Line Segment Features Research Articles

Fast and Robust 2D-Shape Extraction Using Discrete-Point Sampling and Centerline Grouping in Complex Images

This paper initially develops the discrete-point sampling operator's concept, model, and parameters that we have previously proposed, and makes its belt-shaped regions in a discrete-point sampling map more salient and appropriate for centerline extraction. The cross-sectional features of these belt-shaped regions are then analyzed and seven types of feature points are defined to facilitate descriptions of such features. Based on these feature points, a three-level detection system is proposed, including feature points, line segments, and centerlines, to extract centerlines from the belt-shaped regions. Eight basic types of centerlines and five types of relationships among the centerlines are defined by computational geometry algorithms, and Gestalt laws are used to cluster them into groupings. If some prior information about a desired shape is available, retrieval grouping may be carried out by a discrete-point sampling map, the purpose of which is to find centerlines by best matching with prior information. Discrete-point sampling effectually overcomes the influences of interference from noise, textures, and uneven illumination, and greatly reduces the difficulty of centerline extraction. Centerline clustered groupings and retrieval grouping can offer a strong anti-interference ability with nonlinear deformations such as articulation and occlusion. This method can extract large-scale complex shapes combined of lines and planes from complex images. The wheel location results of noise test and other shape extraction experiments show that our method has a strong capability to persist with nonlinear deformations.

Construction of Curve Network on the Multi-Line Contour Based on Convex-Hull

An algorithm is proposed to weave curve network on the multi-line contour, which refers to analysis and design of algorithms in computational geometry and uses convex hull to construct network on the surface of multi-line contour. First of all, with the incremental algorithm, a convex hull of point set, composed of the first and the last point of the feature line segment, is constructed. Then, by using the mapping from convex hull to fitting surface, an initial network is formed which meets the rules of the two-dimensional manifold. At last, a two-dimensional manifold network of feature curves is acquired by modifying connection of the curve network on the multi-line contour. In conclusion, the algorithm this paper put forward is intuitive, concise and easy to be implemented, and the woven curve network can precisely reflect the shape and the topology of the multi-line contour.

Avoiding the interpolation inaccuracy in nearest feature line classifier by spectral feature analysis

In nearest feature line approach, the representational capacity of a given training set is generalized by defining feature lines passing through each pair of samples belonging to the same class. This technique is shown to provide superior performance on various classification problems than the nearest neighbor approach. From the performance point of view, the major weakness of this technique is the interpolation inaccuracy which occurs when a feature line passes through samples that are far away from each other. Several variants are recently proposed to avoid this weakness. In this study, we follow a different path and propose to transform the training data of different classes into separate clusters before applying nearest feature line classifier. Spectral clustering based transformation is used for this purpose and it is shown that the accuracies achieved by both the nearest feature line and the shortest feature line segment approach which is the most recent variant of the nearest feature line technique are improved.

Graph-preserving shortest feature line segment for dimensionality reduction

Shortest feature line segment (SFLS) is a recently proposed classification approach based on nearest feature line (NFL). It naturally inherits the representational capacity enlargement property of NFL and offers many other benefits in accuracy and efficiency. However, SFLS still has several drawbacks, limiting its generalization ability. In this paper, we develop a manifold learning algorithm for dimensionality reduction based on a novel line-based metric derived by integrating SFLS and NFL, which takes advantage of the benefits of the two algorithms and avoids their disadvantages. Unlike the construction of a point-based relationship in traditional dimensionality reduction algorithms, the new measurement forms linear models of multiple feature points, which capture more information than individual prototype and serve to discover the intrinsic connection of nearby points. Moreover, to enhance the discriminating capability, the affinity matrix in graph embedding is designed in supervised manner by using class label information. Experimental results on four standard databases for face recognition confirm the effectiveness of our proposed method.

FPGA-Based Image Processing System for Target Locating

In this paper, we proposed the design of an FPGA-based image processing system for target locating. The locating mechanism is based on the feature line segments of target’s image. The system processes the target’s image sequence, finds and matches feature points on each image, and uses the feature points to calculate the length of feature line segments for target locating. We implemented the Speeded Up Robust Features (SURF) algorithm on FPGA hardware to extract feature points. The system has a core CPU for control and part of the mathematical computation. Custom-designed logic circuit modules are used to accelerate the feature point extraction. The system’s software is designed to work with parallel and pipeline operation. The performance test shows that the system is capable of real-time processing.

