用于位图激光打标的数字曲线多边形逼近算法

为了弥补激光打标系统中传统的位图打标方式的不足,提出了一种基于位图矢量化技术来实现位图激光打标的方法.该方法首先以数字曲线的形式提取出位图的骨架或边缘特征,然后采用多边形逼近的方法实现数字曲线的矢量化,最后再利用连续打标方式完成矢量化数据的打标输出.重点讨论了其中的数字曲线多边形逼近算法,分别对Freeman链码生成算...     

Novel method to obtain the optimal polygonal approximation of digital planar curves based on Mixed Integer Programming

Polygonal approximations of digital planar curves are very useful for a considerable number of applications in computer vision. A great interest in this area has generated a huge number of methods for obtaining polygonal approximations. A good measure to compare these methods is known as Rosin’s merit. This measure uses the optimal polygonal approximation, but the state-of-the-art methods require a tremendous computation time for obtaining this optimal solution.We focus on the problem of obtaining the optimal polygonal approximation of a digital planar curve. Given N ordered points on a Euclidean plane, an efficient method to obtain M points that defines a polygonal approximation with the minimum distortion is proposed.The new solution relies on Mixed Integer Programming techniques in order to obtain the minimum value of distortion. Results, show that computation time for the new method dramatically decreases in comparison with state-of-the-art methods for obtaining the optimal polygonal approximation.     

A new thresholding approach for automatic generation of polygonal approximations

The present paper proposes a new algorithm for automatic generation of polygonal approximations of 2D closed contours based on a new thresholding method. The new proposal computes the significance level of the contour points using a new symmetric version of the well-known Ramer, Douglas–Peucker method, and then a new Adaptive method is applied to threshold the normalized significance level of the contour points to generate the polygonal approximation. The experiments have shown that the new algorithm has good performance for generating polygonal approximations of 2D closed contours. Furthermore, the new algorithm does not require any parameter to be tuned.     

Unsupervised multistage image classification using hierarchical clustering with a Bayesian similarity measure.

A new multistage method using hierarchical clustering for unsupervised image classification is presented. In the first phase, the multistage method performs segmentation using a hierarchical clustering procedure which confines merging to spatially adjacent clusters and generates an image partition such that no union of any neighboring segments has homogeneous intensity values. In the second phase, the segments resulting from the first stage are classified into a small number of distinct states by a sequential merging operation. The region-merging procedure in the first phase makes use of spatial contextual information by characterizing the geophysical connectedness of a digital image structure with a Markov random field, while the second phase employs a context-free similarity measure in the clustering process. The segmentation procedure of region merging is implemented as a hierarchical clustering algorithm whereby a multiwindow approach using a pyramid-like structure is employed to increase computational efficiency while maintaining spatial connectivity in merging. From experiments with both simulated and remotely sensed data, the proposed method was determined to be quite effective for unsupervised analysis. In particular, the region-merging approach based on spatial contextual information was shown to provide more accurate classification of images with smooth spatial patterns.     

Stochastic processes that generate polygonal and related randomfields

A reversible, ergodic, Markov process taking values in the space of polygonally segmented images is constructed. The stationary distribution of this process can be made to correspond to a Gibbs-type distribution for polygonal random fields as introduced by Arak and Surgailis (1989) and a few variants thereof, such as those arising in Bayesian analysis of random fields. Extensions to generalized polygonal random fields are presented where the segmentation boundaries are not necessarily straight line segments     

Polygonal Approximation of Digital Curves to Preserve Original Shapes

In this letter, we propose a novel polygonal approximation of digital curves that preserve original shapes. The proposed method first detects break points, which have two different consecutive vectors, and sets an initial dominant point set. The approximation is then performed iteratively by deleting a dominant point using a novel distance, which can measure both the distance and the angle acuteness. The experimental results show that the proposed method can preserve original shapes and is appropriate for various shapes, including slabsided shapes.     

Inferring road maps from sparsely sampled GPS traces

We propose a novel segmentation-and-grouping framework for road map inference from sparsely sampled GPS traces. First, we extend Density-Based Spatial Clustering of Application with Noise with an orientation constraint to partition the entire point set of the traces into point clusters representing the road segments. Second, we propose an adaptive k-means algorithm that the k value is determined by an angle threshold to reconstruct nearly straight line segments. Third, the line segments are grouped according to the ‘Good Continuity’ principle of Gestalt Law to form a ‘Stroke’ for recovering the road map. Experimental results demonstrate that our algorithm is robust to noises and sampling rates. In comparison with previous work, our method has advantages to infer road maps from sparsely sampled GPS traces.     

