Scale-space derived from B-splines

This paper proposes a scale-space theory based on B-spline kernels. Our aim is twofold: 1) present a general framework, and show how B-splines provide a flexible tool to design various scale-space representations. In particular, we focus on the design of continuous scale-space and dyadic scale-space frame representations. A general algorithm is presented for fast implementation of continuous scale-space at rational scales. In the dyadic case, efficient frame algorithms are derived using B-spline techniques to analyze the geometry of an image. The relationship between several scale-space approaches is explored. The behavior of edge models, the properties of completeness, causality, and other properties in such a scale-space representation are examined in the framework of B-splines. It is shown that, besides the good properties inherited from the Gaussian kernel, the B-spline derived scale-space exhibits many advantages for modeling visual mechanism including the efficiency, compactness, orientation feature and parallel structure     

A spatio-frequency trade-off scale for scale-space filtering

We study implementation issues for spatial convolution filters and their Fourier alternative, with the aim to optimize the accuracy of filter output. We focus on Gaussian scale-space filters and show that there exists a trade-off scale that subdivides the available scale range into two subintervals of equal length. Below this trade-off scale Fourier filtering yields more accurate results than spatial filtering; above it is the other way around. This should be contrasted with demands of computational speed, which show the opposite tenet     

指令集仿真器的关键技术

为适应嵌入式系统开发中对指令集仿真器仿真速度的要求,提出一种改进的指令集仿真技术.该技术在现有的静态多核仿真器基础上引入指令预处理、动态译码缓存、多线程C函数生成和动态调度运行等技术,以实现对仿真器性能的优化.该技术已成功应用于中国科学院微电子所自主研发的IME-Diamond DSP处理器的多核指令集仿真器OPT-ISS中.实际应用程序测试结果表明,该技术在仿真速度提升方面有明显效果.     

A new approach to combine texture compression and filtering

Texture mapping has been widely used to improve the quality of 3D rendered images. To reduce the storage and bandwidth impact of texture mapping, compression systems are commonly used. To further increase the quality of the rendered images, texture filtering is also often adopted. These two techniques are generally considered to be independent. First, a decompression step is executed to gather texture samples, which is then followed by a separate filtering step. We have investigated a system based on linear transforms that merges both phases together. This allows more efficient decompression and filtering at higher compression ratios. This paper formally presents our approach for any linear transformation, how the commonly used discrete cosine transform can be adapted to this new approach, and how this method can be implemented in real time on current-generation graphics cards using shaders. Through reuse of the existing hardware filtering, fast magnification and minification filtering is achieved. Our implementation provides fully anisotropically filtered samples four to six times faster than an implementation using two separate phases for decompression and filtering. Additionally, our transform-based compression also provides increased and variable compression ratios over standard hardware compression systems at a comparable or better quality level.     

A blind audio watermarking technique based on a parametric quantization index modulation

In this paper, we propose an efficient transform-based blind audio watermarking technique by introducing a parametric quantization index modulation (QIM). Theoretical expressions for the signal to watermark ratio and probability of error are derived and then used in an optimization technique based on the Lagrange multipliers method to find the optimal values for the parameters of the parametric QIM that ensure the imperceptibility while maximizing the robustness under an additive white Gaussian noise (AWGN) attack. Moreover, a fast scheme for the implementation of the proposed watermarking technique is developed and an efficient procedure is suggested to find the interval for the best selection of the watermark embedding positions that provide a good trade-off between the effects of high and low pass filtering attacks. The parameters of the resulting optimal parametric QIM coupled with the embedding positions constitute a highly robust secret key for the proposed watermarking technique. We also carry out several experiments to show the usefulness of the theoretical analysis presented in the paper and compare the proposed technique with other existing QIM-based watermarking techniques by considering known attacks such as AWGN, re-quantization, resampling, low/high pass filtering, amplitude scaling and common lossy compressions.

     

多光谱遥感影像建筑物提取

针对传统遥感影像目标提取对数据要求严格及应用受限的问题,提出一种基于非线性尺度空间滤波的建筑物提取算法.首先,构造多光谱影像各个波段的非线性尺度空间并进行迭代滤波;然后,搜索全局影像的标准差曲线的第一个谷点,停止迭代过程;最后,利用最大类间方差法分别对各个波段的滤波结果进行二值化.为了验证本文方法的有效性,选取福州市的一幅航空影像,并与同类方法进行对比.试验结果表明,本文算法能在平滑噪声的同时保留建筑物边缘信息,对于提取排列紧密的建筑物有更好的效果,在保证查准率的前提下,查全率有5%以上的提高.     

