织物组织识别系统中关键技术的研究
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摘要
随着机织物CAD技术的发展，人们迫切需要提高织物仿样或改样设计的智能化，以适应多品种小批量的市场需求。如何利用计算机图像处理技术来分析织物的组织类型与结构参数，是计算机辅助织物分析与设计系统的关键所在。本文针对其中的关键环节开展研究工作，取得的研究成果包括：
在织物图像预处理的研究上，本文提出了一种基于Hough变换的图像倾斜纠正与纬纱密度识别算法。在织物图像录入过程中难免存在图像倾斜，需要对其进行调整。针对这一问题，本文提出了一种适用于织物图像的快速倾斜纠正算法。该算法在提取图像中的纬纱走向信息的基础上，运用层次Hough变换来检测倾斜角度，以达到要求的检测精度。利用倾斜检测结果，本文还提出了一种与倾斜无关的纬纱密度识别算法。该算法通过提取倾斜角处的投影轮廓线得到纬密排列规律，由此来计算纬纱密度。实验结果表明，上述算法具有较高的准确率。
在织物组织识别问题的研究上，本文提出了一种适用于纬二重组织的半自动识别算法。传统的组织分析算法多是针对灰度织物图像进行分析，且局限于单层组织识别。本算法以彩色图像为研究对象，将纬二重组织的识别问题分解为单层组织的识别与整合。在单层组织识别中，首先采用降色算法对经纬组织点进行预分割，然后根据实际织物中经纬纱线各自的分布特点给出了不同的分割方法，最后获得组织图。由于本算法着重考虑了非理想的经纬纱线分布状态，因此比传统识别算法具有更好的实用性。
在织物组织分割问题的研究上，本文首先提出了一种基于颜色和空间信息的分割算法。该算法利用颜色分布的均匀性度量来判断像素是否位于组织区域边界，再利用种子生长和区域合并策略将它们划分到不同组织区域中。针对该算法在边界定位上的不足，本文进一步提出了一种基于活动轮廓线模型的纹理分割算法。与传统分割算法相比，结合水平集方法的活动轮廓线模型更适合于具有复杂拓扑边界的区域分割问题。在特征提取部分，本算法以降维的Gabor滤波响应和颜色特征来组成彩色纹理特征向量，并采用非线性扩散滤波对它进行平滑。在特征分割部分，本算法采用改进的多区域活动轮廓线模型，使轮廓线在区域竞争项和像素错分惩罚项的共同约束下收敛至区域边界。另外，本算法采用有监督的方式，来保证对小面积组织以及相似组织的辨识。实验表明，基于活动轮廓线模型的方法能更好地适用于提花织物的组织区域划分。
综合上述算法，本文实现了一个面向领带组织花样分析与识别的原型系统。该系统的设计参考了人工分析的流程，其中各模块的核心就是本文所研究的各个算法。同时，系统提供了工艺处理模块，支持用户对组织意匠图进行修改编辑、
With the development of Computer Aided Design technique of woven fabric, intelligentization of fabric imitative design and redesign is required urgently to be improved, in order to meet the new market needs of little batch and large types. The key problem in computer-aided weave identification for woven fabric is: how to automatically identify the weave patterns and parameters of the woven fabric sample by using digital image analysis. Some efforts have been performed on several key issues in this dissertation. The main contributions of this dissertation include:
On the study of fabric image preprocessing, this thesis proposes an algorithm for skew detection and weft yarn density recognition based on Hough transform. Since the skew is inevitable during scanning fabric sample, the scanned image should be adjusted. To solve this problem, we provide a fast skew detection and correction algorithm for fabric image. Based on the weft direction information extracted from the image, hierarchical Hough transform is used to estimate the skew angle to get a satisfying precision. In terms of the previous result, a novel algorithm to identify the weft density is also introduced, which is not affected by the image skew. This algorithm pickes up the projection profile of the skew angle which represents the arrangement of weft yarns, and then calculates the weft density. The experimental results show the high accuracy of our algorithm.
On the study of identifying weave patterns and parameters, this thesis proposes a method to semi-automatically identify double-layer weft woven fabric. The traditional image-based algorithms only aim at single-layer fabric and gray image. The strength of our algorithm is to identify double-layer fabric by color fabric image, which regards the double-layer weave pattern as a combination of several single-layer weaves. For the single-layer weave identification, a series of color image pre-processing techniques are adopted firstly, including color reduction and morphology operation. Then the weft and warp yarns are segmented respectively by analysis of their different distribution and arrangement features. The weave pattern is obtained from the correct yarn locations finally. Since the nonideal arrangements of weft and warp are considered especially, this algorithm is more practical than the traditional ones.
