Dominant Point Detection Research Articles

Retraction Note: A heuristic method for initial dominant point detection for polygonal approximations

Retraction Note: A heuristic method for initial dominant point detection for polygonal approximations

Using Ant Colony Optimization Method to Detect Dominant Points

This paper uses the ant colony optimization method to detect dominant points. It develops a dominant point detection system for closed target images with simple backgrounds. The process is to perform image preprocessing on the captured object images, then perform object contour tracking and breakpoint detection, and finally use the ant colony optimization method to detect the object's dominant points. In such problems, ant colony calculus is applied to such problems by first defining a fitness value. In addition, three different hybrid strategies, including the minimum error method, the maximum distance method, and the random selection method, were also experimentally compared. Experimental results show that the used hybrid strategy can improve the computational efficiency of all three types of problems.

Shape Recognition Using Segmenting and String Matching

This paper presents an efficient way to represent objects. The image of the object is converted into an edge image. Important points of the curve are identified by the dominant point detection method. A line segment of every two consecutive important points is a categorical line segment or a non-linear line segment. Nonlinear segments are fitted as circular arcs. In addition, the compactness of approximate polygons is used as a feature in the shape recognition process. Experimental results show that using this new global feature has better recognition performance than traditional features such as relative distance, length and angle. Overall the new method is efficient and effective in representing and recognizing shapes.

A New Deep Wavefront Based Model for Text Localization in 3D Video

With the evolution of electronic devices, such as 3D cameras, addressing the challenges of text localization in 3D video (e.g., for indexing) is increasingly drawing the attention of the multimedia and video processing community. Existing methods focus on 2D video and their performance in the presence of the challenges in 3D video, such as shadow areas associated with text and irregularly sized and shaped text, degrades. This paper proposes the first approach that successfully addresses the challenges of 3D video in addition to those of 2D. It employs a number of innovations, among which, the first is the Generalized Gradient Vector Flow (GGVF) for dominant points detection. The second is the Wavefront concept for text candidate point detection from those dominant points. In addition, an Adaptive B-Spline Polygon Curve Network (ABS-Net) is proposed for accurate text localization in 3D videos by constructing tight fitting bounding polygons using text candidate points. Extensive experiments on custom (3D video) and standard datasets (2D video and scene text) show that the proposed method is practical and useful, and overall outperforms existing state-of-the-art methods.

An Analysis on Polygonal Approximation Techniques for Digital Image Boundary

Polygonal approximation (PA) techniques have been widely applied in the field of pattern recognition, classification, shape analysis, identification, 3D reconstruction, medical imaging, digital cartography, and geographical information system. In this paper, we focus on some of the key techniques used in implementing the PA algorithms. The PA can be broadly divided into three main category, dominant point detection, threshold error method with minimum number of break points and break points approximation by error minimization. Of the above three methods, there has been always a tradeoff between the three classes and optimality, specifically the optimal algorithm works in a computation intensive way with a complexity ranges from O (N2) to O (N3).The heuristic methods approximate the curve in a speedy way, however they lack in the optimality but have linear time complexity. Here a comprehensive review on major PA techniques for digital planar curve approximation is presented.

A new dominant point detection technique for polygonal approximation of digital planar closed curves

On the visual perception, the real-world objects are the collection of irregular polygons. Representation and understanding the irregular polygon is an interesting and major task in various research areas. However, an approximation of the polygon is quite complex and challenging in different views. In this paper, a new approximation algorithm is proposed to represent the irregular polygon. Using centroid most of the weak points are suppressed and elimination of noise is integrated to produce better results. Iterative insertion of the dominant point is followed with suppression until the required approximation. The proposed method detects the dominant points which have a high impact on the shape and suppress the weak points. The obtained approximated polygon is with fewer vertices/points and retain the original shape with less approximation error. The experiments of the proposed algorithm are conducted using MPEG shape datasets to show its performance both in quantitative and qualitative aspect.

A new dominant point detection technique for polygonal approximation of digital planar closed curves

On the visual perception, the real-world objects are the collection of irregular polygons. Representation and understanding the irregular polygon is an interesting and major task in various research areas. However, an approximation of the polygon is quite complex and challenging in different views. In this paper, a new approximation algorithm is proposed to represent the irregular polygon. Using centroid most of the weak points are suppressed and elimination of noise is integrated to produce better results. Iterative insertion of the dominant point is followed with suppression until the required approximation. The proposed method detects the dominant points which have a high impact on the shape and suppress the weak points. The obtained approximated polygon is with fewer vertices/points and retain the original shape with less approximation error. The experiments of the proposed algorithm are conducted using MPEG shape datasets to show its performance both in quantitative and qualitative aspect.

Dominant point detection based on suboptimal feature selection methods

This paper presents a viable alternative solution for dominant point detection predicated on the comparison of suboptimal feature selection methods. Suboptimal feature selection methods are utilized as standard criteria to identify dominant points. Considering that all of the combinations of points comprise many sets, an algorithm that eliminates some of them is affirmed and illustrated. The sequential backward selection, sequential forward selection, generalized sequential forward selection, generalized sequential backward selection and plus l-take away r selection methods are performed on the remaining points to extract the dominant points. The simulation results exhibit that this method is significantly more effective and efficient in comparison to other proposed methods.

A heuristic method for initial dominant point detection for polygonal approximations

This paper presents a novel initial dominant point detection technique for polygonal approximation in digital planar curves. This technique concentrates on the local and global deviation of the curve and detects the dominant point of the polygon. An iterative split and merge strategy is used effectively to insert and/or delete vertex during the approximation of the polygon. Since the internal steps are automated, a symmetric and better approximation is achieved. The technique is robust to rotation and noise of the shape and produces better results compared to the results obtained by recent work. The performance of the proposed system is evaluated using the benchmark data set and the same is compared in terms of the quantitative and qualitative measures. The experimental results show that proposed technique has outperformed an existing similar method with respect to visual perception and numeric metrics.