Visual object recognition using multi-scale local binary patterns and line segment feature

This paper presents two visual features for object recognition. One is multi-scale Local Binary Pattern (LBP) operator extracted from coarse-to-fine image blocks to well describe texture structures. The other is line segment feature based on Gestalt-inspired region segmentation and fast Hough transform to capture accurate geometric information. The experiments on the SIMPLIcity database and PASCAL VOC 2007 benchmark show the effectiveness of line segment feature, and significant accuracy improvement by using fine-level blocks for LBP. Moreover, fusing LBP from different block levels further boosts the performance and outperforms the state-of-the-art SIFT. Both features also prove complementary to SIFT.

A global localization approach based on Line-segment Relation Matching technique

This paper presents a novel global localization approach for mobile robots by exploring line-segment features in any structured environment. The main contribution of this paper is an effective data association approach, the Line-segment Relation Matching (LRM) technique, which is based on a generation and exploration of an Interpretation Tree (IT). A new representation of geometric patterns of line-segments is proposed for the first time, which is called as Relation Table. It contains relative geometric positions of every line-segment respect to the others (or itself) in a coordinate-frame independent sense. Based on that, a Relation-Table-constraint is applied to minimize the searching space of IT therefore greatly reducing the processing time of LRM. The Least Square algorithm is further applied to estimate the robot pose using matched line-segment pairs. Then a global localization system can be realized based on our LRM technique integrated with a hypothesis tracking framework which is able to handle pose ambiguity. Sufficient simulations were specially designed and carried out indicating both pluses and minuses of our system compared with former methods. We also presented the practical experiments illustrating that our approach has a high robustness against uncertainties from sensor occlusions and extraneous observation in a highly dynamic environment. Additionally our system was demonstrated to easily deal with initialization and have the ability of quick recovery from a localization failure.

Localization for Large Mobile Platform Using Laser Radar

For the large mobile platform without position sensor,a localization system using laser radar is designed.The system extracts the features of line segments and landmarks in the environment using laser radar,and then uses the EKF (extended Kalman filter) algorithm for movement prediction and position amendment of the platform,thus the localization is completed.A large number of experiments prove the precision and reliability of the system.

Research on Panoramic Digital Video Mosaic Algorithm

The paper briefly sums up the digital video panorama stitching steps, core issues and common registration algorithm on the base research of panoramic digital video technology. Also image registration algorithm based on feature line segment ratio is applied to educational panoramic digital video production. Examples have shown that the algorithm has good results in convergence rate, accuracy, and workload and so on, which has proved valuable in the digital video research. The advantages and disadvantages of the algorithm are summarized for the further study.

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.

A Line Segments Matching Method based on Epipolar-line Constraint and Line Segment Features

Line segments are ordinary in industrial scene, accurate line segments matching is a key step for many applications, such as 3-D reconstruction. A matching method based on epipolar-line constraint and line segment features is proposed. Firstly, the points on line segments between image pairs are matched by epipolar-line constraint. Secondly, geometric descriptor and gray value descriptor are used to describe the line segment features, then the two descriptors are combined into a feature vector, and Euclidean distance between vectors is used to achieve fine match. Experiment results show that the proposed method is accurate and fast.

A novel classifier based on shortest feature line segment

A new approach called shortest feature line segment (SFLS) is proposed to implement pattern classification in this paper, which can retain the ideas and advantages of nearest feature line (NFL) and at the same time can counteract the drawbacks of NFL. The proposed SFLS uses the length of the feature line segment satisfying given geometric relation with query point instead of the perpendicular distance defined in NFL. SFLS has clear geometric–theoretic foundation and is relatively simple. Experimental results on some artificial datasets and real-world datasets are provided, together with the comparisons between SFLS and other neighborhood-based classification methods, including nearest neighbor (NN), k-NN, NFL and some refined NFL methods, etc. It can be concluded that SFLS is a simple yet effective classification approach.

基于点线对应的多摄像机全局位姿估计迭代算法

To solve multi-camera pose estimation problem with the coexistence of point and line segment under the industry visual scene,a new pose estimation algorithm for multi-camera from point and line correspondences is proposed.The method uses geometric restriction between multiple calibrated cameras,and globally processes point and line segment feature from overlapping and non-overlapping views of multi-camera rig,finally obtains more robust and accurate pose estimation.In the proposed method,the formulation of datum of multiple cameras is unfined,and the sum of object-space collinearity error of point and coplanarity error of line segment all cameras are used as error function.Finally,an iterative algorithm is developed to minimize the error function.The experimental results prove the effectiveness and superiority of the algorithm for pose estimation.

