图像线结构提取与区域分割方法研究
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摘要
图像中的线结构通常是指用来定义目标形状的轮廓或划分区域的边界,它们为图像的分析与理解提供了简洁可靠的形状特征表达。另一种与线结构提取密切相关的技术是图像的区域分割,它是指将图像中具有不同统计特性的区域分离开的技术。一方面,它不仅可以提供由区域的轮廓或边界构成的线结构信息,另一方面,由于在分割得到的每个区域内,像素具有相似的统计特性,因此,区域可以被近似地看成为一个整体,通过对每个区域进行特征提取,还可以为图像的分析和理解提供一种简洁可靠的区域特征表达。
目前,在图像处理与计算视觉领域,对线结构提取与区域分割技术的研究虽然取得了长足的进步,但二者仍然没有彻底的解决方案,依然是两个开放的课题。由于二者均可以为高层视觉的处理,如目标识别和场景分类等,提供重要的特征表达,所以一直都是具有重要意义的研究课题。
对于线结构提取的研究,本文采用小的直线段作为线结构的组成单元,在标记点过程框架下,对图像中的线结构建立了数学建模。采用该模型的优势在于,在对模型求解的过程中可同时完成线结构组成单元的检测和空间感知聚集。通过对合成图像与真实图像进行试验,结果显示本文方法不但能够有效提取出图像中显著的线结构,同时还能抑制那些由纹理或复杂背景产生的干扰边缘。
对于区域分割的研究,本文将其视为对像素的特征向量进行聚类的问题,为此提出了一种鲁棒性良好的特征空间聚类算法,与经典聚类算法相比,当特征空间中数据的分布较为复杂时,由本文聚类算法得到的结果能够更好的保持数据原有的类属关系。由于本文聚类算法的鲁棒性,我们只需在L*a*b*颜色空间上完成对像素的聚类,即可得到满意的图像区域分割结果。此外,对于每幅图像,为了得到满意的分割结果,如何自动选取可调参数的取值,一直是各图像分割方法在实际应用中的一个难题。本文基于最小描述长度的理论,对如何自动选取可调参数的取值提出了一个解决方案。最后,为了验证本文区域分割方法的有效性,实验在Berkeley图像分割数据库上完成,并与现有的几种主流图像分割方法进行了比较。
对区域分割结果进行定量地评价同样具有重要意义,在计算视觉领域,目前仍然没有一个标准的定量评价准则。本文提出了一种新的图像分割结果评价方法。通过定义多个手工标注的分割结果之间在像素上的感知一致程度,赋予每个像素不同的权重。感知一致程度越高,像素的对应权重就越高,最后计算出加权后的Jaccard指数,作为待评价分割结果的最终评价指数。实验显示,本文提出的评价指数更能反映出人类视觉感知对图像分割的理解。
对图像中的显著区域进行分割对于目标识别、基于内容的图像提取等高层视觉应用具有重要的意义。对每个像素的显著程度,本文提出了一种简单有效的计算模型。利用计算得到的显著程度分布图,通过滞后门限阈值方法,有效的对自然图像的显著区域进行了分割。通过在包含1000幅图像的公开数据库上进行实验,定量地评价了本文显著区域分割的结果。
最后,本文对所做的工作进行了归纳总结,并且结合本文的不足之处,分析和讨论了进一步的研究计划。
In images, linear structures usually refers to the contours used to define the target shape or the boundaries used to demarcate the regions, they provide image analysis and understanding a simple and reliable expression of the shape characteristics. Another technology which is closely related with extraction of linear structures is segmentation of regions, which refers to the seperation of those regions have different statistical properties in image. On the one hand, it can provide not only the structural information by the contours or boundaries constitute the regions, on the other hand, as in each segmented region, pixels hold the similar statistical characteristics, thus each region can be approximately viewed as a whole, through the feature extraction for each region, it also provides image analysis and understanding a simple and reliable expression of the regional characteristics.