A polygonal approximation of shape boundaries of marine plankton based-on genetic algorithms

Polygonal approximation of a shape boundary can provide a minimalistic representation of the shape. It can also accelerate the processing speed of feature extraction. Our interest is in applying such a method to approximate the boundaries of plankton shapes. A polygonal approximation method based on genetic algorithms has been designed to compactly describe the plankton shapes by polygons. Firstly, two artificial digital curves are used to test the performance of our algorithm. Results are compared with other existing algorithms which show that our algorithm has efficient performance for solving the problem of the polygonal approximation. Secondly, the proposed method is applied to a selection of plankton images under three different approximation levels to a polygonal fit and then five evaluation criteria are applied to determine which approximation level of a particular image is most suitable for describing the shape. The stability and robustness of three approximation levels are also tested.     

Optimal polygonal approximation of digital curves

An algorithm is described in this paper which finds the optimal polygonal approximation of a digital curve i.e. an approximation with the minimum possible number of line segments. The algorithm can be used either for digital curves in 2-space or 1-space, i.e. in waveforms.     

快速锁定、宽带双环路频率综合器的数字粗调环路

提出了一种用于宽带、双环路频率综合器的粗调环路结构.该粗调环路由数字电路设计实现,包含逐次逼近寄存器和新结构的频率比较单元两个模块.其中,频率比较单元在一定的参考时间内对预分频器的输出信号周期进行计数,然后通过比较计数结果与预设值的大小来估计VCO输出频率.对比较误差进行了详细分析,分析表明,在一定的比较时间内该结构的比较误差比现有结构小20倍,而且由于重复利用可编程分频器作为粗调环路的一部分,整体电路也大为简化.     

Digital Coarse Tuning Loop for Wide-Band Fast-Settling Dual-Loop Frequency Synthesizers

提出了一种用于宽带、双环路频率综合器的粗调环路结构.该粗调环路由数字电路设计实现,包含逐次逼近寄存器和新结构的频率比较单元两个模块.其中,频率比较单元在一定的参考时间内对预分频器的输出信号周期进行计数,然后通过比较计数结果与预设值的大小来估计VCO输出频率.对比较误差进行了详细分析,分析表明,在一定的比较时间内该结构的比较误差比现有结构小20倍,而且由于重复利用可编程分频器作为粗调环路的一部分,整体电路也大为简化.     

半矢量模式求解在光波导弯曲损耗计算中的应用

以柱坐标下的半矢量波动方程为基础,采用基于完美匹配层(PML)边界条件的有限差分方法,对弯曲波导进行模式求解,进而得到波导弯曲引起的辐射损耗.基于计算得到的直弯波导的模场分布,采用二维重叠积分法计算了两者连接时的过渡损耗.计算结果与已有实验结果符合较好.采用该方法,研究了SOI脊型波导的弯曲损耗与波导结构参数之间的关系,并对直弯波导的连接进行了优化.     

Error probabilities of two-dimensional M-ary signaling in fading

A new exact method for computing the average symbol error probability of two-dimensional M-ary signaling in slow fading is presented. The method is generally applicable to polygonal decision regions. The exact average symbol error rate of coherent 16-star-QAM is obtained and compared to that of 16-rectangular-QAM. New results for optimum ring ratios of 16-star-QAM in additive white Gaussian noise (AWGN) and in slow fading are also given     

Exposing photo manipulation with inconsistent perspective geometry

Manipulated digital image is got interesting in recent years. Digital images can be manipulated more easily with the aid of powerful image editing software. Forensic techniques for authenticating the integrity of digital images and exposing forgeries are urgently needed. A geometric-based forensic technique which exploits the principle of vanishing points is proposed. By means of edge detection and straight lines extraction, intersection points of the projected parallel lines are computed. The normalized mean value （NMV） and normalized standard deviation （NSD） of the distances between the intersection points are used as evidence for image forensics. The proposed method employs basic rules of linear perspective projection, and makes minimal assumption. The only requirement is that the parallel lines are contained in the image. Unlike other forensic techniques which are based on low-level statistics, this method is less sensitive to image operations that do not alter image content, such as image resampling, color manipulation, and lossy compression. This method is demonstrated with images from York Urban database. It shows that the proposed method has a definite advantage at separating authentic and forged images.     

