基于图理论的图像处理与物体识别算法的研究
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摘要
因为图像获取可能丢失部分信息,图像底层处理算法无法得到明确的物体的区域,物体的特征描述和物体的表达无法清晰的界定各种环境中的同一物体,物体识别仍旧是计算机视觉系统的一个难题。因此针对物体识别进行研究的开展有重要的理论意义和实践意义。本文针对物体识别的多个阶段进行了研究,从物体的获取,图像底层处理到物体识别分别提出和改进了一些基于图理论的算法,对于提高最终的物体识别能力起到了积极的作用。
在多聚焦图像融合方面,提出了基于代数多重网格的自适应分块的图像融合算法。因为通过代数多重网格方法提取出来的粗网格能在一定程度上提取图像的细节信息,因此可以根据粗网格的数据重建原始图像。实验证明该方法有较好的重建效果。本文从理论上进行了一些分析,也进一步验证了利用代数多重网格方法提取图的强连接子图的有效性。
将粗网格重建效果和原始图像的均方差分析发现,当图像结构较为清晰时,重建效果和原始图像的均方差较大,而当图像结构较为模糊时,重建效果和原始图像的均方差较小,因此使用该特性来指导多聚焦图像的融合。融合结果表明,该方法能得到较好的融合结果。通过一些主观和客观的比较,可以验证本算法的优越性。
针对图像的底层处理,提出了基于K均值的中值滤波算法和基于递归的K均值的中值滤波算法。这一方法可以解决标准中值滤波算法的一些错误的结果,同时从另一方面对耗时较多的K均值方法进行了优化,大大减少了处理时间。针对基于特征值求解图分类的方法中,利用代数多重网格和图分类方法之间的联系,提出了一种利用拉普拉斯矩阵的特征值建立代数多重网格中粗网格的方法,并从理论上分析和从实践上证明了特征值选取的原则和有效性,其结果符合粗网格选取的两个基本原则。
针对基于能量函数的图分类方法,对能量函数进行了研究,建立了等效于区域生长和K均值等方法的等效能量函数。对于具体的图像分割问题,提出了多种结合图分类方法的特征,如小波算子,分数阶微分算子和代数多重网格算子。这些算子对于某类型的图片能够得到更为精细的结构和纹理特征。本文还将图分类方法和OTSU方法结合来进行处理,能够提取更为准确的轮廓特征。
使用代数多重网格中提取的粗网格重建的结果进行特征表达,可以有效地提高特征的对比度,提高物体识别的能力。从人脸识别到多组物体识别,分别进行了一些实践研究。结果证明根据粗网格提取的特征能显著提高特征的对比度和物体识别率。在对单种物体的物体识别中,通过支持向量机方法学习和训练,结合适当的图像特征和图像描述方法能够得到很好的识别结果。在针对多组物体识别中,提出了一种结合区域分割和图理论的“词袋”表达方式和基于能量函数的求解方法。
Object recognition is a challenge subject in the field of computer vision. The challenges lie in three processes of the object recognition system: image acquisition, image low-level processing, and object recognition. It is not uncommon that critical information can be lost during the image acquisition process. During the subsequent image low-level processing, clear regions of an object in an image may not be obtained. Lastly, it is challenging to recognize generic objects in images taken under different imaging conditions based on characteristics descriptions. Therefore, the algorithms relative to image acquisition, image processing, and object recognition warrant further study and this paper presents a series of algorithms to enhance the capability of the object recognition system.
To acquire clear images without critical information loss, an adaptive image fusion algorithm based on algebraic multigrid method is proposed. The algorithm helps to extract coarse-level details for the reconstruction of the original images. Our experiments have showed that the algorithm is promising in image reconstruction and theoretical analysis is performed in this study to further substantiate the effectiveness of the algebraic multigrid method in extracting the strongly connected subgraphs. The mean square error (MSE) between the reconstructed image and the original image is analyzed. When the original image is clear, the MSE is large and when the image is blur, the MSE is small. This principle is used in this study to guide the fusion of multi-focus images. The fusion results show that this method results in better results over other methods based on subjective and objective evaluation criteria.
To obtain the information of the shapes and regions, low-level processing including image denoising and image segmentation is performed. K-means median filtering algorithm and recursive K-means median filtering algorithm are proposed to denoise the image. These approaches can eliminate some errors caused by the standard median filtering algorithm and greatly optimize the time-consuming K-means algorithm. The relationship between the graph cut method and the algebraic multigrid method is analyzed and a method to extract the coarse level details is proposed based on the eigenvalues of the laplacian matrix of the adjacency graph. The results are consistent with two basic principles of coarse grid selection. A new concept of "wavelet transformation of the graph" is proposed to extend the application of the graph in image processing.
For the graph classification methods based on the energy function, we have established an energy function equivalent to energy functions based on regional growth method, K-means method and other methods. For the specific image segmentation, a variety of features combined with normalized cut, such as wavelet operator, fractional differential operator, and algebraic multi-grid operator are presented. These operators are capable of obtaining finer structure and texture features. The OTSU method is combined with normalized cut method in this study to get more accurate contour feature.
