基于斜面分解的非对称逆布局图像表示方法与处理算法研究
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摘要
图像表示是图像数据在计算机中的数字化表示和存储方式，与图像处理一起组成图像模型。图像表示方法对图像处理算法的性能起到了至关重要的作用。选择不同的图像表示方法在很大程度上就决定了图像处理算法的效率。基于非对称逆布局模式表示模型，以改进图像处理算法的效率为目的，提出一种新的图像表示方法——基于斜面分解的非对称逆布局图像表示方法(IDNAM)。IDNAM使用一系列矩形斜面子模式的实例来表示图像。基于失真IDNAM模型和无失真IDNAM模型，分别提出了一种灰度图像的表示方法和一种二值图像的表示方法。在灰度图像IDNAM表示方法中，提出了一种矩形区域的同质性判定条件，来限制在逆布局过程中引入的失真。在IDNAM表示的基础上，提出了一种抽象图像处理算法。该算法概括了基于IDNAM表示的图像处理算法的基本思想，是一个具有指导性的抽象算法。根据算法的时间复杂度分析，当矩形斜面子模式的数目小于像素的数目时，该算法较基于点阵的抽象图像处理算法更快。实验结果表明矩形斜面子模式的数目远小于小于像素的数目，且灰度图像IDNAM表示方法不仅能根据图像的内容自适应地逆布局出不同大小的矩形斜面子模式，而且保持了良好的保真度。
积分投影变换是图像分析中的一种基本方法，在人脸识别、直线检测和倾斜估计等多种领域中有着广泛的应用。传统的积分投影变换算法都是基于点阵表示的，直接根据积分投影的定义逐像素计算的。在二值图像IDNAM表示方法的基础之上，研究矩形斜面子模式的积分投影向量的计算方法、以及图像的积分投影向量与矩形斜面子模式的积分投影向量之间的关系，提出直接根据矩形斜面子模式计算图像的积分投影向量的算法。理论分析和实验结果均表明，在任何投影方向上基于IDNAM表示的积分投影变换算法较传统算法在处理速度方面更具优势。
边缘检测主要是对图像中像素灰度值的不连续性的度量、检测和定位，是图像分析领域中的一个基础问题。边缘检测作为一个预处理过程，已经广泛地应用于图像分割、模式识别和运动分析等领域中。传统的边缘检测算法都是根据像素之间的不连续性来提取边缘信息。在灰度图像IDNAM表示方法的基础之上，提出了矩形斜面子模式的理想边缘模型，将矩形斜面子模式内的边缘分成五种：中心边缘模型、左边界边缘模型、上边界边缘模型、右边界边缘模型、下边界边缘模型，并给出了每一种边缘模型的边缘强度和边缘方向的计算公式。为了消除噪声对边缘检测结果的干扰，提出了矩形斜面子模式内边缘存在性判定条件。根据该判定条件，可以直接根据理想边缘模型的参数判断矩形斜面子模式内是否存在边缘信息，并对存在边缘信息的矩形斜面子模式直接给出其边缘参数。理论分析和实验结果均表明，基于IDNAM表示的边缘检测算法的处理速度较传统的边缘检测算法更快。
图像分割的目的是将一幅图像划分成若干具有相似特征的区域。图像分割是对图像进行进一步分析、理解和识别的基础，在多种图像分析和机器视觉应用中都是常用的技术。基于灰度图像IDNAM表示方法，提出了一种图像分割算法。该算法根据矩形斜面子模式内的边缘信息以及矩形斜面子模式之间的同质性，将矩形斜面子模式聚合成若干个区域，来实现图像分割。理论分析和实验结果均表明，基于IDNAM表示的图像分割算法所需的执行时间远小于分水岭算法。
研究结果表明，IDNAM图像表示方法能有效地加快图像处理运算的速度。
An image system model consists of two parts: image representation and imageprocessing. Image representation is the manner in which images are described and storedin the computer. Image representation plays a key role in optimizing image processingalgorithms. Selection of different image representation methods always determines theefficiency of image processing algorithms to a great extent. Based on the Non-symmetryAnti-packing pattern representation Model (NAM), a new image representation method ispresented, which is called Inclined plane Decomposition based Non-symmetry andAnti-packing image representation Method (IDNAM), in order to improve the efficiencyof image processing algorithms. A series of instances of rectangular inclined planesub-patterns are used to represent an image in IDNAM. A grey scale image representationmethod is proposed based on the distorted IDNAM. A homogeneity determination rule fora rectangular region of a grey scale image is introduced to limit the distortion caused byanti-packing in IDNAM. Experimental results show that the IDNAM representation forgrey scale images can not only extract rectangular inclined plane sub-patterns withdifferent sizes according to the image contents, but also keep a high fidelity. A binaryimage representation method is proposed based on the undistorted IDNAM. An algorithmis given for an abstract image processing based on IDNAM, and the algorithm sums up theentire idea of image processing algorithms based on IDNAM. It is a general, abstract andguide algorithm. The theoretical analyses show that the time complexity of this algorithmis smaller than that of the abstract image processing algorithms based on pixelrepresentation, when rectangular inclined plane sub-patterns are less than pixels in animage.
