Line segment detection is one of the most challenging tasks in a wide range of image processing and computer vision problems. For more than 3 decades, academics have contributed to developing more robust, accurate, and quick algorithms. The basic approaches, particularly the Hough transform and its expansions, which are among the most well-known algorithms for detecting straight lines in a digital image, are reviewed in this work. This paper investigates line segment detection methods based on line segment detector (LSD) and related principles.
What is the line segment detector (LSD)?
Line segment recognition is a fundamental and long-standing topic in image processing and computer vision. Line segments in an image provide important geometric information, especially when the scene contains many manufactured objects. Line segments can also be used as low-level features to help with problems such as stereo matching, interior design recovery, simultaneous localization and mapping (SLAM), road segmentation, crack detection in materials, image compression, etc.
The Line Segment Detector (LSD), a parameterless line detection method that produces accurate line segments while controlling the frequency of false detections, has recently been proposed. Although the line segment detector gives decent results for most types of images, it fails significantly for images with some white noise in the background. In fact, its running time is prohibitive, making it unsuitable for the next generation of real-time programs.
How does the line segment detector work?
The line segment detector is a computer vision algorithm for image segment detection. It works by analyzing the image to identify regions of high edge density and then grouping these edges into line segments.
The line segment detector algorithm identifies the edges in an image using an edge detection algorithm such as the Canny edge detector. The edge points are then used to generate line segment hypotheses by applying filters to the point set. These filters include the angle and length of the line segments and the number of supporting points for each hypothesis.
The line segment hypotheses are then grouped using a clustering algorithm based on their proximity and similarity. This step helps to eliminate false positives and identify multiple segments that form a single line.
Finally, the algorithm refines the line segments by fitting them to the edge points using a least-squares algorithm. This results in more accurate and precise line segments.
The LSD algorithm is known for its robustness to noise, its ability to detect short and curved line segments, and its efficiency in processing large images. It has been widely used in computer vision applications such as object detection, robotics, and autonomous navigation.
The Line Segment Detector Algorithms
Line Segment Detector (LSD) was created as a tool for automated image analysis. As a result, it must function without the need for parameter adjustment. The method is really based on numerous integers that define its behavior; nevertheless, their values were carefully chosen so that they would operate on all photos.
As a result, they are part of LSD’s design and internal parameters and are not left to the user’s discretion.
There are several algorithms available to detect the line segment, the most essential and suggestive of which are as follows:
Image Scaling
Line segment detector produces different results when the image is examined at different sizes or when the algorithm is applied to only a portion of the image. This is normal and related to the many features that can be seen, whether an image is viewed from a distance or when focusing on a specific section. As a result of the contrario validation phase, the detection thresholds are automatically adapted to the image size, which is proportional to the number of tests.
The user can select the scale of analysis by cropping the image. Otherwise, the line segment detector automatically analyzes the entire image. However, the first step in the line segment detector is to scale the input image to 80% of its original size.
This scaling helps deal with many images’ aliasing and quantization anomalies (especially the staircase effect). Blurring the image would have the same effect, but it would change the statistics of an image in the opposite model: specific structures would be identified on a blurred white noise.
Gradient Computation
The gradient and level line angles indicate the edge direction, i.e., the angle of the dark-to-light transition. Notably, the corresponding gradient or level line angles for a dark-to-light change and a light-to-dark transition are 180 degrees apart. This indicates that the line segments detected by the line segment detector are oriented, and the order of their start and end points is not arbitrary because it encodes which side of the line segment is darker.
For example, if the contrast of an image is reversed (changing black to white and white to black), the LSD result is the same, but the start and end points of each line segment are swapped.
Gradient Pseudo-Ordering
line segment detector is a greedy algorithm, so the order in which pixels are processed affects the result. The more contrasted edges correspond to pixels with a high gradient magnitude. The center pixels, on the other hand, generally have the most significant gradient magnitude. Therefore, it makes logical sense to start the search for line segments at the pixels with the most considerable gradient magnitude. To sort n values, sorting algorithms typically require O (n log n) operations. In linear time, however, a simple pixel pseudo-sorting is conceivable. This is done by creating 1024 bins, each corresponding to an equal gradient magnitude interval between zero and the highest significant observed value of the image. Pixels are classified into bins based on the magnitude of their gradient. LSD starts with pixels from the container with the highest gradient magnitudes.
