Stone Texture Image Dataset

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types as Orange Travertine, Creamy Travertine, Hatchet and Marble. Each category is included ... Related papers can be found in this item ZIP file (free of charge)
Stone Texture Image Dataset (Known as STI Dataset) Dataset Goals: Surface defect detection is a main part of manufactories, which provide special products such as stone, polar wafer, fabric textile, metal, etc. This process is known as inspection process. Until now, many systems have proposed to done it accurate and fast. Visual inspection systems provide good results using image processing techniques. Near all of, the visual surface defect detection approaches analyze product images based on texture features. In this respect, each abnormality in the surface of product is detected as fault or defect. Automatic surface defect detection certainly reduces time & costs in production process. Of course, it may increase the quality of inspection system in terms of detection accuracy. Any hole, damage and slot in stones are called porosity. The porosity amount is an important factor for architectonic stones, because the quality of structure is dependent to the porosity amount. Also, the strength of structure or building against earthquake and torrent is dependent to the porosity amount. In stonecutting factories, one of the basic features for categorizing the quality of stones is its porosity amount. Now, in near all of the stonecutting factories, the porosity amount is computed by experts. So, it is necessary to propose a visual inspection approach to decrease time and costs and increase detection accuracy. According to stone images and surface defect description, porosity can be categorized as a defect. Therefore, the defect-detection algorithms can be used for detecting stone porosities. Hence, Porosity detection is one of the active topic researches in image processing and computer vision area. State-of-the-art texture image datasets are not included product figures. It surface defect detection and inspection systems, two category of product surface image are important, defected images & defect-less images. In all cases, authors try to provide a handmade dataset. It may consume time and cost. In this respect, a stone texture image dataset is made here which includes 4 popular stone types as Orange Travertine, Creamy Travertine, Hatchet and Marble. Each category is included 15 images in two groups, porosity-less and porosity images. In each category, 10 images are stone texture images with porosity, 5 images are stone texture image without any efficient porosity (porosity-less). All of the captured images are different in terms of size and rotation angles.

It is provided by Dr. Shervan Fekri-Ershad Official Website: http://www.shfekri.ir Author Researches: https://scholar.google.com/citations?user=9RkgDQIAAAAJ&hl=en Contact: [email protected]

Stone Texture Image Dataset (Known as STI Dataset) STI Dataset Properties:

Orange Travertine

Creamy Travertine

Hatchet

Marble

#Number of defected stone images

10

10

10

10

#Number of defect-less stone images

5

5

5

5

#Total number of stone images

15

15

15

15

Categories

Different

Images Dimensions Min Weight =125

Max Weight =1910

Min Height =157

Max Height = 1536

Image Type

JPEG

Image Color space

RGB

Horizontal Resolution

72 dpi

Vertical Resolution

72 dpi

Bit Depth

24

Dataset Challenges (for researches):      

Defect detection accuracy Noise sensitivity ratio of detection process Rotation sensitivity in detection process Gray-scale sensitivity in detection process Computational complexity of detection process Zoom sensitivity of detection process

It is provided by Dr. Shervan Fekri-Ershad Official Website: http://www.shfekri.ir Author Researches: https://scholar.google.com/citations?user=9RkgDQIAAAAJ&hl=en Contact: [email protected]

Stone Texture Image Dataset (Known as STI Dataset) Related Papers: 1) Shervan Fekri Ershad and Farshad Tajeripour, "A Robust Approach for Surface Defect Detection Based on One Dimensional Local Binary Patterns", Indian Journal of Science and Technology (INDJST), Vol. 5, No. 8, pp. 3197-3203, August 2012 2) Shervan Fekri-Ershad, and Farshad Tajeripour "Developing a Novel Approach For Stone Porosity Detection Using Modified Local Binary Patterns and Single Scale Retinex", Arabian Journal for Science and Engineering (AJSE), Vol. 39, No. 2, pp. 875-889, 2014 3) Shervan Fekriershad, and Farshad Tajeripour, "Multi-Resolution and Noise-Resistant Surface Defect Detection Approach Using New Version of Local Binary Patterns", Applied Artificial Intelligence An International Journal, Vol. 31, No. 5-6, pp. 395-410, 2017

Related papers can be found in this item ZIP file (free of charge)

It is provided by Dr. Shervan Fekri-Ershad Official Website: http://www.shfekri.ir Author Researches: https://scholar.google.com/citations?user=9RkgDQIAAAAJ&hl=en Contact: [email protected]