Dominant Point Detection Research Articles

A discrete geometry approach for dominant point detection

We propose two fast methods for dominant point detection and polygonal representation of noisy and possibly disconnected curves based on a study of the decomposition of the curve into the sequence of maximal blurred segments [2]. Starting from results of discrete geometry [3,4], the notion of maximal blurred segment of width ν [2] has been proposed, well adapted to possibly noisy curves. The first method uses a fixed parameter that is the width of considered maximal blurred segments. The second method is deduced from the first one based on a multi-width approach to obtain a non-parametric method that uses no threshold for working with noisy curves. Comparisons with other methods in the literature prove the efficiency of our approach. Thanks to a recent result [5] concerning the construction of the sequence of maximal blurred segments, the complexity of the proposed methods is O( n log n). An application of vectorization is also given in this paper.

INTERPRETATION OF AMBIGUOUS ZONE TO IMPROVE THINNING RESULTS OF HANDWRITTEN CHINESE CHARACTERS

The skeleton representation of characters is a fundamental step to handwriting recognition, but traditional skeletonization algorithms always produce unwanted artifacts or pattern distortions at regions with intersections or junctions of strokes. In this paper, we propose a novel method to eliminate these unreliable skeleton segments and improve the skeletonization of handwritten Chinese characters on the basis of ambiguous zone interpretation. This method consists of two main phases. In the first phase, the parts of characters which contain the distortions of skeleton, called ambiguous zones, are detected. Instead of exploiting any corner or dominant point detection, a set of feature points from the original skeleton and the contour information around them are manipulated in our approach. In the phase of interpretation, the continuity of skeleton segments of substrokes is estimated based on the minimum curvature variation criterion, and compensations are made to fix interstices of terminated skeleton segments. Finally, the distorted parts of characters are reconstructed by applying a cubic B-spline interpolation. Experimental results show that the proposed method can detect ambiguous zones with arbitrary shapes accurately, and produce skeletons that are close to human perceptions.

Robot-handling fabrics with curved edges based on visual servoing and polygonal approximation

The main scope of this work is the development of a robot controller for the manipulation of fabrics lying on a work table towards the sewing process. A fuzzy visual servoing manipulator controller is developed to guide the fabric along the sewing line. The paper focuses on handling fabrics with curved edges by locally approximating the curve section with straight-line segments. An innovative algorithm based on the dominant point detection method and the genetic-based method is incorporated in the robot visual servoing system to achieve sewing of the curved edges. Simulation and experimental tests are conducted to evaluate the performance of the proposed algorithm. The results demonstrate that this approach is efficient and effective for the polygonal approximation, and the proposed robotic system is proved to be robust and efficient, achieving acceptable seam accuracy in the minimum time.

Morphological multiscale decomposition of connected regions with emphasis on cell clusters

After binarization of cells in complex cytological and histological images the segmented structures can be rather far away from a final quantification of features of single cells since cells may overlap and cluster strongly. Separating optically, partially or totally fused entities like cells is a problem which frequently cannot be solved by a watershed segmentation or a basic morphological processing of images. However, considering different morphological scales after iterative erosion gives rise to dominant markers of singular objects. Performing a reconstruction by iterative dilation yields a scale-independent decomposition of multiple disjointed cell clumps of different sizes within an image. Accordingly we developed a technique that splits cell clumps into meaningful parts. Since this method is based on the analysis of the morphological-scale space, generated by iterative erosion, it is independent on the size of cell clusters. The detection of dominant points within the eroded scales are cell-specific markers. The converse integration of markers at different scales is obtained by a successive reconstruction based on constrained dilation of the original cell shape. The advantages of this approach are the independence of cell shapes which are clumped, the consideration of holes or background intensities within objects and the robustness with regard to convergence. An important benefit is the fitting of the operation time to the size of clusters by the size of the morphological structuring element. This means, that this approach requires only one parameter. Finally, a better match of the morphological scale space approach was found and compared with the ground truth as well as the results of the watershed technique. The primary object of this paper is to highlight the algorithm and its results by using different examples from benchmark databases, self generated images that exhibit different topological features and complex configurations of cells within histological images.