In the recognition of single kind of object, promising results can be achieved after learning and training through the support vector machine method combined with appropriate image features and characteristics descriptions. In the recognition of different kind of objects, an adaptive "word bag" image expression combined with region segmentation and graph theory is proposed. According to the relationships of regional characteristics and some other characteristics, the prominent characteristics are strengthened and others are inhibited.
在多聚焦图像融合方面,提出了基于代数多重网格的自适应分块的图像融合算法。因为通过代数多重网格方法提取出来的粗网格能在一定程度上提取图像的细节信息,因此可以根据粗网格的数据重建原始图像。实验证明该方法有较好的重建效果。本文从理论上进行了一些分析,也进一步验证了利用代数多重网格方法提取图的强连接子图的有效性。
将粗网格重建效果和原始图像的均方差分析发现,当图像结构较为清晰时,重建效果和原始图像的均方差较大,而当图像结构较为模糊时,重建效果和原始图像的均方差较小,因此使用该特性来指导多聚焦图像的融合。融合结果表明,该方法能得到较好的融合结果。通过一些主观和客观的比较,可以验证本算法的优越性。
针对图像的底层处理,提出了基于K均值的中值滤波算法和基于递归的K均值的中值滤波算法。这一方法可以解决标准中值滤波算法的一些错误的结果,同时从另一方面对耗时较多的K均值方法进行了优化,大大减少了处理时间。针对基于特征值求解图分类的方法中,利用代数多重网格和图分类方法之间的联系,提出了一种利用拉普拉斯矩阵的特征值建立代数多重网格中粗网格的方法,并从理论上分析和从实践上证明了特征值选取的原则和有效性,其结果符合粗网格选取的两个基本原则。
针对基于能量函数的图分类方法,对能量函数进行了研究,建立了等效于区域生长和K均值等方法的等效能量函数。对于具体的图像分割问题,提出了多种结合图分类方法的特征,如小波算子,分数阶微分算子和代数多重网格算子。这些算子对于某类型的图片能够得到更为精细的结构和纹理特征。本文还将图分类方法和OTSU方法结合来进行处理,能够提取更为准确的轮廓特征。
使用代数多重网格中提取的粗网格重建的结果进行特征表达,可以有效地提高特征的对比度,提高物体识别的能力。从人脸识别到多组物体识别,分别进行了一些实践研究。结果证明根据粗网格提取的特征能显著提高特征的对比度和物体识别率。在对单种物体的物体识别中,通过支持向量机方法学习和训练,结合适当的图像特征和图像描述方法能够得到很好的识别结果。在针对多组物体识别中,提出了一种结合区域分割和图理论的“词袋”表达方式和基于能量函数的求解方法。
Object recognition is a challenge subject in the field of computer vision. The challenges lie in three processes of the object recognition system: image acquisition, image low-level processing, and object recognition. It is not uncommon that critical information can be lost during the image acquisition process. During the subsequent image low-level processing, clear regions of an object in an image may not be obtained. Lastly, it is challenging to recognize generic objects in images taken under different imaging conditions based on characteristics descriptions. Therefore, the algorithms relative to image acquisition, image processing, and object recognition warrant further study and this paper presents a series of algorithms to enhance the capability of the object recognition system.
To acquire clear images without critical information loss, an adaptive image fusion algorithm based on algebraic multigrid method is proposed. The algorithm helps to extract coarse-level details for the reconstruction of the original images. Our experiments have showed that the algorithm is promising in image reconstruction and theoretical analysis is performed in this study to further substantiate the effectiveness of the algebraic multigrid method in extracting the strongly connected subgraphs. The mean square error (MSE) between the reconstructed image and the original image is analyzed. When the original image is clear, the MSE is large and when the image is blur, the MSE is small. This principle is used in this study to guide the fusion of multi-focus images. The fusion results show that this method results in better results over other methods based on subjective and objective evaluation criteria.
To obtain the information of the shapes and regions, low-level processing including image denoising and image segmentation is performed. K-means median filtering algorithm and recursive K-means median filtering algorithm are proposed to denoise the image. These approaches can eliminate some errors caused by the standard median filtering algorithm and greatly optimize the time-consuming K-means algorithm. The relationship between the graph cut method and the algebraic multigrid method is analyzed and a method to extract the coarse level details is proposed based on the eigenvalues of the laplacian matrix of the adjacency graph. The results are consistent with two basic principles of coarse grid selection. A new concept of "wavelet transformation of the graph" is proposed to extend the application of the graph in image processing.
For the graph classification methods based on the energy function, we have established an energy function equivalent to energy functions based on regional growth method, K-means method and other methods. For the specific image segmentation, a variety of features combined with normalized cut, such as wavelet operator, fractional differential operator, and algebraic multi-grid operator are presented. These operators are capable of obtaining finer structure and texture features. The OTSU method is combined with normalized cut method in this study to get more accurate contour feature.
In the recognition of single kind of object, promising results can be achieved after learning and training through the support vector machine method combined with appropriate image features and characteristics descriptions. In the recognition of different kind of objects, an adaptive "word bag" image expression combined with region segmentation and graph theory is proposed. According to the relationships of regional characteristics and some other characteristics, the prominent characteristics are strengthened and others are inhibited.
引文
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