At present, in the fields of image processing and computational vision, researches on extraction of linear structures and segmentation of regions though have made significant progress, yet both still have no thorough solutions, both are still open topics. As both can provide the high-level visual processing, such as object recognition and scene classification, with important features of expression, they are always two important research topics.
For extraction of linear structures, this thesis uses small straight-line segment as the compositional token, and models the linear structures under the framework of Marked Point Process. The advantage of this model is that we can simultaneously complete the detection and spatial grouping of tokens during the optimization. Experiments on both synthetic and real images showed that our method can capture most of the salient structures, while reduce largely the distracting edge elements due to texture or cluttered background.
For segmentation of regions, this thesis casts it as the problem of clustering feature vectors of pixels. Specially, a robust clustering algorithm for feature space is proposed. Experiments show that our clustering algorithm could obtain more coherent results at the situation when the distribution of data is complicated, than some classical clustering algorithms. Due to the robustness of our clustering algorithm, only cluster the pixels in the L*a*b* color space, we can obtain the satisfactory segmentation result. In addition, for each image, in order to obtain a satisfactory segmentation results, how to automatically set the value of tuning parameters, is always a problem for many image segmentation methods in practical applications. Based on the theory of Minimum Description Length, this problem is put forward a solution. To verify the validity of our region segmentation method, experiments are performed on the Berkeley segmentation database, and the quantitative comparison with state-of-the-art segmentation methods is reported.
Quantitatively evaluating the performance of segmentation is also important in the field of computational vision, however, there is still no standard performance measure. This thesis presents a new evaluation method for image segmentation results. For each pixel, we give it a weight by defining its degree of perceptual consistency among the hand-labeled segmentation ground-truths. The higher the degree of perceptual consistency, the higher the weight of corresponding pixel, our final evaluation index is calculated based on Jaccard Index with the weight map. Experiments show that, our evaluation index could better reflect the understanding of human visual perception on image segmentation from the ground-truth.
Extraction of salient regions from image is important for many high-level vision applications, such as object recognition, content-based image retrieval. We propose a simple and effective model to compute the saliency for pixels in image. With the resulted saliency map for image, salient regions are extracted by using hysteresis thresholding. At last, we evaluate our method on a database which contains 1000 pairs of image and groundtruth.
At last, we summarize the presented work. According to the imperfect aspects, we analyze and discuss the future work.
目前,在图像处理与计算视觉领域,对线结构提取与区域分割技术的研究虽然取得了长足的进步,但二者仍然没有彻底的解决方案,依然是两个开放的课题。由于二者均可以为高层视觉的处理,如目标识别和场景分类等,提供重要的特征表达,所以一直都是具有重要意义的研究课题。
对于线结构提取的研究,本文采用小的直线段作为线结构的组成单元,在标记点过程框架下,对图像中的线结构建立了数学建模。采用该模型的优势在于,在对模型求解的过程中可同时完成线结构组成单元的检测和空间感知聚集。通过对合成图像与真实图像进行试验,结果显示本文方法不但能够有效提取出图像中显著的线结构,同时还能抑制那些由纹理或复杂背景产生的干扰边缘。
对于区域分割的研究,本文将其视为对像素的特征向量进行聚类的问题,为此提出了一种鲁棒性良好的特征空间聚类算法,与经典聚类算法相比,当特征空间中数据的分布较为复杂时,由本文聚类算法得到的结果能够更好的保持数据原有的类属关系。由于本文聚类算法的鲁棒性,我们只需在L*a*b*颜色空间上完成对像素的聚类,即可得到满意的图像区域分割结果。此外,对于每幅图像,为了得到满意的分割结果,如何自动选取可调参数的取值,一直是各图像分割方法在实际应用中的一个难题。本文基于最小描述长度的理论,对如何自动选取可调参数的取值提出了一个解决方案。最后,为了验证本文区域分割方法的有效性,实验在Berkeley图像分割数据库上完成,并与现有的几种主流图像分割方法进行了比较。
对区域分割结果进行定量地评价同样具有重要意义,在计算视觉领域,目前仍然没有一个标准的定量评价准则。本文提出了一种新的图像分割结果评价方法。通过定义多个手工标注的分割结果之间在像素上的感知一致程度,赋予每个像素不同的权重。感知一致程度越高,像素的对应权重就越高,最后计算出加权后的Jaccard指数,作为待评价分割结果的最终评价指数。实验显示,本文提出的评价指数更能反映出人类视觉感知对图像分割的理解。
对图像中的显著区域进行分割对于目标识别、基于内容的图像提取等高层视觉应用具有重要的意义。对每个像素的显著程度,本文提出了一种简单有效的计算模型。利用计算得到的显著程度分布图,通过滞后门限阈值方法,有效的对自然图像的显著区域进行了分割。通过在包含1000幅图像的公开数据库上进行实验,定量地评价了本文显著区域分割的结果。
