What Are The Applications of Edge Detection?
Visual processing is an essential branch of computer vision and machine vision systems. Thanks to this technology, very important computer vision functions such as face detection, face recognition, or object detection are widely available today. The operations performed by the algorithms that make these functions possible are among the points that need to be known to understand the technologies better.
Edge detection is also one of the first steps, i.e., functions that significantly enable visual processing. In this article, we will take a look at how edge detection works and where it is used.
What Is Edge Detection
In digital images, it is crucial to identify the features of the images. Just like humans, algorithms should pay attention to these features in order to perceive images. Edge detection application is a visual processing technique used at this point.
The technique is based on locating bridges and sharp changes in the image. Here bridges and sharp changes refer to points where colors and light change. Color and light changes reveal the sharp lines of the processed image. These are precisely the places we call edges here: the points of changing light and brightness on the digital image. A digital image becomes an edge in multiple and different ways, like:
- Horizontal Edges
- Vertical Edges
- Diagonal Edges
Detecting the edges of the image allows the observer to examine the light and color changes in the image in detail. It also means that the algorithm, i.e., machine vision and computer vision, can better process and understand the image.
There are several ways to detect an edge, and these methods differ from each other. Let's take a look at the different edge detection methods:
Prewitt Edge Detection
This method is generally used to detect horizontal and vertical edges.
Sobel Edge Detection
This method is usually used when you want to draw attention to the center of the image to be processed.
Laplacian Edge Detection
Laplacian edge detection can be considered a combination of the abovementioned methods.
Canny Edge Detection
Canny Edge detection is a method used more often than others. It also has more steps and can be considered quite complex. In this method, the image is:
- First converted to grayscale.
- Then the noise is reduced.
- The gradient is calculated, and the corners of the image are smoothed
- Pixels with different properties in the image are identified.
- Finally, weak pixels are converted into solid pixels.
What Are The Benefits of Edge Detection
Here, we should ask, "Why do we need edge detection applications? We can briefly simplify and summarize the reasons mentioned above to answer the question. Discontinuous depth, layer orientation, ambient light variations, and variations in material properties introduce discontinuities in image brightness. With edge detection apllications, we get a set of curves showing the boundaries of objects' surfaces and the features in the image.
Like many technologies, edge detection applications have their advantages and disadvantages. While some minor limitations can be counted among the disadvantages of edge detection applications, in this section, we will focus on the benefits of the method. Edge detection applications are easy to use, widespread and advantageous because they are high-speed result applications.
One of the most important benefits of edge detection applications is that they can greatly reduce the size of the visual data to be processed. Since only the so-called edges remain in the image, processing data is significantly reduced. Since the image's structure remains constant, the most important parts are preserved, there is no loss of vital data, and the processing of the data is accelerated.
Edge detection facilitates the detection of hard and soft characteristics in images. It reveals the outlines of visual data. The most important advantage of the method is that it simplifies the visual data to be processed, as explained above.
Edge Detection Applications in 2022
Edge detection applications and algorithms are used in many different industries. Especially nowadays, edge detection applications are widely used and extremely common. Perhaps the most common of these is object detection. Let's take a look at what edge detection applications are used in this part of our article.
We recognize that using artificial intelligence in healthcare is revolutionizing the field. However, edge detection applications are at the heart of this. Edge detection is used to detect many diseases in the field of health.
For example, abnormalities in the body, such as the detection of tumors, are revealed thanks to edge detection applications. Edge detection applications reveal the characteristics of the image so that doctors can spot the differences.
The "edges" here are the folds at the fingertips. Since there is no other image on the image and the curves are revealed, the system can focus directly on the curves. Edge detection applications can detect these curves, making fingerprint recognition possible.
The self-driving car technology is a relatively new but evolving technology. But for the vehicles to drive themselves, they need to be able to detect other vehicles and people on the road continuously. This is where edge detection applications come into play. In this way, vehicles can locate different objects on the road.
Satellite imagery is another application where edge detection is used. In this way, the noise in the image can be reduced, and maps with important details and the structure of the region can be extracted.
Many technologies are utilizing robotic imaging using edge detection applications. For example, object detection cameras utilize this technique.
When it comes to the question of how to use edge detection applications, edge detection algorithms can be considered complex algorithms. You can use platforms such as Open CV to create these algorithms. Open CV shows you how to create edge detection algorithms in different coding languages and how to create edge detection applications.
However, it is possible to use some much easier applications, especially for applications such as object detection. For example, with Cameralyze, you can access artificial intelligence solutions such as object detection and human detection without code.
Edge detection is a commonly used visual processing technique. One of the most important benefits of the technique is that it simplifies the processed image, reducing the amount of data. Reducing the amount of data and simplifying the processed image so that only important structures are present in the image greatly increases the speed of data processing and saves time.
Edge detection is used in many different fields, from medicine to security. The technique is advantageous in object detection solutions as well. Cameralzye allows you to benefit from code-free object detection solutions without needing technical knowledge. Thanks to Cameralyze, you can benefit from edge detection techniques and object detection solutions with a single click. Simply upload your input, and you can be sure that you will receive a fast and secure output.
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