Regularization, Scale-Space, and Edge Detection Filters

Computational vision often needs to deal with derivatives ofdigital images. Such derivatives are not intrinsic properties ofdigital data; a paradigm is required to make them well-defined.Normally, a linear filtering is applied. This can be formulated interms of scale-space, functional minimization, or edge detectionfilters. The main emphasis of this paper is to connect these theoriesin order to gain insight in their similarities and differences. We donot want, in this paper, to take part in any discussion of how edgedetection must be performed, but will only link some of the current theories. We take regularization (or functional minimization) as astarting point, and show that it boils down to Gaussian scale-space ifwe require scale invariance and a semi-group constraint to besatisfied. This regularization implies the minimization of afunctional containing terms up to infinite order of differentiation.If the functional is truncated at second order, the Canny-Deriche filter arises. It is also shown that higher dimensional regularizationboils down to a rotated version of the one dimensional case, whenCartesian invariance is imposed and the image is vanishing at theborders. This means that the results from 1D regularization can beeasily generalized to higher dimensions. Finally we show how anefficient implementation of regularization of order n can be made byrecursive filtering using 2n multiplications and additions peroutput element without introducing any approximation.     

A novel white blood cell segmentation scheme based on feature space clustering

This paper presents a novel white blood cell (WBC) segmentation scheme based on two feature space clustering techniques: scale-space filtering and watershed clustering. In this scheme, nucleus and cytoplasm, the two components of WBC, are extracted, respectively, using different methods. First, a sub image containing WBC is separated from the original cell image. Then, scale-space filtering is used to extract nucleus region from sub image. Later, a watershed clustering in 3-D HSV histogram is processed to extract cytoplasm region. Finally, morphological operations are performed to obtain the entire connective WBC region. Through feature space clustering techniques, this scheme successfully avoids the variety and complexity in image space, and can effectively extract WBC regions from various cell images of peripheral blood smear. Experiments demonstrate that the proposed scheme performs much better than former methods.     

基于垂直网站的网络信息支持系统研究

网络信息支持系统是指根据用户的偏好,为用户主动搜集、筛选和推送网上信息的主动式信息服务系统。讨论了网络信息支持系统的概念、作用,分析了基于垂直网站的信息支持系统的特点和逻辑功能,给出了系统的逻辑模型,对系统开发研制中的关键技术进行了研究。     

Comparison of SIFT,Bi-SIFT,and Tri-SIFT and their frequency spectrum analysis

This paper aims to explore frequency behavior of isotropic (regular SIFT) and anisotropic (Bi-SIFT and Tri-SIFT) versions of the scale-space keypoint detection algorithm SIFT. We introduced a new smoothing function Trilateral filter that can be used in formation of a scale-space as an alternative to the Gaussian scale-space. The number of matching pixels, warping error, and scatteredness are employed in comparison. We made the comparison out of face dataset and object dataset for scale, orientation, and view-angle transformations as well as lighting and compression variations. The comparison results show that anisotropic smoothing detects more keypoints than isotropic one. The Tri-SIFT is more robust to variation in viewpoint angle.     

基于快速尺度空间特征检测的手势识别方法

在基于几何模型的手势识别方法中,尺度空间特征检测是一种最常用的方法。由于传统方法涉及大量的高斯卷积运算,计算非常复杂。提出了一种快速的尺度空间特征检测方法,采用一组简单的矩形特征模板近似传统方法中复杂的高斯导数卷积模板,得到了尺度空间几何特征的快速检测子。通过对手势图像中Blob和Ridge结构的检测,得到手掌和手指结构的描述,进而完成手势识别。矩形特征模板的卷积可以用积分图进行快速计算,该方法使特征检测的速度得到了很大提高。在标准数据集和自然环境图像数据上的实验结果表明,该方法在保证识别准确率的同时,有效地提高了手势识别的实时性。     