最后,本文对所做的工作进行了归纳总结,并且结合本文的不足之处,分析和讨论了进一步的研究计划。
In images, linear structures usually refers to the contours used to define the target shape or the boundaries used to demarcate the regions, they provide image analysis and understanding a simple and reliable expression of the shape characteristics. Another technology which is closely related with extraction of linear structures is segmentation of regions, which refers to the seperation of those regions have different statistical properties in image. On the one hand, it can provide not only the structural information by the contours or boundaries constitute the regions, on the other hand, as in each segmented region, pixels hold the similar statistical characteristics, thus each region can be approximately viewed as a whole, through the feature extraction for each region, it also provides image analysis and understanding a simple and reliable expression of the regional characteristics.
At present, in the fields of image processing and computational vision, researches on extraction of linear structures and segmentation of regions though have made significant progress, yet both still have no thorough solutions, both are still open topics. As both can provide the high-level visual processing, such as object recognition and scene classification, with important features of expression, they are always two important research topics.
For extraction of linear structures, this thesis uses small straight-line segment as the compositional token, and models the linear structures under the framework of Marked Point Process. The advantage of this model is that we can simultaneously complete the detection and spatial grouping of tokens during the optimization. Experiments on both synthetic and real images showed that our method can capture most of the salient structures, while reduce largely the distracting edge elements due to texture or cluttered background.
For segmentation of regions, this thesis casts it as the problem of clustering feature vectors of pixels. Specially, a robust clustering algorithm for feature space is proposed. Experiments show that our clustering algorithm could obtain more coherent results at the situation when the distribution of data is complicated, than some classical clustering algorithms. Due to the robustness of our clustering algorithm, only cluster the pixels in the L*a*b* color space, we can obtain the satisfactory segmentation result. In addition, for each image, in order to obtain a satisfactory segmentation results, how to automatically set the value of tuning parameters, is always a problem for many image segmentation methods in practical applications. Based on the theory of Minimum Description Length, this problem is put forward a solution. To verify the validity of our region segmentation method, experiments are performed on the Berkeley segmentation database, and the quantitative comparison with state-of-the-art segmentation methods is reported.
Quantitatively evaluating the performance of segmentation is also important in the field of computational vision, however, there is still no standard performance measure. This thesis presents a new evaluation method for image segmentation results. For each pixel, we give it a weight by defining its degree of perceptual consistency among the hand-labeled segmentation ground-truths. The higher the degree of perceptual consistency, the higher the weight of corresponding pixel, our final evaluation index is calculated based on Jaccard Index with the weight map. Experiments show that, our evaluation index could better reflect the understanding of human visual perception on image segmentation from the ground-truth.
Extraction of salient regions from image is important for many high-level vision applications, such as object recognition, content-based image retrieval. We propose a simple and effective model to compute the saliency for pixels in image. With the resulted saliency map for image, salient regions are extracted by using hysteresis thresholding. At last, we evaluate our method on a database which contains 1000 pairs of image and groundtruth.
At last, we summarize the presented work. According to the imperfect aspects, we analyze and discuss the future work.
引文
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