Superpixels generation is becoming increasingly popular as a preprocessing in many computer vision applications. A superpixel is an image patch which has uniform pixels intensity and is aligned with intensity edges. S...Superpixels generation is becoming increasingly popular as a preprocessing in many computer vision applications. A superpixel is an image patch which has uniform pixels intensity and is aligned with intensity edges. Superpixels provide a convenient primitive from which local image features can be computed. So far, there are many methods to generate superpixels. Several main superpixels generation algorithms are summarized in this paper and the advantages and disadvantages of them are analyzed simply. In the end, some applications of superpixels are listed.展开更多
为了克服传统基于区域的图像分割方法对图像初始划分完全随机进而导致算法效率低下的缺点,本文提出了一种基于Delaunay划分并结合最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximization of the Posterior Marginal,MPM)...为了克服传统基于区域的图像分割方法对图像初始划分完全随机进而导致算法效率低下的缺点,本文提出了一种基于Delaunay划分并结合最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximization of the Posterior Marginal,MPM)算法的图像分割方法。该方法首先提取图像特征点,并把特征点集作为构建Delaunay三角网的基础点集。利用Delaunay三角网的构建将影像划分成众多彼此连接的超像素,并假设这些超像素内的像素灰度值服从同一独立的正态分布,基于此完成特征场模型的建立,再运用EM\MPM方法分别模拟特征场模型和分割影像。为了验证本文提出的算法能够有效地分割图像,分别对模拟图像和真实图像进行分割测试,并和经典的初始划分完全随机的超像素影像分割算法进行对比,测试结果定性和定量地表明了该方法的有效性和准确性。展开更多
Background:Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing.Traditionally,size uniformity is one of the significant features of superpixels.However,in medical image...Background:Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing.Traditionally,size uniformity is one of the significant features of superpixels.However,in medical images,in which subjects scale varies greatly and background areas are often flat,size uniformity rarely conforms to the varying content.To obtain the fewest superpixels with retaining important details,the size of superpixel should be chosen carefully.Methods:We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images,especially pathological images.A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content,that is smaller(larger)superpixels in color-riching areas(flat areas).Results:The proposed superpixel algorithm can generate superpixels with boundary adherence,insensitive to noise,and with extremely big sizes and extremely small sizes on one image.The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images.Conclusion:With the proposed algorithm,the choice of superpixel size is automatic,which frees the user from the predicament of setting suitable superpixel size for a given application.The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.展开更多
基金Supported by Independent Innovation Foundation of Shandong University,IIFSDU(No.2012TB013)Scientific Research Foundation of Shandong Province of Outstanding Young Scientist Award(No.BS2013DX041,No.BS2013DX048)+1 种基金Shandong Province Natural Fund(zr2011FM031)Ji'nan Science and Technology Development Project(No.201202015)
文摘Superpixels generation is becoming increasingly popular as a preprocessing in many computer vision applications. A superpixel is an image patch which has uniform pixels intensity and is aligned with intensity edges. Superpixels provide a convenient primitive from which local image features can be computed. So far, there are many methods to generate superpixels. Several main superpixels generation algorithms are summarized in this paper and the advantages and disadvantages of them are analyzed simply. In the end, some applications of superpixels are listed.
文摘为了克服传统基于区域的图像分割方法对图像初始划分完全随机进而导致算法效率低下的缺点,本文提出了一种基于Delaunay划分并结合最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximization of the Posterior Marginal,MPM)算法的图像分割方法。该方法首先提取图像特征点,并把特征点集作为构建Delaunay三角网的基础点集。利用Delaunay三角网的构建将影像划分成众多彼此连接的超像素,并假设这些超像素内的像素灰度值服从同一独立的正态分布,基于此完成特征场模型的建立,再运用EM\MPM方法分别模拟特征场模型和分割影像。为了验证本文提出的算法能够有效地分割图像,分别对模拟图像和真实图像进行分割测试,并和经典的初始划分完全随机的超像素影像分割算法进行对比,测试结果定性和定量地表明了该方法的有效性和准确性。
基金The work was supported by the NSFC-Zhejiang Joint Fund of the Integration of Informatization and Industrialization(No.U1909210)the National Natural Science Foundation of China(No.61772312)the Fundamental Research Funds of Shandong University(No.2018JC030).
文摘Background:Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing.Traditionally,size uniformity is one of the significant features of superpixels.However,in medical images,in which subjects scale varies greatly and background areas are often flat,size uniformity rarely conforms to the varying content.To obtain the fewest superpixels with retaining important details,the size of superpixel should be chosen carefully.Methods:We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images,especially pathological images.A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content,that is smaller(larger)superpixels in color-riching areas(flat areas).Results:The proposed superpixel algorithm can generate superpixels with boundary adherence,insensitive to noise,and with extremely big sizes and extremely small sizes on one image.The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images.Conclusion:With the proposed algorithm,the choice of superpixel size is automatic,which frees the user from the predicament of setting suitable superpixel size for a given application.The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.
