Image Texture, Texture Features, and Image Texture Classification and Segmentation
In this chapter, we will discuss the basic concept of image texture, texture features, and image texture classification and segmentation. These concepts will be the foundation to understand image texture models and algorithms used for image texture analys
- PDF / 578,347 Bytes
- 12 Pages / 439.37 x 666.142 pts Page_size
- 28 Downloads / 225 Views
Image Texture, Texture Features, and Image Texture Classification and Segmentation
The journey of a thousand miles begins with a single step. —Lao Tzu
In this chapter, we will discuss the basic concept of image texture, texture features, and image texture classification and segmentation. These concepts will be the foundation to understand image texture models and algorithms used for image texture analysis. Once texture features are available, many classification and segmentation algorithms from traditional pattern recognition can be utilized for labeling textural classes. Image texture analysis strongly depends on the spatial relationships among gray levels of pixels. Therefore, methods for texture feature extraction are developed by looking at this spatial relationship. For example, the gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP) were derived based on this spatial concept. Traditional techniques for image texture analysis, including classification and segmentation, fall into one of the four categories: statistical, structural, model-based, and transform-based methods. The rapid advancement of deep machine learning in artificial intelligence and convolutional neural networks (CNN) has been widely used in image texture analysis. It would be essential for us to further explore image texture analysis with deep CNN.
1.1
Introduction
Image texture analysis is an important branch in digital image processing and computer vision. Image texture refers to the characterization of the surface of a given object or phenomenon present in the image. Texture occurs in many different types of images such as natural and remote sensing and medical images. In order for machine interpretation and understanding, image texture analysis is used to investigate the contents of image textures, characterize each texture, and categorize different types of textures. In general, image texture analysis consists of four types of problems: (1) texture segmentation, (2) texture classification, (3) texture © Springer Nature Switzerland AG 2019 C.-C. Hung et al., Image Texture Analysis, https://doi.org/10.1007/978-3-030-13773-1_1
3
4
1
Image Texture, Texture Features, and Image Texture Classification
synthesis, and (4) shape from texture [33, 34, 37, 41]. Broadly speaking, texture segmentation is similar to image segmentation in which a priori information is unknown. Image segmentation is defined as the meaningful partitioning of an image into homogeneous regions. Due to the repetition of pixel elements in each texture, some algorithms used in image segmentation may not be suitable for texture segmentation. Texture classification assumes that a priori knowledge about image texture is known, for example, the number of different textural classes in an image. Texture synthesis refers to the generation of textures using mathematical models by providing some parameters for texture models. Shape from texture is the construction of three-dimensional shapes of texture surfaces. Techniques for texture analysis can be classified int
Data Loading...
