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Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face.
Computers are indispensable for the analysis of large amounts of data, for tasks that require complex computation, or for the extraction of quantitative information. On the other hand, the human visual cortex is an excellent image analysis apparatus, especially for extracting higher-level information, and for many applications — including medicine, security, and remote sensing — human analysts still cannot be replaced by computers. For this reason, many important image analysis tools such as edge detectors and neural networks are inspired by human visual perception models.
Computer Image Analysis
Computer Image Analysis largely contains 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.
Digital Image Analysis
Digital 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. The applications of digital image analysis are continuously expanding through all areas of science and industry, including:
- medicine, such as detecting cancer in an MRI scan.
- microscopy, such as counting the germs in a swab.
- remote sensing, such as detecting intruders in a house, and producing land cover/land use maps.
- astronomy, such as calculating the size of a planet.
- materials science, such as determining if a metal weld has cracks.
- machine vision, such as to automatically count items in a factory conveyor belt.
- security, such as detecting a person's eye color or hair color.
- robotics, such as to avoid steering into an obstacle.
- optical character recognition, such as automatic license plate detection.
- assay micro plate reading, such as detecting where a chemical was manufactured.
- metallography, such as determining the mineral content of a rock sample.
Object-based Image Analysis
Object-Based Image Analysis (OBIA) – also Geographic Object-Based Image Analysis (GEOBIA) – "is 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".
The two main processes in OBIA are (1) segmentation and (2) 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 land cover
Each of these application areas has spawned separate subfields of digital image analysis, with a large collection of specialized algorithms and concepts—and with their own journals, conferences, technical societies, and so on.
Land cover mapping
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
•Vulnerability and Risk Assessments
•Monitoring the effects of climate change
•Alternative landscape futures and conservation
•Environmental impact assessment
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- Structure Magazine
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- Bare Images Toolbox Online image processing and analysis application
- Manawatu Microscopy - first known collaboration environment for Microscopy and Image Analysis featuring Open DataBase of Image Analysis Functions.
- Image analysis using MATLAB algorithms