Image Segmentation in digital image processing

Image segmentation transitions from processing images to extracting attributes by partitioning an image into multiple regions based on intensity values. It identifies abrupt changes in these values, detecting objects and segmenting the image accordingly. This process also aids in boundary detection, line detection, or edge detection by analyzing similar intensity patterns within specific areas of an image.

Discontinuities in images can be identified using methods such as isolated point detection, line detection, and edge detection. These techniques focus on locating abrupt changes or breaks within the image structure. For identifying similarities, approaches include thresholding to segment based on intensity levels; region growing by expanding areas with similar properties; region splitting to divide an image into smaller homogeneous parts; and merging regions that share common characteristics.

Point detection identifies discontinuities in an image using sharpening filters, specifically the second-order derivative with Laplacian. A point is detected at a location (x, y) if the absolute filter response exceeds a set threshold. Detected points are labeled as 1 while others are marked as 0, resulting in a binary output image.

The point detection kernel identifies points in an image by comparing pixel values to a threshold. The process begins with padding the input image using methods like zero-padding or pixel replication, followed by applying a specific mask for point detection. Each resulting pixel value is compared against the given threshold (t); if greater than t, it becomes 1; otherwise, it's set to 0. For example, with t=8 and results such as [3,5,...], only pixels exceeding this value are marked as detected points.

For line detection, second-order derivatives are used to produce thinner lines and stronger filter responses compared to first-order derivatives. First-order derivatives create thicker lines. Specific kernels are applied for detecting horizontal, vertical, or angled lines: one kernel detects horizontal lines; another identifies vertical ones; a third is for +45-degree angles; and the last handles -45-degree angles. The process involves applying these specific filters on images while following standard procedures.

Edge detection is a method for segmenting images by identifying abrupt changes in intensity. First-order derivatives like Robert Cross, Prewitt, and Sobel operators are used to detect thicker lines due to their smoothing characteristics. Second-order derivatives excel at detecting thinner lines, making them more commonly utilized. Additionally, Cresh Compass kernels identify maximum edge strength across predetermined directions.

To detect diagonal edges in images, specific kernels are used. For a +45-degree angle detection, one kernel is applied; for a -45-degree angle detection, another distinct kernel is utilized. These masks effectively highlight the respective directional features within an image.

First-order derivative filters, such as the compass edge detection kernel, are used to detect edges in images. These kernels include specific masks for detecting directions like north, northwest, west, south-west, southeast east and northeast. The second type of kernels discussed is crushed compass kernels which also specialize in directional edge detection across various orientations.

Second-order derivative kernels are used in edge detection, with two main variants available. The application process involves padding the input image and applying these kernels while carefully selecting the appropriate kernel and direction for accurate results.