Image Analysis

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Image processing techniques for image analysis

Image analysis is the process of extracting meaningful information from images. Image analysis can include such tasks as finding shapes, detecting edges, counting objects, or measuring properties of an object.

Common image analysis algorithms include edge detection, shape detectors, color-based segmentation, and image thresholding. By combining these common image processing techniques with region analysis functions, detailed statistics can be obtained from images to provide human analysts with additional quantitative and qualitative data. Below are several image processing techniques that are often used for image analysis.

Finding and counting objects with a circle detector
Finding and counting objects with a circle detector
Color-based segmentation using K-means clustering
Color-based segmentation using K-means clustering
Image thresholding using a set level
Image thresholding using a set level

For more detail on image analysis, see Image Processing Toolbox.

Examples and How To

Software Reference

See also: color profile, image thresholding, image enhancement, image reconstruction, image segmentation, image transform, digital image processing, image and video processing, Steve on Image Processing (blog), edge detection, image registration, pattern recognition, image analysis videos