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Posted 3 months ago

Why is region labelling of a binary image a useful image analysis tool? Labelling is usually done based on one of two characteristics, what are they? Using the binary image illustrated in Figure Q2.1, calculate the number of distinct regions that will be found using both 4 - connected and 8 - connected region labelling. Your answer should clearly show each of the distinct regions found.
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Answer from Sia
Posted 3 months ago
Region labeling of a binary image is useful for identifying and analyzing separate distinct areas within the image. The two characteristics used for labeling are connectivity and intensity. The number of distinct regions found using 4-connected and 8-connected region labeling will depend on the specific binary image provided.
Connectivity: This refers to how pixels are connected to each other. In 4-connected labeling, a pixel is considered connected to its immediate horizontal and vertical neighbors. In 8-connected labeling, a pixel is also connected to its diagonal neighbors
Intensity: This refers to the value of the pixels. In a binary image, there are typically two intensity values: one for the background (usually 0) and one for the foreground (usually 1). Labeling is done based on these intensity values
Calculation of distinct regions: To calculate the number of distinct regions, one would need to apply the rules of 4-connected or 8-connected labeling to the binary image provided. This involves scanning the image and assigning labels to connected pixels while ensuring that the same label is used for all pixels in a region
Without the specific binary image, it is not possible to provide the exact number of distinct regions. However, the process would involve identifying connected components based on the chosen connectivity criterion and labeling them accordingly
Key Concept
Connectivity in region labeling determines how pixels are considered adjacent to each other, which affects the number of distinct regions identified in a binary image.
The usefulness of region labeling lies in its ability to segment and analyze different parts of an image based on pixel connectivity and intensity. The exact number of regions identified can vary depending on whether 4-connected or 8-connected labeling is used.

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