基于PCNN图像骨架毛刺去除方法的研究

针对图像骨架毛刺去除的过程中图像骨架的几何结构很难保持完整的问题,提出了基于脉冲耦合神经网络(PCNN)图像骨架毛刺去除方法.该方法根据图像的性质对PCNN进行了改进,并定义了骨架中的端点、交叉点等概念.根据PCNN对应神经元同步激发而产生脉冲的传播特性,判断和除去骨架上的毛刺.仿真结果表明,该方法不仅能完整而准确的反应目标图像的几何结构,而且具有很强的抗干扰和去噪能力.     

一种模板匹配与高适应性的裂缝骨架提取算法

在对裂缝图像骨架进行提取时,已有的算法通常存在细化后骨架主体信息缺失、毛刺去除效果随图像规模增大而快速下降等问题。针对上述问题,该算法提出一种模板匹配与高适应性的裂缝骨架提取算法。首先,结合模板匹配对Rosenfeld细化算法进行改进,以保留骨架主体结构;然后提出一种高适应性毛刺去除算法,以分支像素点数量与细化后骨架图像目标像素点数量之比作为判断标准,可以高效适应不同目标像素点密度和规模的裂缝图像。实验结果表明,该算法能够有效实现单一像素宽度骨架并尽可能去除骨架毛刺,有一定的可行性及优越性。     

字符识别中毛刺的去除方法

在字符识别中提取字符特征前经常需要将图像细化,但细化过程会受到照明系统和字符本身质量的影响而产生毛刺。毛刺会使特征提取发生错误,进而影响最终的字符识别结果。针对此问题提出一种基于搜索毛刺生长点的毛刺去除方法,利用此方法可以将毛刺剔除并且保证图像的其它有用信息不被破坏。     

字符识别中毛刺的去除方法

在字符识别中提取字符特征前经常需要将图像细化，但细化过程会受到照明系统和字符本身质量的影响而产生毛刺。毛刺会使特征提取发生错误，进而影响最终的字符识别结果。针对此问题提出一种基于搜索毛刺生长点的毛刺去除方法，利用此方法可以将毛刺剔除并且保证图像的其它有用信息不被破坏。     

一种基于遮挡区域补偿的眼镜去除方法

为了提高戴眼镜人脸图像的识别率, 提出了一种从人脸图像中检测并去除眼镜的方法。首先对输入的戴眼镜人脸图像与系统预留的无眼镜人脸图像进行基于人眼位置的标定, 检测出眼镜遮挡区域, 再用无眼镜人脸图像中对应的遮挡区域对戴眼镜人脸图像进行补偿, 从而合成了对应输入图像的不戴眼镜的人脸图像。实验结果表明, 该方法能有效地合成无眼镜人脸图像, 将合成后的人脸图像再应用于人脸识别系统, 识别率显著提高。     

基于改进ZS细化算法的指针骨架提取

在进行指针式仪表示数读取时,由于指针的不规则性引起的直线检测偏差,会导致最后的读数出现误差。针对此问题提出一种改进的ZS细化算法。经典的ZS细化算法针对指针不同角度所需的斜线细化时,细化后的指针不满足单像素骨架要求,会出现冗余像素,并且由于仪表指针在进行细化的过程中出现毛刺。改进的ZS细化算法首先在原有算法的基础上增设斜线冗余像素删除模板使得细化之后的指针骨架为单像素宽。并利用路径规划的思想,使得图像以像素为单位进行路径搜索,通过设置判断条件,使得最后的指针去掉所有分支。实验表明,改进算法实现了指针的单像素和无毛刺,并且使得指针式仪表的示数平均相对误差降低了0.22%。     

一种基于骨架的彩色图像分割方法

在自然场景的图象分割识别中,如何克服光照变化对分割结果的影响是研究面临的一个关键问题.针对传统方法的限制,提出了一种基于骨架匹配的彩色图像分割识别方法.利用颜色和纹理信息对彩色图像进行初分割,然后在初分割的基础上,利用目标的骨架模型在假设检验判决的框架下在初分割的目标近邻区域对包括阴影区域在内的候补目标区域进行融合处理.实验结果表明该算法是一种行之有效的算法,在对自然场景图像的目标分割识别中能一定程度上消除光照变化对图像分割的影响,取得与人的主观视觉感知相一致的图像分割结果.     

