Digital image processing is a fascinating field where computers are used to transform, enhance, and interpret images. It plays a crucial role in numerous applications, from medical imaging to computer vision and machine learning. One of the foundational techniques in this domain is edge detection ✨— a method used to identify significant transitions in pixel intensities, enabling us to detect objects and boundaries within images.
🔍 Why Edge Detection Matters
Edges are essentially where the most critical information of an image lies. They represent boundaries between different objects, colors, or textures, making them vital for tasks like segmentation and object recognition. Without edge detection, computers would struggle to differentiate between regions in an image.
🔨 How Edge Detection Works
Edge detection is primarily achieved through mathematical operations called convolutions. By applying convolution filters, we can detect changes in pixel intensities in both the horizontal (
xx x
) and vertical (
yy y
) directions. The most commonly used filters for edge detection are the Prewitt, Sobel, and Canny operators. In this project, we focus on using the Prewitt operator to perform this task.
Consider the following convolution matrices for detecting edges in both directions:
|-1 0 1| | 0 0 0|
|-1 0 1| | 1 1 1|
GxG_x Gx
detects horizontal changes.
GyG_y Gy
detects vertical changes.
By applying these matrices across the image, we extract valuable edge information for each pixel. The final edge map is generated by combining both directional components.
📋 Step-by-Step Process
Let’s walk through the basic algorithm to detect edges in a grayscale image of size
M×NM times N M×N
:
Initialize Matrices:
Create three matrices:
GxG_x Gx
,
GyG_y Gy
, and
GG G
, each of size
M×NM times N M×N
. These will store the horizontal, vertical, and final edge information, respectively.
Convolution Operation:
For each pixel, the gradient in the
xx x
-direction (
GxG_x Gx
) and
yy y
-direction (
GyG_y Gy
) is calculated using the convolution matrices.
Combine Results:
Finally, the gradient magnitudes are combined to form the final edge-detected image,
GG G
.
For j from 1 to N–2
Gx(i, j) = [ I(i+1, j–1) + I(i+1, j) + I(i+1, j+1) ] – [ I(i–1, j–1) + I(i–1, j) + I(i–1, j+1) ]
If Gx(i, j) < 0, Gx(i, j) = 0
If Gx(i, j) > 255, Gx(i, j) = 255
end–for
end–for
For i from 0 to M–1
For j from 0 to N–1
G(i, j) = Gx(i, j) + Gy(i, j)
If G(i, j) > 255, G(i, j) = 255
end–for
end–for
🧵 Multi-Threaded Implementation in Ruby
A key feature of this project is its use of multi-threading to parallelize the edge detection process 🧑💻. Instead of processing the entire image in a single thread, the work is divided into two threads:
One thread calculates the horizontal edges using
GxG_x Gx
.
Another thread handles the vertical edges with
GyG_y Gy
.
This approach significantly speeds up the computation, especially for large images.
Main thread: Reads the input image, initializes matrices, and spawns two threads.
Thread 1: Computes the edge detection in the
xx x
-direction.
Thread 2: Computes the edge detection in the
yy y
-direction.
Final step: The results from both threads are combined to produce the final edge map,
GG G
.
🖼️ Results
Here are examples of processed images using this method. The left column shows the original images, and the right column presents the edge-detected outputs:
Original
Processed
🎯 Conclusion
By leveraging the power of Ruby threads, this project demonstrates how to efficiently apply edge detection to images in both the horizontal (
xx x
) and vertical (
yy y
) directions. The results show how edge detection enhances the structural details of images, making them more suitable for tasks like object recognition and segmentation. This approach not only speeds up the processing but also makes it scalable for larger datasets.
For more details and to explore the code, visit the GitHub repository. 🎉