Integral projection is a basic method in image analysis, which is widely used inseveral domains such as face recognition, line detection and skew detection etc. Thetraditional integral projection algorithm is developed using the pixel representation. Bystudying the method of computing the integral projection vector of a rectangular inclinedplane sub-pattern and the relationship between the integral projection vectors of arectangular inclined plane sub-pattern and an image, a fast integral projection algorithm isproposed, which achieves computing the integral projection vectorof an image fromrectangular inclined plane sub-patterns directly. The theoretical analyses and experimentalresults show that significant improvement is obtained with the algorithm using theIDNAM representation over the pixel representation along all projection directions.
Edge detection, which measures, checks and captures discontinuity in gray levelvalues in an image, is a basic problem in image analysis. Edge detection, as an initial step,is widely used in several domains such as image segmentation, pattern recognition andmotion detection etc. Classical edge detection algorithms are based on pixel representation,which extract edge information from pixels. On the basis of the IDNAM representation forgrey scale images, an ideal edge model within a rectangular inclined plane sub-pattern ispresented. In the model, edges within rectangular inclined plane sub-patterns are dividedinto five types: central edge model, left border edge model, top border edge model, rightborder edge model and bottom border edge model. The methods for computing the edgestrength and direction of the five edge models are presented. The existence of an edgewithin a rectangular inclined plane sub-pattern should be determined according to theparameters of the five edge models, to separate between edge and noise. The theoreticalanalyses and experimental results show that the edge detection algorithm using IDNAMperforms faster than the classical ones because it permits the execution of operations onsub-patterns instead of pixels.
Segmentation of an image entails the division or separation of the image into regionswith similar attribute. Image segmentation is a common technology in many applicationsof image analysis and computer vision. A fast image segmentation algorithm is proposedbased on IDNAM. According to the edge strength within rectangular inclined planesub-patterns and the homogeneity between them, the algorithm groups the rectangularinclined plane sub-patterns into different regions, to achieve the segmentation. Thetheoretical analyses and experimental results show that the image segmentation methodusing IDNAM performs faster than the watershed algorithm.
The theoretical analyses and experimental results show that the IDNAM imagerepresentation method can speedup image processing effectively.
积分投影变换是图像分析中的一种基本方法，在人脸识别、直线检测和倾斜估计等多种领域中有着广泛的应用。传统的积分投影变换算法都是基于点阵表示的，直接根据积分投影的定义逐像素计算的。在二值图像IDNAM表示方法的基础之上，研究矩形斜面子模式的积分投影向量的计算方法、以及图像的积分投影向量与矩形斜面子模式的积分投影向量之间的关系，提出直接根据矩形斜面子模式计算图像的积分投影向量的算法。理论分析和实验结果均表明，在任何投影方向上基于IDNAM表示的积分投影变换算法较传统算法在处理速度方面更具优势。
边缘检测主要是对图像中像素灰度值的不连续性的度量、检测和定位，是图像分析领域中的一个基础问题。边缘检测作为一个预处理过程，已经广泛地应用于图像分割、模式识别和运动分析等领域中。传统的边缘检测算法都是根据像素之间的不连续性来提取边缘信息。在灰度图像IDNAM表示方法的基础之上，提出了矩形斜面子模式的理想边缘模型，将矩形斜面子模式内的边缘分成五种：中心边缘模型、左边界边缘模型、上边界边缘模型、右边界边缘模型、下边界边缘模型，并给出了每一种边缘模型的边缘强度和边缘方向的计算公式。为了消除噪声对边缘检测结果的干扰，提出了矩形斜面子模式内边缘存在性判定条件。根据该判定条件，可以直接根据理想边缘模型的参数判断矩形斜面子模式内是否存在边缘信息，并对存在边缘信息的矩形斜面子模式直接给出其边缘参数。理论分析和实验结果均表明，基于IDNAM表示的边缘检测算法的处理速度较传统的边缘检测算法更快。
图像分割的目的是将一幅图像划分成若干具有相似特征的区域。图像分割是对图像进行进一步分析、理解和识别的基础，在多种图像分析和机器视觉应用中都是常用的技术。基于灰度图像IDNAM表示方法，提出了一种图像分割算法。该算法根据矩形斜面子模式内的边缘信息以及矩形斜面子模式之间的同质性，将矩形斜面子模式聚合成若干个区域，来实现图像分割。理论分析和实验结果均表明，基于IDNAM表示的图像分割算法所需的执行时间远小于分水岭算法。
研究结果表明，IDNAM图像表示方法能有效地加快图像处理运算的速度。