手背静脉图像骨架特征提取的算法

手背静脉近红外图像识别是一种新的非接触式生物认证技术。对采集的手背静脉图像进行了增强处理。对二值化图像采用四邻域区域生长的方法,去除噪声斑块。对处理后的静脉图像采用了一种快速细化的细化算法。分析和解决了细化后特征点——交叉点的提取。针对细化后骨架中所引入的另一类噪声——毛刺和静脉图像细化后的特点,提出了一种毛刺修复算法。实验结果表明,经过该算法处理后得到的骨架图像,能够较好的反映静脉纹理特性。     

一种去除图像毛刺的快速算法

本文根据图像跟踪中毛刺的具体特点,提出一种快速去除算法,该方法穿插于边界跟踪的整个过程之中。实验证明,去除效果明显,处理速度快。     

一种改进的基于样例的目标物体移除方法

基于P-M方程可以进行梯度和等照度线两个方向的分解,将其引入Criminisi算法,提出一种改进的基于样例的目标物体移除方法。该方法在平坦区和边界区有不同的优先权计算模式,可以获得更加可靠的优先权值,使修复后的图像不仅具有等照度线的连通性而且边缘得到强化。     

采用PCNN模板的二值指纹图像改进细化算法

针对传统PCNN 细化图像时存在细化不彻底、纹线的断裂、产生很多毛刺等问题,给出了一种采用PCNN并发特点对二值指纹图像进行细化的改进算法。经过分析图像细化后留下的毛刺特征,提出了几种消除模板。采用该模板能消除指纹细化后的毛刺;也有效地解决了细化不彻底的问题。利用FVC2004标准指纹图像库仿真的结果表明,指纹图像细化彻底,且能有效地消除纹线断裂和毛刺的产生。应用于指纹图像识别系统,提高了指纹的识别率。     

Systematic design space exploration using a template-based ontological method

The realization of complex engineered systems using models that are typically incomplete, inaccurate and not of equal fidelity requires the understanding and prediction of process behavior in design. This necessitates the need for extending designer’s abilities in making design decisions that are robust, flexible and modifiable particularly in the early stages of design. To address this requirement, we propose in this paper, an ontology for design space exploration and a template-based ontological method that supports systematic design space exploration ensuring the determination of the right combination of design information that meets the different goals and requirements set for a process chain. Using the proposed method, a designer is able to (1) systematically adjust the design space in due time to manage the risks of errors accumulating and propagating during the design of different stages of the process chain, (2) improve the ability to communicate and understand the interactions between design information in the process chain. We achieve the said through (1) procedure for design space exploration is identified to determine the sequence of activities needed for the systematic exploration of design space under uncertainty; (2) the decision-based design information flow is archived using the design space exploration process template and represented by utilizing frame-based ontology to facilitate the management of re-usable information. We demonstrate the efficacy of this template-based ontological method for design space exploration by carrying out the design of a multi-stage hot rod rolling system in steel manufacturing process chain.     

A template-based method for identifying input regions in survey forms

This paper presents a template-based approach for survey form analysis. User input areas on the survey forms are identified. Regions of interest (ROI) include checkboxes, radio buttons, underlined and parenthesized handwritten regions. Scale invariant vertical and horizontal projection profile patterns for each of those four types of ROI are defined as matching templates. The templates are matched against projection profiles of connected components in the given survey form. Experiments with 158 different forms at 100-dpi scan resolution resulted in recognition rate of over 99%.     