Scale-space for discrete signals

A basic and extensive treatment of discrete aspects of the scale-space theory is presented. A genuinely discrete scale-space theory is developed and its connection to the continuous scale-space theory is explained. Special attention is given to discretization effects, which occur when results from the continuous scale-space theory are to be implemented computationally. The 1D problem is solved completely in an axiomatic manner. For the 2D problem, the author discusses how the 2D discrete scale space should be constructed. The main results are as follows: the proper way to apply the scale-space theory to discrete signals and discrete images is by discretization of the diffusion equation, not the convolution integral; the discrete scale space obtained in this way can be described by convolution with the kernel, which is the discrete analog of the Gaussian kernel, a scale-space implementation based on the sampled Gaussian kernel might lead to undesirable effects and computational problems, especially at fine levels of scale; the 1D discrete smoothing transformations can be characterized exactly and a complete catalogue is given; all finite support 1D discrete smoothing transformations arise from repeated averaging over two adjacent elements (the limit case of such an averaging process is described); and the symmetric 1D discrete smoothing kernels are nonnegative and unimodal, in both the spatial and the frequency domain     

An Analysis of the Factors Affecting Keypoint Stability in Scale-Space

The most popular image matching algorithm SIFT, introduced by D. Lowe a decade ago, has proven to be sufficiently scale invariant to be used in numerous applications. In practice, however, scale invariance may be weakened by various sources of error inherent to the SIFT implementation affecting the stability and accuracy of keypoint detection. The density of the sampling of the Gaussian scale-space and the level of blur in the input image are two of these sources. This article presents a numerical analysis of their impact on the extracted keypoints stability. Such an analysis has both methodological and practical implications, on how to compare feature detectors and on how to improve SIFT. We show that even with a significantly oversampled scale-space numerical errors prevent from achieving perfect stability. Usual strategies to filter out unstable detections (e.g., poorly contrasted extrema) are shown to be inefficient. We also prove that the effect of the error in the assumption on the initial blur is asymmetric and that the method is strongly degraded in the presence of aliasing or without a correct assumption on the camera blur. This analysis leads to a series of practical recommendations.     

基于指令集模拟器的处理器建模与验证

介绍处理器仿真建模技术以及指令集模拟器在其中的应用,讨论处理器ISA, MA模型建立以及指令精确、时钟精确的指令集模拟器实现方法,提出一种基于多线程技术的调试器集成方法,介绍指令集模拟器在一款密码专用微处理器开发过程中的具体应用方法。     

改进的语音特征提取方法及其应用

介绍处理器仿真建模技术以及指令集模拟器在其中的应用,讨论处理器ISA,MA模型建立以及指令精确、时钟精确的指令集模拟器实现方法,提出一种基于多线程技术的调试器集成方法,介绍指令集模拟器在一款密码专用微处理器开发过程中的具体应用方法.     

Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about (i) parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation in terms of recursive filters over time, (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision. We show that by distributing the intermediate temporal scale levels according to a logarithmic distribution, we obtain a new family of temporal scale-space kernels with better temporal characteristics compared to a more traditional approach of using a uniform distribution of the intermediate temporal scale levels. Specifically, the new family of time-causal kernels has much faster temporal response properties (shorter temporal delays) compared to the kernels obtained from a uniform distribution. When increasing the number of temporal scale levels, the temporal scale-space kernels in the new family do also converge very rapidly to a limit kernel possessing true self-similar scale-invariant properties over temporal scales. Thereby, the new representation allows for true scale invariance over variations in the temporal scale, although the underlying temporal scale-space representation is based on a discretized temporal scale parameter. We show how scale-normalized temporal derivatives can be defined for these time-causal scale-space kernels and how the composed theory can be used for computing basic types of scale-normalized spatio-temporal derivative expressions in a computationally efficient manner.     

一个基于向量空间模型的OUTLOOK垃圾邮件过滤器

邮件过滤是指从大量的邮件中过滤掉含有无用信息的垃圾邮件,以帮助用户得到所需的有用邮件。本文将介绍一个基于向量空间模型的OUTLOOK邮件过滤器的设计与实现,它包含了邮件过滤和训练两个子系统,其过滤方法还对传统的向量空间模型法做了改进,使之更适合于垃圾邮件过滤。     

用计算机模拟光学滤波过程的研究

介绍了一种用计算机模拟光学滤波过程的方法,并给出了MATLAB平台下的仿真结果.相对于传统的光学滤波过程更方便和快捷,是研究光学滤波的有效手段.     

一个基于各向异性的热传导方程导出的图像锐化算子

Ｗｉｔｋｉｎ等人的迟度空间滤波办法是对原图像一个逐渐平滑的过程,这个过程可以用传统的热传导方程来描述,假设“热量”由低温向高温外流动,导出一个各向异性的反向热传导方程。将它用于图像处理,通过选择合适的各向异性函数,使得在盛兴趣的边缘处“热量”由低温向高温处流动,即“灰度”从“