Image analysis

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 quantitative or qualitative characterization of two-dimensional (2D) or three-dimensional (3D) digital images. 2D images are, for example, to be analyzed in computer vision, and 3D images in medical imaging. The field was established in the 1950s—1970s, for example with pioneering contributions by Azriel Rosenfeld, Herbert Freeman, Jack E. Bresenham, or King-Sun Fu.

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,
• motion detection e.g. Single particle tracking,
• video tracking,
• optical flow,
• medical scan analysis,
• 3D Pose Estimation,
• automatic number plate recognition.

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.
• 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.
• 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.

Image segmentation during the object base image analysis

Object-Based Image Analysis (OBIA) employs two main processes, segmentation and classification. Traditional image segmentation is on a per-pixel basis. However, OBIA groups pixels into homogeneous objects. These objects can have different shapes and scale. Objects also have statistics associated with them which can be used to classify objects. Statistics can include geometry, context and texture of image objects. The analyst defines statistics in the classification process to generate for example land cover. The technique is implemented in software such as eCognition or the Orfeo toolbox.

When applied to earth images, OBIA is known as Geographic Object-Based Image Analysis (GEOBIA), defined as "a sub-discipline of geoinformation science devoted to (...) partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their characteristics through spatial, spectral and temporal scale".[4] The international GEOBIA conference has been held biannually since 2006.[5]

Object-based image analysis is also applied in other fields, such as cell biology or medicine. It can for instance detect changes of cellular shapes in the process of cell differentiation.[6]

Process of land cover mapping using TM images

Land cover and land use change detection using remote sensing and geospatial data provides baseline information for assessing the climate change impacts on habitats and biodiversity, as well as natural resources, in the target areas.

Application of land cover mapping

• Local and regional planning
• Disaster management
• Vulnerability and Risk Assessments
• Ecological management
• Monitoring the effects of climate change
• Wildlife management.
• Alternative landscape futures and conservation
• Environmental forecasting
• Environmental impact assessment
• Policy development

• Archeological imagery
• Imaging technologies
• Image processing
• Military intelligence
• Remote sensing

• 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 Tony F. Chan and Jianhong (Jackie) Shen, 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., ASM International, 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.