Shengzhi Du

An Improved Hough Transform Neighborhood Map for Straight Line Segments

The distance between a straight line and a straight line segment in the image space is proposed in this paper. Based on this distance, the neighborhood of a straight line segment is defined and mapped into the parameter space to obtain the parameter space neighborhood of the straight line segment. The neighborhood mapping between the image space and parameter space is a one to one reversible map. The mapped region in the parameter space is analytically derived and it is proved that it can be efficiently approximated by a quadrangle. The proposed straight line segment neighborhood technique for the HT outperforms conventional straight line neighborhood methods currently used with existing HT variations. In contrast to the straight line neighborhoods used in existing HT variations, the proposed straight line segment neighborhood has several advantages including: 1) the detection error of the proposed neighborhood is not affected by the length of the straight line segments; 2) a precision requirement in the image space described using the proposed distance can be explicitly resolved using the proposed formulation; 3) the proposed neighborhood has the ability to distinguish between segments belonging to the same straight line. A variety of experiments are executed to demonstrate that the proposed neighborhood has a variety of interesting properties of high practical value.

Collinear Segment Detection Using HT Neighborhoods

In this paper, geometrical analysis is used to extract novel straight line segment features from the wings around the peaks of the Hough Transform (HT). Based on these features, a practical segment detection method is proposed which has the ability to determine complete straight line segment parameters including the location of the center, length, slope and the Euclidean distance to the origin. The proposed method does not rely on edge point verification in the image space, i.e., the complete set of segment features are determined only using the information embodied in the HT data. The proposed method can distinguish between highly collinear straight line segments. Segment detection is robust to disturbing edge points, especially ones collinear with the object. A predefined collinear segment resolution that provides a theoretical criterion to determine straight line contiguity is derived. Image processing and analysis experiments show consistent robust performance.

Measuring Straight Line Segments Using HT Butterflies

This paper addresses the features of Hough Transform (HT) butterflies suitable for image-based segment detection and measurement. The full segment parameters such as the position, slope, width, length, continuity, and uniformity are related to the features of the HT butterflies. Mathematical analysis and experimental data are presented in order to demonstrate and build the relationship between the measurements of segments and the features of HT butterflies. An effective method is subsequently proposed to employ these relationships in order to discover the parameters of segments. Power line inspection is considered as an application of the proposed method. The application demonstrates that the proposed method is effective for power line inspection, especially for corner detection when they cross poles.

Dynamic Small World Network Topology for Particle Swarm Optimization

A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

Improving open distance learning efficiency by non-invasive brain computer interface

One of the biggest problems of ODL teaching/learning is that lecturers cannot get the feedback from students in time and modify the teaching materials and styles according to the interaction of students. The burgeoning Brain Computer Interface (BCI) created the possibility of assessing the activities of working memory which is closely related to the knowledge accepting (learning, understanding) efficiency. This research aims to build a real-time teaching and learning efficiency assessing system based on the technique of electroencephalograph (EEG, a kind of non-invasive BCI). The activities of working memory is detected by the system when students learning, based on which both sides of lecturers and students, can modify teaching/learning materials and styles. So a relative higher efficiency of knowledge delivery will be created.

A super resolution algorithm to improve the hough transform

This paper introduces a Super Resolution Hough Transform (SRHT) scheme to address the vote spreading, peak splitting and resolution limitation problems associated with the Hough Transform (HT). The theory underlying the generation of multiple HT data frames and the registration of cells obtained from multiple frames are discussed. Experiments show that the SRHT avoids peak splitting and successfully alleviates vote spreading and resolution limitations.

Heuristic Bayesian pixel classification for power line inspection

In this paper the heuristic knowledge obtained using the Hough Transform is applied to determine the prior and posterior probabilities for a traditional Bayesian classifier. This novel Bayesian classifier is used in unmanned aerial vehicle (UAV) for overhead power line inspection. The proposed method fuses the Bayesian probabilities and information pertaining to the position and width of the power lines. The results show that this strategy significantly improves the performance of the classifier. This approach presented in this paper can be generalized by using a variety of heuristics, such as contour information of circles, polygons, or other shapes with or without mathematical equations.

Detecting straight line segments using a triangular neighborhood

A novel straight line segment detection method is proposed in this paper, based on the theory of mapping straight line segment neighborhoods between the image and the HT spaces and the geometrical analysis of the HT butterfly wings. This paper makes full use of the information in the butterfly wings to detect the segments, i.e. detecting segments by matching its butterfly wings. Due to the fact that the butterfly changes its shape and orientation according to the segment parameters, this paper deduces an approximation of the butterfly wings with triangles by moving and/or flipping the segments to the position that minimizes the approximating error. This movement alleviates the computation and precision loss introduced by the butterfly distortions, because straight side triangular regions can be used to obtain the parameters of segments. Compared to existing methods that detect segments using HT data, the proposed method utilizes more information around the butterfly center, and hence is more effective, especially when it is used to detect collinear segments. The experiments verify the performance of the proposed method.

Good resolutions for Hough Transform

This paper demonstrates the relationship between detection errors and resolutions (ρ- and θ- directions) when the Hough Transform (HT) is employed to detect straight segments in images. The inflexion of the error-resolution curve was uncovered. To comprehensively study the location of the inflexion, the effects of several factors are considered, such as the positions (ρ and θ), widths and lengths of straight segments, noise level, and the ratio of resolutions. An error surface according to ρ- and θ- resolutions is obtained in the paper to guide seeking of the best resolutions. The area containing “good” resolution settings is uncovered and modelled.

Integrating method improving HT-butterfly based segment detection

This paper addresses an integrating method to be used in segments detection based on Hough Transform (HT) butterflies. The proposed method is employed to detect the edges of butterflies in higher accuracy than the commonly used existing method, and hence improves the performances (the accuracy and robustness) of segment detection.