基于局部描述子的纹理识别方法及其在叶片识别方面的应用
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摘要
纹理识别是图像处理和机器视觉的一个基础研究方向,在科学研究和工程技术方面有着广泛的应用,比如遥感、生物医学图像分析、生物特征识别、图像检索等等。提取纹理特征是纹理识别的基础,经过几十年的研究和发展,研究人员取得了关于纹理的一些共识：纹理是由局部纹理的大量重复所表现的,可以通过统计局部特征来描述纹理信息。近年来,随着对局部纹理信息的发掘,局部纹理算子类方法成为了纹理识别的一个热门的研究领域,出现了许多高效的局部纹理算子,例如LBP即是其中一种有代表性的局部算子。在真实的应用情形下,纹理图像是在不同的光照、旋转和视角条件下采集的,如何提高局部算子的判别力同时又要保证局部算子对光照、旋转、尺度和视角的变化具有鲁棒性,依旧是一个需要进一步研究的课题。
本文中我们提出了一系列局部纹理描述子,来研究在局部纹理信息中起到关键作用的判别信息,同时尝试寻找提高判别力但是不损失其旋转不变性的局部算子。在传统的LBP类算子观点中认为,局部纹理信息可以划分为由灰度差描述的灰度差异信息和由邻居结构描述的微结构信息,而其中微结构信息起到主要的判别作用。首先,本文提出了一种舍弃了LBP中微结构编码的局部算子,通过实验证明了局部灰度差异分布才是描述局部纹理的主要信息。为了进一步提高局部算子对灰度差异信息的描述能力,本文中又提出了一种通过提高局部量化等级的局部算子来提取纹理特征,通过实验找出了一个最优的局部量化等级。其次,在本文中又提出了鲁棒的LBP模式,作为对LBP算子的改进,鲁棒的LBP能够很好的提高LBP算子的抗噪性和鲁棒性。再次,本文提出了一种基于局部连通的局部算子,用以描述旋转不变的局部结构信息,作为对局部灰度差异信息的补充能够提高算子的识别能力。最后,本文提出了两种把局部算子类算法应用于植物叶片识别的方法,在叶片识别方面也取得较好的效果。
Texture classification is a basic issue of image processing and computer vision, and has an extensive application background in science research and engineering technology, e.g., remote sensing, biomedical image analysis, biometrics, image retrieval. Extracting the texture feature is the foundation of texture classification. In the past decades, the researchers have got a consensus about the conception of texture: Macroscopic texture can be regard as the repeats for a large number of local microcosmic textural patterns, and it can be characterized by the statistic of the local texture patterns. Recently, local texture descriptor has become a popular research topic of texture classification. Some efficient local texture descriptors have been proposed, e.g., Local Binary Pattern (LBP). Generally, texture images captured in the real world may have obvious illumination, orientation, and scale variations. How to enhance the discriminative capability of local descriptors and keep them invariant to illumination and viewpoint is still worth further research.
In this paper we proposed several new local texture descriptors. We try to find the key discriminative information of local texture. In conventional LBP theories, the local texture information is divided into two orthotropic components:the local gray-value difference distribution and the local micro-structure. The micro-structure is thought to be the main information of local texture. Firstly, we proposed a new local operator which totally discards the structural information of the LBP, and the experimental results demonstrate that the key discriminative information of local texture is the distribution of local gray-value difference. Then we try to extract more information about the local gray-value difference by increasing the local quantization levels. Secondly, we presented a robust framework of the LBP, and the proposed RLBP can be more insensitive to noise and illuminations. Thirdly, we proposed another interesting local descriptor, named local connection code, to extract the rotation invariant micro-structure information. Lastly, we presented two effective leaves recognition methods by using two local texture operators.
本文中我们提出了一系列局部纹理描述子,来研究在局部纹理信息中起到关键作用的判别信息,同时尝试寻找提高判别力但是不损失其旋转不变性的局部算子。在传统的LBP类算子观点中认为,局部纹理信息可以划分为由灰度差描述的灰度差异信息和由邻居结构描述的微结构信息,而其中微结构信息起到主要的判别作用。首先,本文提出了一种舍弃了LBP中微结构编码的局部算子,通过实验证明了局部灰度差异分布才是描述局部纹理的主要信息。为了进一步提高局部算子对灰度差异信息的描述能力,本文中又提出了一种通过提高局部量化等级的局部算子来提取纹理特征,通过实验找出了一个最优的局部量化等级。其次,在本文中又提出了鲁棒的LBP模式,作为对LBP算子的改进,鲁棒的LBP能够很好的提高LBP算子的抗噪性和鲁棒性。再次,本文提出了一种基于局部连通的局部算子,用以描述旋转不变的局部结构信息,作为对局部灰度差异信息的补充能够提高算子的识别能力。最后,本文提出了两种把局部算子类算法应用于植物叶片识别的方法,在叶片识别方面也取得较好的效果。
Texture classification is a basic issue of image processing and computer vision, and has an extensive application background in science research and engineering technology, e.g., remote sensing, biomedical image analysis, biometrics, image retrieval. Extracting the texture feature is the foundation of texture classification. In the past decades, the researchers have got a consensus about the conception of texture: Macroscopic texture can be regard as the repeats for a large number of local microcosmic textural patterns, and it can be characterized by the statistic of the local texture patterns. Recently, local texture descriptor has become a popular research topic of texture classification. Some efficient local texture descriptors have been proposed, e.g., Local Binary Pattern (LBP). Generally, texture images captured in the real world may have obvious illumination, orientation, and scale variations. How to enhance the discriminative capability of local descriptors and keep them invariant to illumination and viewpoint is still worth further research.
In this paper we proposed several new local texture descriptors. We try to find the key discriminative information of local texture. In conventional LBP theories, the local texture information is divided into two orthotropic components:the local gray-value difference distribution and the local micro-structure. The micro-structure is thought to be the main information of local texture. Firstly, we proposed a new local operator which totally discards the structural information of the LBP, and the experimental results demonstrate that the key discriminative information of local texture is the distribution of local gray-value difference. Then we try to extract more information about the local gray-value difference by increasing the local quantization levels. Secondly, we presented a robust framework of the LBP, and the proposed RLBP can be more insensitive to noise and illuminations. Thirdly, we proposed another interesting local descriptor, named local connection code, to extract the rotation invariant micro-structure information. Lastly, we presented two effective leaves recognition methods by using two local texture operators.
引文
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