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This document discusses image segmentation. It defines image segmentation as dividing a digital image into multiple regions based on properties like color, intensity, texture, and boundaries. The goals of segmentation are to simplify images into meaningful regions and discriminate foreground from background. Common segmentation techniques include thresholding, region growing, and edge detection. Challenges include irregular lighting, textured surfaces, and image noise. The document also lists several applications of image segmentation such as medical imaging and fingerprint recognition.
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- 3. Image segmentation refer to the process of dividing a digital image into multiple region. Regions:- A group of connected pixels with similar properties. Properties are based on Color Boundaries Intensity Texture pattern Contour Goal , simplify the image into meaning full once.
- 4. It is useful in discriminating foreground from the background. To convert a gray-level image to a binary image by comparing with value T
- 5. . The edges identified by edge detection A sharp adjustment in intensity at the region boundaries.
- 6. Selection of seed (pixel) from the image. Compare with neighboring pixel and them to the region. The properties should be similar. It stop adding when there is no pixels.
- 7. Difficulties are: Images suffer from several weaknesses Irregular illumination, shadows and reflections Objects with textured surfaces Noise in the image. Noise clearly visible in an image from a digital camera
- 9. It is the technique and process used to create image of the human body for clinical purposes or medical science including the study of normal anatomy and physiology. Locate tumors and other pathogenic.
- 11. Digital Image processing Author -Anil Kumar Jain W. K. Pratt, Image Segmentation, in Digital image processing Image Segmentation - University of Wisconsin-Madison homepages.cae.wisc.edu/~ece533/project/f06/huang_wu_rpt.pdf http://perception.csl.illinois.edu/coding/papers/MobahiH2011-IJCV.pdf L. Lucchese, S. Mitra, Color image segmentation: a state-of-the-art survey people.cs.uct.ac.za/~mgallott/honsproj/conclusion.htm
- 12. The approaches for image segmentation based on applicability suitability performance computational cost .