Rectified nearest feature line segment for pattern classification

This paper points out and analyzes the advantages and drawbacks of the nearest feature line (NFL) classifier. To overcome the shortcomings, a new feature subspace with two simple and effective impr...

Rectified nearest feature line segment for pattern classification

This paper points out and analyzes the advantages and drawbacks of the nearest feature line (NFL) classifier. To overcome the shortcomings, a new feature subspace with two simple and effective improvements is built to represent each class. The proposed method, termed rectified nearest feature line segment (RNFLS), is shown to possess a novel property of concentration as a result of the added line segments (features), which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex tasks such as the two-spiral distribution, which the original NFL cannot deal with properly. Finally, experimental comparisons with NFL, NN(nearest neighbor), k -NN and NNL (nearest neighbor line) using both artificial and real-world data-sets demonstrate that RNFLS offers the best performance.

Evaluation of crystalline objects in crystallizing protein droplets based on line-segment information in greyscale images

Several automated crystallization systems have recently been developed for high-throughput X-ray structure analysis. However, the evaluation process for the growth state of crystallizing protein droplets has not yet been completely automated. This paper proposes a new evaluation method for crystalline objects in automated crystallization experiments. The main objective is to determine whether a droplet contains crystals suitable for diffraction experiments and analysis. The evaluation method developed here involves extracting line-segment features from an image of the droplet and discriminating the state of crystallization using classifiers based on line features. In order to verify the efficacy of the proposed method, it was used to classify images obtained by an automated crystallization system.

Model-based stroke extraction and matching for handwritten Chinese character recognition

This paper proposes a model-based structural matching method for handwritten Chinese character recognition (HCCR). This method is able to obtain reliable stroke correspondence and enable structural interpretation. In the model base, the reference character of each category is described in an attributed relational graph (ARG). The input character is described with feature points and line segments. The strokes and inter-stroke relations of input character are not determined until being matched with a reference character. The structural matching is accomplished in two stages: candidate stroke extraction and consistent matching. All candidate input strokes to match the reference strokes are extracted by line following and then the consistent matching is achieved by heuristic search. Some structural post-processing operations are applied to improve the stroke correspondence. Recognition experiments were implemented on an image database collected in KAIST, and promising results have been achieved.

A Practical, Globally Optimal Algorithm for Geometric Matching under Uncertainty

Geometric matching under uncertainty is a long-standing problem in computer vision. This paper presents a simple and efficient branch-and-bound algorithm for finding globally optimal solutions to geometric matching problems under a wide variety of allowable transformations (translations, isometries, equiform transformations, others) and a wide variety of allowable feature types (point features, oriented point features, line features, line segment features, etc.). The algorithm only requires an implementation of the forward transformation (model-to-image) and an error model to be supplied. Benchmarks and comparisons of the algorithm in comparison with alignment and Hough transform methods are presented.

Positional error curves band of a planar line segment within GISs

In this paper, the positional error curve of point features was extended to an error curves band of line segment features. Firstly, the constitution and shape of the error curves band were analyzed. On this basis, the general boundary curve formula of that band was derived. Secondly, the visualizing error curves bands were realized through three examples. Finally, area index has been examined by comparing numerical results from error curves band and error ellipes band.

View variation of point-set and line-segment features

The variation, with respect to view, of 2D features defined for projections of 3D point sets and line segments is studied. It is established that general-case view-invariants do not exist for any number of points, given true perspective, weak perspective, or orthographic projection models. Feature variation under the weak perspective approximation is then addressed. Though there are no general-case weak-perspective invariants, there are special-case invariants of practical importance. Those cited in the literature are derived from linear dependence relations and the invariance of this type of relation to linear transformation. The variation with respect to view is studied for an important set of 2D line segment features: the relative orientation, size, and position of one line segment with respect to another. The analysis includes an evaluation criterion for feature utility in terms of view-variation. This relationship is a function of both the feature and the particular configuration of 3D line segments. The use of this information in objection recognition is demonstrated for difficult discrimination tasks. >