Improving texture pattern recognition by integration of multiple texture feature extraction methods

Building extraction is an important way to obtain information in urban planning, land management, and other fields. As remote sensing has various advantages such as large coverage and real-time capability, it becomes an essential approach for building extraction. Among various remote sensing technologies, the capability of providing 3D features makes the LiDAR point cloud become a crucial means for building extraction. However, the LiDAR point cloud has difficulty distinguishing objects with similar heights, in which case texture features are able to extract different objects in a 2D image. In this paper, a building extraction method based on the fusion of point cloud and texture features is proposed, and the texture features are extracted by using an elevation map that expresses the height of each point. The experimental results show that the proposed method obtains better extraction results than that of other texture feature extraction methods and ENVI software in all experimental areas, and the extraction accuracy is always higher than 87%, which is satisfactory for some practical work.

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Texture feature extraction methods for microcalcification classification in mammograms

10.1117/12.387602 ◽

2000 ◽

Cited By ~ 3

Author(s):

Hamid Soltanian-Zadeh ◽

Siamak Pourabdollah-Nezhad ◽

Farshid Rafiee Rad

Keyword(s):

Feature Extraction ◽

Texture Feature ◽

Extraction Methods ◽

Texture Feature Extraction

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Texture Feature Extraction by Using Local Binary Pattern

Jurnal INKOM ◽

10.14203/j.inkom.420 ◽

2016 ◽

Vol 9 (2) ◽

pp. 45 ◽

Cited By ~ 7

Author(s):

Esa Prakasa

Keyword(s):

Feature Extraction ◽

Euclidean Distance ◽

Local Binary Pattern ◽

Texture Feature ◽

Texture Feature Extraction ◽

Distance Method ◽

Texture Pattern ◽

Distribution Shape ◽

Texture Characteristics ◽

Image Pixels

Local Binary Pattern (LBP) is a method that used to describe texture characteristics of the surfaces. By applying LBP, texture pattern probability can be summarised into a histogram. LBP values need to be determined for all of the image pixels. Texture regularity might be determined based on the distribution shape of the LBP histogram. The implementation results of LBP on two texture types - synthetic and natural textures - shows that extracted texture feature can be used as input for pattern classification. Euclidean distance method is applied to classify the texture pattern obtained from LBPcomputation.

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Colour-Based Extraction Methods for the Classification of Breast Milk (ASI)

CCIT Journal ◽

10.33050/ccit.v14i1.966 ◽

2021 ◽

Vol 14 (1) ◽

pp. 21-27

Author(s):

Rogayah Rogayah ◽

Waliya Rahmawanti ◽

Nur Azizah

Keyword(s):

Image Processing ◽

Feature Extraction ◽

Breast Milk ◽

Texture Feature ◽

Extraction Methods ◽

Image Recovery ◽

Texture Feature Extraction ◽

Quality Image

The development of cellular devices makes accessing information in the form of text or images more easier. In line with the growing field of computer vision, various processes in image/image processing continue to increase. Image processing can be done by increasing image quality (image enhancement) and image recovery (image restoration). Feature extraction is divided into three types, namely feature form extraction, texture feature extraction, and color feature extraction. The application of color-based feature extraction methods has been widely used by researchers in the process of classification of various objects. This paper aims to review the technology that can be applied to image processing in a CBIR system with the object of breast milk so that it can measure the quality of breast milk based on its color.

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