Rectangular Approximation
A line segment is a geometrical event, such as a rectangle. The rectangle associated with a line-support area must be located before being evaluated. The pixel area is understood as a solid object, with the gradient magnitude of each pixel serving as the point’s “mass.” The center of mass of the region is then chosen as the center of the rectangle, and the principal direction of the rectangle is assigned to the region’s initial inertia axis. Finally, the rectangles’ width and length are set to the minimum values that allow the rectangle to encompass the whole line-support zone.
The Saiwa line segment detection service
The open-source implementation of the line segment detector has been used in the Saiwa Line Segment Detection Service. The LSD algorithm starts by computing a plane line field of vectors perpendicular to the plane line passing through its base point. This field is then divided into linked sections of pixels with the same level line angle up to a certain point. The line support area is then used to estimate a rectangle. Finally, this rectangle is subjected to a contrarian-based validation. In the contrarian model, an event in an observed rectangle of aligned points is certified as relevant if the predicted number of occasions is as excellent as the actual number is modest.
The advantages of line segment detection service in Saiwa
- A cutting-edge, quick, and precise technique
- Reduced the number of false detections without changing any parameters
- Changing parameters manually to customize the algorithm as needed
- Image aggregation is used to apply the method on several photos at the same time.
- View and download the generated images.
- Exporting and archiving findings on the user’s cloud or locally
- The Saiwa team can customize services using the “Request for Customization” option.
Applications of line segment detection service
Line segment detection is a computer vision technique that involves the identification of straight lines in an image. This technique has various applications in different fields, such as
- Robotics: In robotics, line segment detection is used to guide robots along a path. A robot can navigate through a room or follow a specific path by detecting lines in an image.
- Industrial Automation: Line segment vision can be used in industrial automation for quality control, such as detecting cracks or defects in a product’s surface. It can also be used to identify parts on an assembly line.
- Autonomous vehicles: Line segment detection is a critical component of self-driving cars, enabling the car to detect and follow road markings.
- Augmented Reality: Line segment detection can be used in augmented reality applications to detect the edges of objects and overlay digital content on them.
- Medical imaging: Line segment detection can be used in medical imaging to identify blood vessels or other anatomical structures in X-rays or CT scans.
Line segment detection algorithms in machine learning
If you are a data scientist or software engineer working in the field of machine learning, you may encounter tasks that involve line segment detection. line segment detection is a major problem in computer vision that finds applications in various fields including robotics, image processing, and autonomous vehicles. In this section, we will review some common line segment detection algorithms used in machine learning.
Hough Transform
This is a classic algorithm for line segment detection, especially useful for detecting lines in images that are corrupted or broken by noise. The Hough Transform works by representing lines in an image using polar coordinates instead of Cartesian coordinates. This gives the algorithm the ability to handle lines of any direction and intercept.
Probabilistic Hough Transform
The Probabilistic Hough Transform is an extension of the Hough Transform which deals with its computational inefficiency. Instead of considering all possible lines, the probabilistic Hough transform randomly selects a subset of edge transition points and performs line fitting on only those points. This significantly reduces computational complexity while providing accurate line segment detection.
RANSAC (Random Sample Consensus)
It is a powerful line fitting algorithm that is widely used in computer vision and machine learning applications. This algorithm is suitable for line segment detection in the presence of outliers and noise. This algorithm works by repeatedly fitting lines to subsets of data points and selecting the model that best fits the most points.
Deep Learning Approaches
With recent advances in deep learning, several neural network-based approaches have been proposed for line segment detection. These methods use convolutional neural networks to learn line features directly from the data. One of these approaches is Line-CNN, which uses CNN architecture to predict line parameters, including orientation and cutoff, directly from the image. Another popular method is line segment detector, which uses CNN to extract line segments from images.
Deep learning approaches have shown promising results in line segment detection tasks that achieve high accuracy and robustness. However, they require a large amount of described training data and may be computationally expensive during training and inference.
