targeted by the antinuclear antibodies – the humoral components of immune system that bind human antigens as a result of the immune system malfunction. The method extracts morphological properties of stained cell regions using a combination of thresholding-based and thresholding-less approaches and applies a conventional machine-learning algorithm for image classification.     

FastNBL: fast neighbor lists establishment for molecular dynamics simulation based on bitwise operations

In the molecular dynamics simulation, an important step is the establishment of neighbor list for each particle, which involves the distance calculation for each particle pair in the simulation space. However, the distance calculation will cause costly floating-point operations. In this paper, we propose a novel algorithm, called Fast Neighbor List, which establishes the neighbor lists mainly using the bitwise operations. Firstly, we design a data layout, which uses an integer value to represent the three-dimensional coordinates of a particle. Then, a bunch of bitwise operations and two subtraction operations are used to judge whether the distance between a pair of particles is within the cutoff radius. We demonstrate that our algorithm can deal with the periodic boundary seamlessly. We also use single instruction multiple data (SIMD) instructions to further improve the performance. We implement our algorithm on Intel Xeon E5-2670, ARM v8, and Sunway many-core processors, respectively. Compared with the traditional method, our algorithm achieves on average 1.79x speedup on Intel Xeon E5-2670 processor, 3.43x speedup on ARM v8 processor, and 4.03x speedup on Sunway many-core processor. After using SIMD instructions, our algorithm achieves on average 2.64x speedup and 14.43x speedup on Intel Xeon E5-2670 and ARM v8 processors, respectively.

     

Multiscale recognition of legume varieties based on leaf venation images

In this work we propose an automatic low cost procedure aimed at classifying legume species and varieties based exclusively on the characterization and analysis of the leaf venation network. The identification of leaf venation patterns which are characteristic for each species or variety is not an easy task since in some situations (specially for cultivars from the same species) the vein differences are visually indistinguishable for humans. The proposed procedure takes as input leaf images acquired using a standard scanner, processes the images in order to segment the veins at different scales, and measures different traits on them. We use these features in combination with modern automatic classifiers and feature selection techniques in order to perform recognition. The process was initially applied to recognize three different legumes in order to evaluate the improvements over previous works in the literature, and then it was employed to distinguish three diverse soybean cultivars. The results show the improvements achieved by the usage of the multiscale features. The cultivar recognition is a more challenging problem, since the experts cannot distinguish evident differences in plain sight. However, we achieve acceptable classification results. We also analyze the feature relevance and identify, for each classifier, a small set of distinctive traits to differentiate the species and varieties.     

基于密文医学图像的可逆信息隐藏算法

针对现有算法嵌入容量低以及应用于医学图像时效果不稳定的问题,提出一种基于斜线分组和梯度算子的密文医学图像的可逆信息隐藏算法。算法利用按位异或操作对医学图像加密,接着对密文图像分块,每块再斜线分为4组,根据待嵌入2比特信息翻转对应像素组的最低3位有效位（3LSBs）。接收端先解密图像,然后根据信息隐藏密钥和梯度算子恢复原始图像并提取信息。实验结果显示,算法能无损地恢复原始医学图像,准确地提取嵌入信息,在分块大小相同的情况下,有更好的嵌入容量,更高的PSNR值和更低的信息提取错误率。     

Segmentation and adaptive assimilation for detail-preserving display of high-dynamic range images

Realistic display of high-dynamic range images is a difficult problem. Previous methods for high-dynamic range image display suffer from halo artifacts or are computationally expensive. We present a novel method for computing local adaptation luminance that can be used with several different visual adaptation-based tone-reproduction operators for displaying visually accurate high-dynamic range images. The method uses fast image segmentation, grouping, and graph operations to generate local adaptation luminance. Results on several images show excellent dynamic range compression, while preserving detail without the presence of halo artifacts. With adaptive assimilation, the method can be configured to bring out a high-dynamic range appearance in the display image. The method is efficient in terms of processor and memory use.     

一种优化的Freeman链码及其在工程图纸绘制中的应用

提出一种适合于线状结构二值图象的最优化Ｆｒｅｅｍａｎ链码,并讨论其在绘图仪图纸绘制中的应用。与传统的Ｆｒｅｅｍａｎ链码相比,本文的方法是基于数字化点阵图象的无失真编码。根据图象中直线段长度的统计特性选择码长,以使编码压缩率为最高。理论分析和实验结果证明,本方法优于已有的方法并易于工程图纸的图象存储和在绘图仪上的输出。