针对简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)算法对不同图像自适应性差的问题,提出了一种基于皮尔森相关系数的自适应SLIC超像素图像分割算法。首先,通过量化非间隔进行图像预处理,并计算颜色熵作为图像复杂度,从...针对简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)算法对不同图像自适应性差的问题,提出了一种基于皮尔森相关系数的自适应SLIC超像素图像分割算法。首先,通过量化非间隔进行图像预处理,并计算颜色熵作为图像复杂度,从而确定所需分割的超像素个数。其次,利用皮尔森相关系数作为相似性度量函数。最后,通过纹理特征对类内异常点进行滤除,确保种子点更新的准确性。实验结果表明,在超像素个数相同的情况下,基于皮尔森相关系数的自适应SLIC超像素图像分割算法相比主流超像素分割算法,可以获得更高的边缘命中率以及更低的欠分割率,性能优于LSC(Linear Spectral Clustering)、SLIC和SLIC0(Simple Linear Iterative Clustering Zero)算法。展开更多
传统人工智能图像超像素分割方法的像素更新簇中心不符合预设阈值,导致方法运算复杂度较高,耗时较长。为解决上述问题,提出基于快速简单线性迭代聚类(Simple Linear Iterative Clustering, SLIC)的人工智能图像超像素的快速分割方法。...传统人工智能图像超像素分割方法的像素更新簇中心不符合预设阈值,导致方法运算复杂度较高,耗时较长。为解决上述问题,提出基于快速简单线性迭代聚类(Simple Linear Iterative Clustering, SLIC)的人工智能图像超像素的快速分割方法。根据簇中心与像素间关系特征,划分全部像素点为不同窗格。标记像素点标签,更新簇中心后,迭代聚类至符合预设阈值条件。基于原始分割图像,区分背景部分与目标部分,衡量不同图像部分相似度,自适应合并超像素的相似部分。仿真中,利用Proteus软件模拟超像素分割,经对比视觉效果与指标评估结果。实验结果表明,所提方法可准确分割低色差的无噪图像区域,有效抑制各类噪声影响,大幅提升分割速度,具有较好的分割精准性与噪声免疫性,且能够满足用户实时性需求。展开更多
To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. ...To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.展开更多
文摘针对简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)算法对不同图像自适应性差的问题,提出了一种基于皮尔森相关系数的自适应SLIC超像素图像分割算法。首先,通过量化非间隔进行图像预处理,并计算颜色熵作为图像复杂度,从而确定所需分割的超像素个数。其次,利用皮尔森相关系数作为相似性度量函数。最后,通过纹理特征对类内异常点进行滤除,确保种子点更新的准确性。实验结果表明,在超像素个数相同的情况下,基于皮尔森相关系数的自适应SLIC超像素图像分割算法相比主流超像素分割算法,可以获得更高的边缘命中率以及更低的欠分割率,性能优于LSC(Linear Spectral Clustering)、SLIC和SLIC0(Simple Linear Iterative Clustering Zero)算法。
文摘传统人工智能图像超像素分割方法的像素更新簇中心不符合预设阈值,导致方法运算复杂度较高,耗时较长。为解决上述问题,提出基于快速简单线性迭代聚类(Simple Linear Iterative Clustering, SLIC)的人工智能图像超像素的快速分割方法。根据簇中心与像素间关系特征,划分全部像素点为不同窗格。标记像素点标签,更新簇中心后,迭代聚类至符合预设阈值条件。基于原始分割图像,区分背景部分与目标部分,衡量不同图像部分相似度,自适应合并超像素的相似部分。仿真中,利用Proteus软件模拟超像素分割,经对比视觉效果与指标评估结果。实验结果表明,所提方法可准确分割低色差的无噪图像区域,有效抑制各类噪声影响,大幅提升分割速度,具有较好的分割精准性与噪声免疫性,且能够满足用户实时性需求。
基金The National Natural Science Foundation of China(No.81272501)the National Basic Research Program of China(973Program)(No.2011CB707904)Taishan Scholars Program of Shandong Province,China(No.ts20120505)
文摘To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.