Benchmarking template-based tracking algorithms

For natural interaction with augmented reality (AR) applications, good tracking technology is key. But unlike dense stereo, optical flow or multi-view stereo, template-based tracking which is most commonly used for AR applications lacks benchmark datasets allowing a fair comparison between state-of-the-art algorithms. Until now, in order to evaluate objectively and quantitatively the performance and the robustness of template-based tracking algorithms, mainly synthetically generated image sequences were used. The evaluation is therefore often intrinsically biased. In this paper, we describe the process we carried out to perform the acquisition of real-scene image sequences with very precise and accurate ground truth poses using an industrial camera rigidly mounted on the end effector of a high-precision robotic measurement arm. For the acquisition, we considered most of the critical parameters that influence the tracking results such as: the texture richness and the texture repeatability of the objects to be tracked, the camera motion and speed, and the changes of the object scale in the images and variations of the lighting conditions over time. We designed an evaluation scheme for object detection and interframe tracking algorithms suited for AR and other computer vision applications and used the image sequences to apply this scheme to several state-of-the-art algorithms. The image sequences are freely available for testing, submitting and evaluating new template-based tracking algorithms, i.e. algorithms that detect or track a planar object in an image sequence given only one image of the object (called the template).     

TM-generation model: a template-based method for automatically solving mathematical word problems

The Journal of Supercomputing - In this study, we propose a novel model called template-based multitask generation (TM-generation) that can improve the problem-solving accuracy of mathematical word...     

Functional template-based SAR image segmentation

We present an approach to automatic image segmentation, in which user selected sets of examples and counter-examples supply information about the specific segmentation problem. In our approach, image segmentation is guided by a genetic algorithm which learns the appropriate subset and spatial combination of a collection of discriminating functions, associated with image features. The genetic algorithm encodes discriminating functions into a functional template representation, which can be applied to the input image to produce a candidate segmentation. The performance of each candidate segmentation is evaluated within the genetic algorithm, by a comparison to two physics-based techniques for region growing and edge detection. Through the process of segmentation, evaluation, and recombination, the genetic algorithm optimizes functional template design efficiently. Results are presented on real synthetic aperture radar (SAR) imagery of varying complexity.     

A higher-order removal method

A major attraction of functional languages is their support for higher-order functions. This facility increases the expressive power of functional languages by allowing concise and reusable programs to be written. However, higher-order functions are more expensive to execute and to analyse for optimisations.To reduce the performance penalties of using higher-order functions, this paper proposes two transformation algorithms which could automatically removemost higher-order functions from functional programs. The first algorithm uses aneta-expansion technique to eliminate expressions which return function-type results. The second algorithm uses afunction specialisation technique to eliminate expressions with function-type arguments. Together, they remove higher-order functions by transforming each higher-order program to an equivalent first-order (or lower-order) program. We shall prove that the two algorithms areterminating (when applied to well-typed programs) and alsototally-correct (preserving non-strict semantics).A preliminary version of this paper appeared in the Proceedings of 15th Australian Computer Science Conference, Hobart, Tasmania, January 1992.     

Topology preservation of template-based 6-subiteration reduction operations

Generally, a reduction operation (e.g., thinning and shrinking) on 3D binary images can be represented as a set of reduction templates where every object voxel of the image satisfying any template is turned to a background voxel. Generally, it is rather difficult, error-prone and time-consuming for verifying the topological soundness of a 3D parallel reduction operation. This paper proposes sufficient conditions of time complexity O(n) for verifying the topological soundness of 3D parallel 6-subiteration reduction operations of n templates where such a kind of 3D reduction operations is performed alternatively from the six orthogonal directions to turn object voxels to background voxels. By such sufficient conditions, the topology soundness of a 3D 6-subiteration parallel reduction operation can be verified by checking each and every of its templates.     

A higher-order removal method

A major attraction of functional languages is their support for higher-order functions. This facility increases the expressive power of functional languages by allowing concise and reusable programs to be written. However, higher-order functions are more expensive to execute and to analyse for optimisations. To reduce the performance penalties of using higher-order functions, this paper proposes two transformation algorithms which could automatically removemost higher-order functions from functional programs. The first algorithm uses aneta-expansion technique to eliminate expressions which return function-type results. The second algorithm uses afunction specialisation technique to eliminate expressions with function-type arguments. Together, they remove higher-order functions by transforming each higher-order program to an equivalent first-order (or lower-order) program. We shall prove that the two algorithms areterminating (when applied to well-typed programs) and alsototally-correct (preserving non-strict semantics).