Grayscale Level Connectivity: Theory and Applications
Citations Over TimeTop 13% of 2004 papers
Abstract
A novel notion of connectivity for grayscale images is introduced, defined by means of a binary connectivity assigned at image-level sets. In this framework, a grayscale image is connected if all level sets below a prespecified threshold are connected. The proposed notion is referred to as grayscale level connectivity and includes, as special cases, other well-known notions of grayscale connectivity, such as fuzzy grayscale connectivity and grayscale blobs. In contrast to those approaches, the present framework does not require all image-level sets to be connected. Moreover, a connected grayscale object may contain more than one regional maximum. Grayscale level connectivity is studied in the rigorous framework of connectivity classes. The use of grayscale level connectivity in image analysis applications, such as object extraction, image segmentation, object-based filtering, and hierarchical image representation, is discussed and illustrated.
Related Papers
- → Hiding binary image in a grayscale image using Pixel Matching and Randomization Technique(2016)5 cited
- → Comparative study on image processing algorithms in laser cladding process(2020)1 cited
- Centroid-based approach to multiscale representation of greyscale images.(2004)
- → High Image Quality Tone Adjustment Methods for Binary Images(2018)
- Study of Binary Image and Rectifying Results from Grey Scale Images in Image processing(2016)