2. Introduction to Edge Detection
Edge detection is an important technique in computer vision and image processing that
involves identifying the boundaries between different objects or regions in an image. This
information can be used for a wide range of applications, from object recognition to image
segmentation and more.
Why is Edge Detection Important?
Edge detection is important because it allows us to extract useful information from images
that can be used for a wide range of applications. For example, in object recognition, edge
detection can be used to identify the boundaries of different objects in an image, which can
then be used to classify those objects based on their shape or other features. In image
segmentation, edge detection can be used to separate different regions of an image based
on their boundaries, which can be useful for tasks like background removal or object
tracking.
3. Sobel Operator
The Sobel operator is a popular edge detection algorithm that uses a 3x3 kernel to calculate the
gradient of an image. It works by convolving the kernel with the image and finding the magnitude of
the resulting gradient. The Sobel operator is particularly useful for detecting edges in images with
high levels of noise.
How it works
The Sobel operator uses two kernels, one for detecting horizontal edges and the other for
detecting vertical edges. The kernels are defined as follows:
● Horizontal kernel: [[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]
● Vertical kernel: [[-1, -2, -1], [0, 0, 0], [1, 2, 1]]
The kernels are convolved with the image to calculate the gradient in the horizontal and vertical
directions. The magnitude of the gradient is then calculated using the following formula:
The resulting magnitude image can then be thresholded to produce a binary edge map.
4. Canny Edge Detector
How it Works
The Canny Edge Detector is a multi-stage algorithm that aims to detect the edges of an
image while minimizing noise and false positives. The algorithm involves the following steps:
1. Apply Gaussian filter to smooth the image and reduce noise.
2. Compute gradient magnitude and direction using Sobel operator.
3. Perform non-maximum suppression to thin out edges and keep only the strongest ones.
4. Apply double thresholding to classify edge pixels as strong, weak, or non-edges.
5. Perform hysteresis thresholding to link weak edges to strong ones and obtain the final
edge map.
5. Laplacian of Gaussian
The Laplacian of Gaussian (LoG) is a popular edge detection technique that
combines the Gaussian smoothing filter and the Laplacian operator. The Gaussian
filter reduces noise in the image while preserving edges, and the Laplacian
operator enhances edges and detects zero-crossings to identify edges.
● The LoG operator can detect edges at different scales by varying the
standard deviation of the Gaussian filter. This makes it useful for detecting
edges in images with varying levels of detail.
● However, the LoG operator is computationally expensive and can produce
false positives in noisy images.
6. Hough Transform
The Hough Transform is a feature extraction technique used in image
analysis and computer vision. It is commonly used to identify lines and
other simple shapes in an image. The Hough Transform works by
converting an image from the spatial domain to the Hough domain, where
each point in the Hough domain corresponds to a line in the spatial
domain.
The Hough Transform can be used to detect straight lines, circles, ellipses,
and other simple shapes. It is particularly useful in applications such as
object recognition, where it can be used to identify specific shapes or
patterns in an image.
7. Applications of Edge Detection
● Object Detection and Recognition
● Image Segmentation and Boundary Detection
● Medical Imaging and Diagnosis
● Robotics and Autonomous Systems