集成成像同名像点自筛选技术

从集成成像三维获取技术出发,利用光线追迹的方 法,理论分析了元素图像阵列中同名像点之间的关联性,得到了同名像点之间的等差关系, 并利用此关系式对光学获取的元素图像阵列提取的同名像点的结果进行筛选,去除误差大的 配准结果,从 而提升同名像点的配准精度。光学实验结果显示,对于同一物体进行三维获取,经本文的集 成成像 同名像点自筛选技术得到的结果是500mm,相对误差为0.4% ;而传统技术重构得到的结果为502mm,相对 误差为0.8%。我们所提的这种方法有效提升了再现精度,提升幅度为 50%,为集成成像三维形貌获取技术实现高精度获取提供了有效的技 术支持。     

直线特征立体匹配中的不确定性问题

直线特征断裂和提取不完整、过提取是直线特征提取结果中常见的不确定性问题,是影响直线特征立体匹配结果的关键因素。提出一种以边缘检测结果为依据定量描述这些不确定性的方法,依据已提取直线特征延长线附近边缘点分布,定义直线特征端点定位的概率密度函数,以单条直线特征的不确定性描述为基础,计算断裂直线特征编组假设的可靠性测度,进而计算用于衡量立体图像中任意两条直线特征匹配假设可靠性的核线约束测度和灰度相似性测度。应用真实遥感立体图像,对比已有方法,结果证明了本文方法能够有效提高直线特征匹配结果对于两类直线特征提取不确定性问题的抑制能力。      

The boundary element method in the forward and inverse problem of electrical impedance tomography

In this paper, a new formulation of the reconstruction problem of electrical impedance tomography (EIT) is proposed. Instead of reconstructing a complete two-dimensional picture, a parameter representation of the gross anatomy is formulated, of which the optimal parameters are determined by minimizing a cost function. The two great advantages of this method are that the number of unknown parameters of the inverse problem is drastically reduced and that quantitative information of interest (e.g., lung volume) is estimated directly from the data, without image segmentation steps. The forward problem of EIT is to compute the potentials at the voltage measuring electrodes, for a given set of current injection electrodes and a given conductivity geometry. In this paper, it is proposed to use an improved boundary element method (BEM) technique to solve the forward problem, in which flat boundary elements are replaced by polygonal ones. From a comparison with the analytical solution of the concentric circle model, it appears that the use of polygonal elements greatly improves the accuracy of the BEM, without increasing the computation time. In this formulation, the inverse problem is a nonlinear parameter estimation problem with a limited number of parameters. Variants of Powell's and the simplex method are used to minimize the cost function. The applicability of this solution of the EIT problem was tested in a series of simulation studies. In these studies, EIT data were simulated using a standard conductor geometry and it was attempted to find back this geometry from random starting values. In the inverse algorithm, different current injection and voltage measurement schemes and different cost functions were compared. In a simulation study, it was demonstrated that a systematic error in the assumed lung conductivity results in a proportional error in the lung cross sectional area. It appears that our parametric formulation of the inverse problem leads to a stable minimization problem, with a high reliability, provided that the signal-to-noise ratio is about ten or higher.     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

The computation of polygonal approximations for 2D contours based on a concavity tree

In this work, a new proposal to improve some methods based on the merge approach to obtain polygonal approximations in 2D contours is presented. These methods use a set of candidate dominant points (CDPs) to obtain a polygonal approximation. Then, redundant candidate dominant points of the set of CDPs are deleted, and the remaining dominant points will be the polygonal approximation of the original contour. The main drawback of most of these methods is that they use all breakpoints as CDPs and most of these breakpoints depict only the noise of the original contour.Our proposal, based on a concavity tree, obtains a more reduced and significant set of CDPs. When this proposal is used by some methods based on the merge approach (the Masood methods and the Carmona method), their computation times are reduced. The experimental results show that the new proposal is efficient and improves the tested methods.