An image system model consists of two parts: image representation and imageprocessing. Image representation is the manner in which images are described and storedin the computer. Image representation plays a key role in optimizing image processingalgorithms. Selection of different image representation methods always determines theefficiency of image processing algorithms to a great extent. Based on the Non-symmetryAnti-packing pattern representation Model (NAM), a new image representation method ispresented, which is called Inclined plane Decomposition based Non-symmetry andAnti-packing image representation Method (IDNAM), in order to improve the efficiencyof image processing algorithms. A series of instances of rectangular inclined planesub-patterns are used to represent an image in IDNAM. A grey scale image representationmethod is proposed based on the distorted IDNAM. A homogeneity determination rule fora rectangular region of a grey scale image is introduced to limit the distortion caused byanti-packing in IDNAM. Experimental results show that the IDNAM representation forgrey scale images can not only extract rectangular inclined plane sub-patterns withdifferent sizes according to the image contents, but also keep a high fidelity. A binaryimage representation method is proposed based on the undistorted IDNAM. An algorithmis given for an abstract image processing based on IDNAM, and the algorithm sums up theentire idea of image processing algorithms based on IDNAM. It is a general, abstract andguide algorithm. The theoretical analyses show that the time complexity of this algorithmis smaller than that of the abstract image processing algorithms based on pixelrepresentation, when rectangular inclined plane sub-patterns are less than pixels in animage.
Integral projection is a basic method in image analysis, which is widely used inseveral domains such as face recognition, line detection and skew detection etc. Thetraditional integral projection algorithm is developed using the pixel representation. Bystudying the method of computing the integral projection vector of a rectangular inclinedplane sub-pattern and the relationship between the integral projection vectors of arectangular inclined plane sub-pattern and an image, a fast integral projection algorithm isproposed, which achieves computing the integral projection vectorof an image fromrectangular inclined plane sub-patterns directly. The theoretical analyses and experimentalresults show that significant improvement is obtained with the algorithm using theIDNAM representation over the pixel representation along all projection directions.
Edge detection, which measures, checks and captures discontinuity in gray levelvalues in an image, is a basic problem in image analysis. Edge detection, as an initial step,is widely used in several domains such as image segmentation, pattern recognition andmotion detection etc. Classical edge detection algorithms are based on pixel representation,which extract edge information from pixels. On the basis of the IDNAM representation forgrey scale images, an ideal edge model within a rectangular inclined plane sub-pattern ispresented. In the model, edges within rectangular inclined plane sub-patterns are dividedinto five types: central edge model, left border edge model, top border edge model, rightborder edge model and bottom border edge model. The methods for computing the edgestrength and direction of the five edge models are presented. The existence of an edgewithin a rectangular inclined plane sub-pattern should be determined according to theparameters of the five edge models, to separate between edge and noise. The theoreticalanalyses and experimental results show that the edge detection algorithm using IDNAMperforms faster than the classical ones because it permits the execution of operations onsub-patterns instead of pixels.
Segmentation of an image entails the division or separation of the image into regionswith similar attribute. Image segmentation is a common technology in many applicationsof image analysis and computer vision. A fast image segmentation algorithm is proposedbased on IDNAM. According to the edge strength within rectangular inclined planesub-patterns and the homogeneity between them, the algorithm groups the rectangularinclined plane sub-patterns into different regions, to achieve the segmentation. Thetheoretical analyses and experimental results show that the image segmentation methodusing IDNAM performs faster than the watershed algorithm.
The theoretical analyses and experimental results show that the IDNAM imagerepresentation method can speedup image processing effectively.
引文
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