On the study of weave pattern segmentation, this thesis firstly proposes a segmentation algorithm based on color and spatial information. In this algorithm, the
在织物图像预处理的研究上，本文提出了一种基于Hough变换的图像倾斜纠正与纬纱密度识别算法。在织物图像录入过程中难免存在图像倾斜，需要对其进行调整。针对这一问题，本文提出了一种适用于织物图像的快速倾斜纠正算法。该算法在提取图像中的纬纱走向信息的基础上，运用层次Hough变换来检测倾斜角度，以达到要求的检测精度。利用倾斜检测结果，本文还提出了一种与倾斜无关的纬纱密度识别算法。该算法通过提取倾斜角处的投影轮廓线得到纬密排列规律，由此来计算纬纱密度。实验结果表明，上述算法具有较高的准确率。
在织物组织识别问题的研究上，本文提出了一种适用于纬二重组织的半自动识别算法。传统的组织分析算法多是针对灰度织物图像进行分析，且局限于单层组织识别。本算法以彩色图像为研究对象，将纬二重组织的识别问题分解为单层组织的识别与整合。在单层组织识别中，首先采用降色算法对经纬组织点进行预分割，然后根据实际织物中经纬纱线各自的分布特点给出了不同的分割方法，最后获得组织图。由于本算法着重考虑了非理想的经纬纱线分布状态，因此比传统识别算法具有更好的实用性。
在织物组织分割问题的研究上，本文首先提出了一种基于颜色和空间信息的分割算法。该算法利用颜色分布的均匀性度量来判断像素是否位于组织区域边界，再利用种子生长和区域合并策略将它们划分到不同组织区域中。针对该算法在边界定位上的不足，本文进一步提出了一种基于活动轮廓线模型的纹理分割算法。与传统分割算法相比，结合水平集方法的活动轮廓线模型更适合于具有复杂拓扑边界的区域分割问题。在特征提取部分，本算法以降维的Gabor滤波响应和颜色特征来组成彩色纹理特征向量，并采用非线性扩散滤波对它进行平滑。在特征分割部分，本算法采用改进的多区域活动轮廓线模型，使轮廓线在区域竞争项和像素错分惩罚项的共同约束下收敛至区域边界。另外，本算法采用有监督的方式，来保证对小面积组织以及相似组织的辨识。实验表明，基于活动轮廓线模型的方法能更好地适用于提花织物的组织区域划分。
综合上述算法，本文实现了一个面向领带组织花样分析与识别的原型系统。该系统的设计参考了人工分析的流程，其中各模块的核心就是本文所研究的各个算法。同时，系统提供了工艺处理模块，支持用户对组织意匠图进行修改编辑、
With the development of Computer Aided Design technique of woven fabric, intelligentization of fabric imitative design and redesign is required urgently to be improved, in order to meet the new market needs of little batch and large types. The key problem in computer-aided weave identification for woven fabric is: how to automatically identify the weave patterns and parameters of the woven fabric sample by using digital image analysis. Some efforts have been performed on several key issues in this dissertation. The main contributions of this dissertation include:
On the study of fabric image preprocessing, this thesis proposes an algorithm for skew detection and weft yarn density recognition based on Hough transform. Since the skew is inevitable during scanning fabric sample, the scanned image should be adjusted. To solve this problem, we provide a fast skew detection and correction algorithm for fabric image. Based on the weft direction information extracted from the image, hierarchical Hough transform is used to estimate the skew angle to get a satisfying precision. In terms of the previous result, a novel algorithm to identify the weft density is also introduced, which is not affected by the image skew. This algorithm pickes up the projection profile of the skew angle which represents the arrangement of weft yarns, and then calculates the weft density. The experimental results show the high accuracy of our algorithm.
On the study of identifying weave patterns and parameters, this thesis proposes a method to semi-automatically identify double-layer weft woven fabric. The traditional image-based algorithms only aim at single-layer fabric and gray image. The strength of our algorithm is to identify double-layer fabric by color fabric image, which regards the double-layer weave pattern as a combination of several single-layer weaves. For the single-layer weave identification, a series of color image pre-processing techniques are adopted firstly, including color reduction and morphology operation. Then the weft and warp yarns are segmented respectively by analysis of their different distribution and arrangement features. The weave pattern is obtained from the correct yarn locations finally. Since the nonideal arrangements of weft and warp are considered especially, this algorithm is more practical than the traditional ones.
On the study of weave pattern segmentation, this thesis firstly proposes a segmentation algorithm based on color and spatial information. In this algorithm, the
引文
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