| Image processing and computer vision--Chapter 3-Color and texture analysis-6 questions |
| 1. What factors determine the perception of object color? |
The perception of object color mainly depends on the following three factors:
1. Distribution of wavelengths of light irradiated on the surface of an object
2. How the surface of an object reflects illumination light
3. Sensitivity of sensors or visual cells
In addition to the above three factors, there are also some complex factors that can affect imaging, as shown below:
1. The roughness of the object surface has a certain impact on imaging.
2. For the same point light source, the surface closer to the light source absorbs more energy than the surface farther away, and the intensity of the received energy is inversely proportional to the distance.
| 2. What are the main primary color systems? |
There are five common color base color systems:
1.RGB primary color system
2.CMY primary color system
3.HSV primary color system
4.HSI primary color system
5.LAB primary color system
| 3.What is the diffuse reflection model? |
| 4.What is the specular reflection model? |
| 5. What is the definition of texture characteristics and its application? |
Definition of texture features: Texture is a visual feature that reflects the homogeneous phenomenon in an image. It embodies the slowly changing or periodically changing surface structure organization and arrangement properties of the object surface.
Application of texture features:
1. Image classification and recognition: Texture features can be used to identify different texture types in images, for example, grass, brick walls, tree bark, etc.
2. Medical image analysis: In medical images, texture features can be used to detect and analyze the texture of tissues and identify diseased cells or organs, such as tumors and vascular structures.
3. Computer Vision and Computer Graphics: Texture features can be used to simulate and render the appearance of objects, as well as perform image processing tasks.
4. Geographic Information System (GIS): Texture features are widely used in maps and surface analysis. Remote sensing images are segmented through texture to identify and distinguish various landforms.
5. Industrial process: automatic detection of surface defects of objects, determination of important defects in industrial products, such as detection of defects on the surface of silk fabrics and paintings, to determine the quality of the product.
| 6.What is the principle of Laws algorithm? |
The Laws algorithm is a method for image processing and texture analysis that aims to extract information about different texture features in an image. The core principle of this algorithm is to use a set of basic two-dimensional convolution kernels (Laws kernels) to decompose the image, and calculate the convolution result of each kernel with the image, thereby obtaining the representation of different texture features. These different texture characteristics include structure, orientation, smoothness, roughness, etc.
The following are the basic principle steps of the Laws algorithm:
Generate Laws kernel: Laws kernel is composed of a basic set of 1D filters. These filters represent different texture features such as horizontal, vertical, diagonal and cross textures. By combining these filters, more complex texture feature representations can be obtained.
Convolution operation: Convolve the generated Laws kernel with the input image. This will produce a set of convolutional images, each convolutional image representing information about different texture features in the original image.
Feature extraction: Perform statistics and feature extraction on each convolution image. This can include calculating statistics such as mean, variance, energy, entropy, and other measures related to texture features.
Texture classification or analysis: Use extracted features for texture classification, recognition, or analysis. This can help distinguish different texture types in images for tasks such as texture classification, defect detection, medical image analysis, etc.
In general, the Laws algorithm is able to capture a variety of texture features in images by using a set of basic filters with different orientations and structures, which makes it widely applicable in image analysis and texture recognition tasks. The key idea of this algorithm is to use filters to extract local features of the image and describe these features through statistics, thereby achieving description and analysis of texture.