Chunling Tu

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.

Motion control and stabilization of a Skid-Steering Mobile Robot

In this paper, motion control and stabilization of a 4-Wheel Skid-Steering Mobile Robot (4WSSMR) are studied. A ratio controller which has been successfully applied in the chemical industry is designed to limit the vehicle lateral skid. Motion stability is guaranteed by generating an appropriate steering action (driving bias in left and right wheels) according to the states of the vehicle. A model free High Order Differential Feedback Controller (HODFC) cooperates with the ratio controller for motion control. In the proposed model-free scheme vehicle states used by the ratio controller and HODFC are estimated by high order differential (HOD) observer. Different road surface scenarios with varying frictional coefficients are designed to test performance and the ability to reject disturbances and noise. Trajectory tracking simulation results show that the proposed is robust to uncertainties and performs well even if some parameters are unknown.

An efficient crop row detection method for agriculture robots

In this paper an efficient crop row detection method is proposed for vision-based navigation for agriculture robots. In the proposed method, no low level features (such as edges and middle lines of the images) are needed. So the complex algorithms for edging and matching (e.g. the Hough transform) are avoided, which greatly saves the computation loads. Instead, a flexible quadrangle is defined to detect the crop rows. The proposed method moves, extends or shrinks the flexible quadrangle to localise the crop rows in the captured frames. The experiments demonstrate that the proposed method is effective with high time efficiency and detection accuracy.

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.

Image Representation in Differential Space

In this paper, a derivative estimator is introduced to obtain differential information of images. Experiments show that differentials obtained by this estimator outperform the traditional Sobel operator and this estimator is practical for extracting differential image information. A new image representation in this differential space is also proposed. Differential sign sequences of images are used as the signature of image patterns. The Hamming distance is used for template matching. The proposed representation is invariant to brightness and contrast and is robust to noise because of the low pass property of the estimator. Template matching is used as an example to exhibit the advantage of this representation. Experiments demonstrate good 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.