EDGE DETECTION IN DIP LINE DETECTION AND ALGORITHMS

DIP

1. The Moravec corner detection algorithm follows these steps: Convert the input image to grayscale if it is not already in grayscale. Define a window of a small size (e.g., 3x3 pixels) that moves over the image. For each pixel in the image, calculate the sum of squared differences (SSD) between the intensities of the window and shifted versions of the window in different directions (typically horizontal, vertical, and two diagonal directions). The SSD measures the intensity variation when the window is shifted in a particular direction. Repeat this calculation for all possible shifts of the window in each direction. Compare the SSD values obtained for each shift and select the minimum SSD value. This minimum value represents the direction with the least intensity variation within the window. Compute a corner response metric, typically using the minimum SSD value. In the original Moravec algorithm, the corner response is defined as the sum of squared differences across all directions. However, other metrics like the minimum value or a combination of values from different shifts can also be used. Apply a threshold to the corner response metric to determine whether a pixel is a corner or not. If the response value exceeds the threshold, the pixel is considered a corner. Perform non-maximal suppression to eliminate weak corners and keep only the strongest corners. This can be done by suppressing corners that are adjacent to stronger corners.

2. HARRIS CORNER DETECTION

3. Input: We have a grayscale image with pixel intensities ranging from 0 to 255. Histogram computation: Calculate the histogram of the image. The histogram shows the distribution of pixel intensities in the image. Probability distribution: Normalize the histogram to obtain the probability distribution function (PDF). Cumulative distribution: Compute the cumulative distribution function (CDF) from the PDF. Threshold selection: Iterate over all possible threshold values from 0 to 255. Class probabilities: Calculate the probabilities of pixels belonging to class 1 (foreground) and class 2 (background) for each threshold. Class means: Compute the mean intensities of class 1 and class 2 for each threshold. Between-class variance: Calculate the between-class variance for each threshold. Optimal threshold selection: Find the threshold value that maximizes the between-class variance. Output: Use the optimal threshold to separate the image into foreground (digit) and background regions.

4. Once the optimal threshold is determined, we can apply it to the image. All pixels with intensities greater than the threshold will be classified as foreground, representing the digit, while pixels with intensities less than or equal to the threshold will be classified as background.

5. MODULE 3 REPRESENTATIONS To recognize the object first we have to convert object into a representation using some proper representation technique and from that representation we can get the descriptor and this will used for recognizing the object SIGNATURES

6. Skeleton In image representation, a skeleton refers to a simplified version of the shape or structure of an object or figure within an image. It is commonly used in computer vision and image processing tasks for various purposes such as object recognition, shape analysis, and motion tracking. A skeleton is typically represented as a set of lines or curves that capture the essential features or connectivity of the object while discarding unnecessary details.

7. One common method for skeleton representation is the thinning algorithm, also known as skeletonization. This algorithm iteratively removes pixels from the object boundaries until only the skeleton remains

8. DESCRIPTORS Boundary descriptors are the entities which helps to calculate the property of an object and used to compare between two different boundaries