Concept of Hough Transformation

Development of research and application of Hough transformation at Home and Abroad

He was proposed by Paul Hough in 1962 and published as a patent in the United States. It implements a ing from image space to parameter space. Because of its obvious advantages and valuable properties, it has attracted the attention of many domestic and foreign scholars and engineering technicians. For example, because it calculates a full description parameter based on a local metric, some interruptions occur to the boundary caused by noise interference on the region boundary or being covered by other targets, it has good fault tolerance and robustness. Over the years, experts have conducted in-depth and extensive research on the theoretical nature and application methods of the Hough transformation, and have made many valuable achievements.

The essence of the Hough transformation is to cluster pixels with certain relationships in the image space, and find the parameter space to accumulate corresponding points that can associate these pixels in a certain resolution form. When the parameter space does not exceed two dimensions, this transformation has an ideal effect. However, as the parameter space increases, the computing workload increases sharply, and the storage space consumes a huge amount of time. In this regard, over the years, many scholars at home and abroad have explored many aspects of the conventional hough transformation based on specific situations, and put forward many valuable improvement methods.

L extend the application scope and propose multiple parameterization methods

In earlier studies, the Hough transformation only detects the straight lines in the image and expands to the shapes of detected arcs, quadratic curves, and arbitrary curves; line parameterization methods have also evolved from initial intercept parameters to slope inclination and intercept parameters, dual-hough space parameters, as well as circle center coordinates, radius parameters, and complex shape detection. template-based multi-dimensional key point parameters.

L improve real-time performance and propose a variety of methods to reduce computational workload

Aiming at the deficiencies in the large amount of computing in the Hough transformation, we have successively proposed the four-tree structure of the like, the gradient information-guided, the like, the layered like, the adaptive like, the fast adaptive like, and the random like the like; for high-dimensional Hough transformations, dimensionality reduction is adopted, and data structures are mostly quantified dynamically.

L enhance anti-interference capability and improve detection accuracy

Abstract: The accuracy of the extraction by using the key-value-transform algorithm has always been widely concerned, such as the discretization error, overlapping interference, and anti-noise interference performance of the key-value-transform algorithm. For example, kiryati and buckstein proposed to use the best kaider window function to smoothly filter the parameter fields to reduce the mixing Error; hunt, Nolte, and others used the Signal Detection Theory to compare the anti-interference performance of the Hough transform and the best algorithm based on the maximum posterior probability, and pointed out the reasons that affect the anti-interference performance of the Hough transform.

L various Peak Detection Methods

The peak value detection of parameter space is a clustering detection problem in the Hough transformation, and the selection of threshold is the key to success or failure. One method is to weight the image space to change the peak distribution of the parameter space, and the other is to directly search for the maximum value of the parameter space.

The theory and practice have always been inseparable and complement each other. The main reason for the rapid development of the theory and practice lies in the wide range of practical applications; the shortcomings exposed in practice further promote its development and reciprocating, just like the evolution of life. The main application fields are listed as follows:

L Biomedicine

It has been successfully applied to Ai-based expert diagnostic systems; processing and interpretation of X-ray human photos and CT images; automatic cell nucleus Analysis Systems in optical microscopy and electron microscopy; three-dimensional arterial features are extracted from ultrasound diagnostics.

L automation, Robot Vision

It has been used for automatic monitoring of product components, defect diagnosis, automatic monitoring of production processes, and computer-aided manufacturing (CAM. For example, the system for detecting and locating Mechanical Parts Based on Hough Transformation and the system for inspecting industrial products based on straight lines and arcs as basic features.

L space technology and military defense

It has been used for detection and identification of moving target tracks, and feature extraction of high-altitude reconnaissance plane, spyware satellite, military radar, and other automatic target recognition systems. For example, the shape features of a fighter are extracted and automatically recognized using the Hough transform and the signal detection theory are used to solve the problem of tracking Multiple Moving Targets in parallel.

L Office Automation

It is well applied in many application systems. For example, if the English character feature is extracted and automatically recognized using the Hough transform, the recognition rate of printed characters is 99.6%, and the average recognition rate of Handwritten Characters is 86.9%, it has been successfully applied to automatic sorting and file processing of postal mail.

From the above analysis, we can see that the Hough transformation has a wide range of concerns and good application prospects. In computer vision and automatic target recognition systems, the Hough transform is a powerful tool for edge line feature extraction.

1.2 Contents of this project

As mentioned above, although conventional hough transformation has significant advantages, its shortcomings cannot be ignored. Without improvement, it is sometimes confusing. For example, the detection speed is too slow to achieve real-time control; the accuracy is not high enough. If the expected information cannot be detected, the system makes incorrect judgments, resulting in a large amount of redundant data. In terms of Circle Detection, there are mainly the following shortcomings in conventional Hough Transformation:

1. The parameters are raised from the two parameters of a straight line, namely the intercept and slope, to three, that is, the Center Coordinate and radius. Each point is mapped to a surface in the parameter space, is one to multiple ing, so the amount of computing increases dramatically;

2. A large amount of memory space is required, which takes a long time and has poor real-time performance;

3. In reality, images are generally subject to external noise interference, and the signal-to-noise ratio is low. In this case, the performance of conventional hough transformation will be greatly reduced. It is difficult to determine the maximum parameter space for search due to the appropriate threshold value, there are often "virtual peaks" and "missed checks.

This topic aims at the above issues. Due to the considerable efforts made by our predecessors, there are many types of improvement algorithms in the current process. Here, we only choose a mainstream improvement direction, that is, Random Hough Transformation (RHT, in comparison with conventional hough transformation, this paper analyzes its performance characteristics. Although rht also has obvious defects, there are also many improvement algorithms for it. However, due to limited time and energy, the most perfect effect cannot be achieved for the moment, however, this algorithm is very effective when the number of detected circles is small and the edge extraction effect is good.