Image analysis
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Image analysis or imagery analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques.[1] Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face.
Digital
Digital Image Analysis or Computer Image Analysis is when a computer or electrical device automatically studies an image to obtain useful information from it. Note that the device is often a computer but may also be an electrical circuit, a digital camera or a mobile phone. It involves the fields of computer or machine vision, and medical imaging, and makes heavy use of pattern recognition, digital geometry, and signal processing. This field of computer science developed in the 1950s at academic institutions such as the MIT A.I. Lab, originally as a branch of artificial intelligence and robotics.
It is the
Techniques
There are many different techniques used in automatically analysing images. Each technique may be useful for a small range of tasks, however there still aren't any known methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human's image analysing capabilities. Examples of image analysis techniques in different fields include:
- 2D and 3D object recognition,
- image segmentation,
- Single particle tracking,
- video tracking,
- optical flow,
- medical scan analysis,
- 3D Pose Estimation.
Applications
The applications of digital image analysis are continuously expanding through all areas of science and industry, including:
- assay micro plate reading, such as detecting where a chemical was manufactured.
- astronomy, such as calculating the size of a planet.
- automated species identification (e.g. plant and animal species)
- defense
- error level analysis
- filtering
- machine vision, such as to automatically count items in a factory conveyor belt.
- materials science, such as determining if a metal weld has cracks.
- medicine, such as detecting cancer in a mammography scan.
- metallography, such as determining the mineral content of a rock sample.
- microscopy, such as counting the germs in a swab.
- automatic number plate recognition;
- optical character recognition, such as automatic license plate detection.
- remote sensing, such as detecting intruders in a house, and producing land cover/land use maps.[2][3]
- robotics, such as to avoid steering into an obstacle.
- security, such as detecting a person's eye color or hair color.
Object-based
Object-based image analysis (OBIA) involves two typical processes, segmentation and classification. Segmentation helps to group pixels into homogeneous objects. The objects typically correspond to individual features of interest, although over-segmentation or under-segmentation is very likely. Classification then can be performed at object levels, using various statistics of the objects as features in the classifier. Statistics can include geometry, context and texture of image objects. Over-segmentation is often preferred over under-segmentation when classifying high-resolution images.[4]
Object-based image analysis has been applied in many fields, such as cell biology, medicine, earth sciences, and remote sensing. For example, it can detect changes of cellular shapes in the process of cell differentiation.;[5] it has also been widely used in the mapping community to generate land cover.[4][6]
When applied to
OBIA techniques are implemented in software such as
See also
- Archeological imagery
- Imaging technologies
- Image processing
- imc FAMOS (1987), graphical data analysis
- Land cover mapping
- Military intelligence
- Remote sensing
References
- ISBN 978-0470844731.)
{{cite book}}: CS1 maint: multiple names: authors list (link - .
- PMID 24817838.
- ^ S2CID 92025959.
- S2CID 3785563.
- ^ PMID 24623958.
- ^ G.J. Hay & G. Castilla: Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In: T. Blaschke, S. Lang & G. Hay (eds.): Object-Based Image Analysis – Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Lecture Notes in Geoinformation and Cartography, 18. Springer, Berlin/Heidelberg, Germany: 75-89 (2008)
- ^ "Remote Sensing | Special Issue: Advances in Geographic Object-Based Image Analysis (GEOBIA)". Archived from the original on 2013-12-12.
Further reading
- The Image Processing Handbook by John C. Russ, ISBN 0-8493-7254-2(2006)
- Image Processing and Analysis - Variational, PDE, Wavelet, and Stochastic Methods by ISBN 0-89871-589-X(2005)
- Front-End Vision and Multi-Scale Image Analysis by Bart M. ter Haar Romeny, Paperback, ISBN 1-4020-1507-0(2003)
- Practical Guide to Image Analysis by J.J. Friel, et al., ISBN 0-87170-688-1(2000).
- Fundamentals of Image Processing by Ian T. Young, Jan J. Gerbrands, Lucas J. Van Vliet, Paperback, ISBN 90-75691-01-7(1995)
- Image Analysis and Metallography edited by P.J. Kenny, et al., International Metallographic Society and ASM International (1989).
- Quantitative Image Analysis of Microstructures by H.E. Exner & H.P. Hougardy, DGM Informationsgesellschaft mbH, ISBN 3-88355-132-5(1988).
- "Metallographic and Materialographic Specimen Preparation, Light Microscopy, Image Analysis and Hardness Testing", Kay Geels in collaboration with Struers A/S, ASTM International 2006.
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