Edge Count Heuristic

Implement a simple edge detection technique to count the number of edges in a grayscale image. This task involves applying a basic heuristic to identify pixels with significant intensity changes.

The concept of edge detection is crucial in computer vision as it helps in identifying boundaries and structures within an image. A grayscale image can be represented as a 2D array of intensity values, where each pixel's value is a scalar between 0 and 255. The absolute difference between neighboring pixels can be used to determine the presence of an edge.

To detect edges, follow these steps:

Iterate over each pixel in the image, excluding border pixels.
For each pixel, calculate the absolute difference with its 4-neighbors.
If any of these differences exceed a given threshold, mark the pixel as an edge.

\text{Edge} = \begin{cases} 1, & \text{if } |I(x, y) - I(x', y')| > \text{threshold} \\ 0, & \text{otherwise} \end{cases}

This technique is widely used in image processing applications.

Example:

Input:

count_edges([[0,0,100],[0,0,100],[0,0,100]], 50)

Output:

Reasoning:

Middle column pixels have neighbors differing by 100 > 50

Constraints:

Image is a 2D grayscale array
Threshold is a positive integer
Don't count border pixels as edges
Return total count of edge pixels

Example:

Constraints:

Test Results