Dominant point detection by reverse polygonization of digital curves

A polygonal approximation technique using reverse polygonization is presented in this paper. The reverse polygonization starts from an initial set of dominant points i.e. break points. In that, dominant points are deleted (one in each iteration) such that the maximal perpendicular distance of approximating straight line from original curve is minimal. A comparative study with some commonly referred algorithms is also presented, which shows that this technique can produce better approximation results. The algorithm has additional advantages like simplicity, polygonal approximation with any number of dominant points and up to any error value, and computational efficiency.

GA-based system for critical points detection

A GA-based algorithm for detection of dominant points in closed contours is presented. The proposed scheme aims to simultaneously optimise the number of detected critical points and the approximation error. A new error metric is proposed which takes into account information from the Chamfer image. The performance of the system is shown to be robust to changes in the position, orientation and size of the contour as well as the length of the chromosome.

Dominant point detection: A new proposal

A new method for dominant point detection is presented. This method can be classified as search corner detection using some significant measurement other than curvature category, and needs no input parameters. A new and normalized measurement is described to compute the estimated curvature and to detect dominant points, and a new algorithm is proposed to eliminate collinear points using an optimization procedure. The experimental results show that this method is efficient, effective, reduces the number of dominant points as compared to other proposed methods, and the obtained contours using this objective function are properly adjusted to the original contour.

Polygonal representation of digital planar curves through dominant point detection—a nonparametric algorithm

We describe a new algorithm that detects a set of feature points on the boundary of an 8-connected shape that constitute the vertices of a polygonal approximation of the shape itself. The set of feature points (nodes) is a ranked subset of the original shape points whose connected left and right arm extents cover the entire shape. Nodes are ranked based on their strength (in terms of their importance to other boundary points), length of support region, and distance from the centroid. The polygon obtained by linking the detected nodes approximates the contour in an intuitive way. The proposed algorithm does not require an input parameter and works well for shapes with features of multiple sizes.

Fast Algorithms for Dominant Point Detection with Variable Tradeoff between the Approximation Error and the Compression

This paper proposes two new algorithms for dominant point detection in digital curves. The motivation is the following: ‘points of large curvature contain much information about shape; however, points of small curvature are not devoid of such Information. In many cases, the cumulative curvature due to a large number of such points occurring consecutively is significant.’ In the proposed schemes, detecting the next dominant point on the curve involves determining the curvature at each point, and computing the resultant cumulative curvature for all the points starting with the last detected dominant point. It is demonstrated that such an approach effectively imposes an upper bound on the error in reconstruction. In our algorithms, the compression ratio and the error in the shape representation have been controlled by appropriately chosen parameters. The magnitude of curvature and its sign are both important. Since the algorithms use only 1-curvature, they are very efficient. The proposed strategies yield excellent results, and have been compared with the Teh-Chin algorithm [1] and the Ansari-Huang [2] algorithm.

Hierarchical representation of digitized curves through dominant point detection

Hierarchical data structures play a key role in representation of spatial data. This paper proposes an algorithm for constructing hierarchical representations of digitized curves based on the dominant points of the curve. The comparison of the proposed scheme with the other hierarchical representation schemes viz. Arc Tree and Strip Tree reveals that the proposed algorithm is generally better than the Arc Tree and compares well with the Strip Tree scheme.

A new algorithm for dominant points detection and polygonization of digital curves

A new algorithm for detecting dominant points and polygonal approximation of digitized closed curves is presented. It uses an optimal criterion for determining the region-of-support of each boundary point, and a new mechanism for selecting the dominant points. The algorithm does not require an input parameter, and can handle shapes that contain features of multiple sizes efficiently. In addition, the approximating polygon preserves the symmetry of the shape.

Dominant point detection using adaptive bending value

An efficient method for dominant point detection is proposed in this paper. The region of support for each point on curve is determined using bending value. The points with local maximum smoothing bending value can be located as the dominant points on the curve. The proposed algorithm needs no input parameter. The experimental results show that the new method is efficient and effective in detecting dominant points.

A dynamic method for dominant point detection

A dynamic method for dominant point detection

A dynamic method for dominant point detection

Detecting dominant points is an important step for shape representation. Most of dominant point detection methods attend to preset or find the region of support of each point. In this paper, we demonstrate an improved method for determining the region of support in the dominant point detection. Instead of setting the regions of support for points independently, the region of support dynamically depending on the previous region of support. The experimental results show that the proposed method is effective in detecting dominant points.

Improving fitting quality of polygonal approximation by using the dynamic programming technique

The dynamic programming technique is applied to improve the fitting quality of polygonal approximation. The dominant points of a digital curve are detected by applying a scale-based approach. Then, a polygonal approximation of the digital curve can be obtained by connecting its dominant points. The fitting quality of the polygonal approximation is improved by using a novel dynamic programming algorithm. The combination of the dominant point detection and the dynamic programming can produce a description that has high geometric importance and good fitting quality. Experimental results are provided and compared with several existing methods to show the excellence of the proposed method.

New nonparametric dominant point detection algorithm

The authors propose a new nonparametric dominant point detection algorithm which is divided into two phases: an initial detection phase that locates possible dominant points and a suppression phase that removes redundant dominant points. In the initial detection phase, not only the points with high local curvatures, but also the end points and interception points are detected. In the suppression phase, a novel measurement to act as a suppressing criterion for the removal of the redundant dominant points is proposed. The curvature of a contour segment is modelled by the average cosine angle. If the contour is slightly curved, more points will be suppressed. On the other hand, fewer points will be suppressed if the curved contour is highly curved. The experimental results show that the proposed algorithm can obtain a set of dominant points to represent contours efficiently.

A simple method for dominant point detection

An efficient algorithm for dominant point detection is proposed in this paper. The concept of break points is used to determine an asymmetric region of support of each point on the curves. The curvature is then estimated by the cosine values to assess the degree of possibility of a point being a dominant point. Finally, the dominant points are located by finding the local curvature extreme among the determined asymmetric region of support. The proposed method needs no input parameter. The experimental results show that the new method is efficient and effective in detecting dominant points.

A Cellular Automaton Processor for Line and Corner Detection in Gray-Scale Images

The design and VLSI implementation of a Cellular Automaton processor for the detection of lines and corners in gray-scale images is presented in this paper. The behavior of a number of different Cellular Automaton rules was investigated and it was found that certain rules result in transitions in the Cellular Automaton state-transition diagram that correspond to the masks required for the line and corner detection. More specifically, the one-dimensional Cellular Automaton of length 8, operating under rule 56 with periodic boundary conditions, is capable of generating different sets of mask operators for line detection, corner detection and dominant point detection (and, thus, for arbitrarily-shaped curve detection), depending only on the initial state of the Cellular Automaton, without any additional hardware cost for the implementation or the reconfiguration of different masks. The proposed architecture was designed and implemented on a single VLSI chip using 0.7 μm double-layer metal (DLM) CMOS technology. The behavior of the chip was successfully verified for all sets of masks for line detection, corner detection and dominant point detection.

A boundary concavity code to support dominant point detection

A symbolic algorithm is described to detect dominant points of a simple closed boundary. First, a concavity code is constructed from the Freeman chain code to classify the degree of concavity or convexity of boundary coordinates. Then, dominant points are extracted by discarding shallow curvature sequences of the concavity code, by appealing to a technique called error budgeting.

Using Neural Networks to Detect Dominant Points in Chain-Coded Contours

A novel approach for dominant point detection in chain-coded contours is presented. Classical operations, such as computing a measurement of the curvature from the (x, y) co-ordinates of the contour points, finding curvature maxima, etc., are substituted by a neural network that traverses the contour, and gives a measurement of the relevance of every point. Further and straight-forward processing of the network output provides the dominant points. Two translations of the Freeman chain-code are presented, that easily provide the network input from the chain link values. Results with real and test images are presented that show the feasibility of the proposed algorithm. The simulation of the neural computations on a sequential machine makes the execution time of this algorithm of the same order as that of existing algorithms, but the cost can be significantly reduced by executing these computations on a neural-oriented hardware.