Dominant point detection based on discrete curve structure and applications

In this paper, we investigate the problem of dominant point detection on digital curves which consists in finding points with local maximum curvature. Thanks to previous studies of the decomposition of curves into sequence of discrete structures [1–3], namely maximal blurred segments of width ν[4], an initial algorithm has been proposed in [5] to detect dominant points. However, an heuristic strategy is used to identify the dominant points. We now propose a modified algorithm without heuristics but a simple measure of angle. In addition, two applications are as well presented: (1) polygonal simplification to reduce the number of detected dominant points by associating a weight to each of them, and (2) classification using the polygon issued from the reduced dominant points. The experimental results demonstrate the efficiency and robustness of the proposed method.

Analysis of Noisy Digital Contours with Adaptive Tangential Cover

The notion of tangential cover, based on maximal segments, is a well-known tool to study the geometrical characteristics of a discrete curve. However, it is not robust to noise, while extracted contours from digital images typically contain noise and this makes the geometric analysis tasks on such contours difficult. To tackle this issue, we investigate in this paper a discrete structure, named Adaptive Tangential Cover (ATC), which is based on the notion of tangential cover and on a local noise estimator. More specifically, the ATC is composed of maximal segments with different widths deduced from the local noise values estimated at each point of the contour. Furthermore, a parameter-free algorithm is also presented to compute ATC. This study leads to the proposal of several applications of ATC on noisy digital contours: dominant point detection, contour length estimator, tangent/normal estimator, detection of convex and concave parts. An extension of ATC to 3D curves is also proposed in this paper. The experimental results demonstrate the efficiency of this new notion.

An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition

This paper highlights a novel strategy for online Arabic text recognition using a hybrid Genetic Algorithm (GA) and Harmony Search algorithm (HS). The strategy is divided into two phases: text segmentation using dominant point detection, and recognition-based segmentation using GA and HS. At first, the pre-segmentation algorithm uses a modified dominant point detection algorithm to mark a minimal number of points which defines the text skeleton. The generated text skeleton from this process is expressed as directional vector, using 6-directional model, to minimize the effect of character body on segmentation process. Then, GA and HS algorithms are used as recognition-based segmentation phase for text and character recognition respectively. For the segmentation based recognition, binary GA is used to explore different combinations of segmentation points which gives the best score, while HS is integrated inside the GA segmentation to explore the best character score produced from matching the character with different characters stored in the database. In order to initially calibrate and test the system, a locally collected text dataset was used that contains 4500 Arabic words. The algorithm scored a 93.4% successful word recognition rate. Finally, the system was tested on the benchmark ADAB dataset 2 consist of 7851 Arabic words and it scored a successful recognition rate in the range of 94–96%.

Correction of Signboard Distortion by Vertical Stroke Estimation

In this paper, we propose a preprocessing method that it is to correct the distortion of text area in Korean signboard images as a preprocessing step to improve character recognition. Distorted perspective in recognizing of Korean signboard text may cause of the low recognition rate. The proposed method consists of four main steps and eight sub-steps: main step consists of potential vertical components detection, vertical components detection, text-boundary estimation and distortion correction. First, potential vertical line components detection consists of four steps, including edge detection for each connected component, pixel distance normalization in the edge, dominant-point detection in the edge and removal of horizontal components. Second, vertical line components detection is composed of removal of diagonal components and extraction of vertical line components. Third, the outline estimation step is composed of the left and right boundary line detection. Finally, distortion of the text image is corrected by bilinear transformation based on the estimated outline. We compared the changes in recognition rates of OCR before and after applying the proposed algorithm. The recognition rate of the distortion corrected signboard images is 29.63% and 21.9% higher at the character and the text unit than those of the original images.

Fabrication imperfection analysis and statistics generation using precision and reliability optimization method

This paper applies a recently proposed dominant point detection method - precision and reliability optimization (PRO) - for representing shapes in the microscopy images of fabricated structures. This method uses both the local and the global nature of fit for dominant point detection. A smaller value of its control parameter better represents the local curvature properties of the shape while a larger value better indicates the global curvature properties. The applicability of this method to a wide range of microscopy images is demonstrated using four microscopy examples of brightness enhancement films, electromagnetic and photonic band gap materials, and aspherical mirror alignments. It is shown that PRO can clearly highlight several image effects and imperfections which may not be easily identifiable by human eye or may be difficult to analyze and assess. Further, for large scale arrays, it can be used to generate useful fabrication accuracy statistics and detect features with low fidelity or more imperfections.

PRO: A Novel Approach to Precision and Reliability Optimization Based Dominant Point Detection

A novel method that uses both the local and the global nature of fit for dominant point detection is proposed. Most other methods use local fit to detect dominant points. The proposed method uses simple metrics like precision (local nature of fit) and reliability (global nature of fit) as the optimization goals for detecting the dominant points. Depending on the desired level of fitting (very fine or crude), the threshold for precision and reliability can be chosen in a very simple manner. Extensive comparison of various line fitting algorithms based on metrics such as precision, reliability, figure of merit, integral square error, and dimensionality reduction is benchmarked on publicly available and widely used datasets (Caltech 101, Caltech 256, and Pascal (2007, 2008, 2009, 2010) datasets) comprising 102628 images. Such work is especially useful for segmentation, shape representation, activity recognition, and robust edge feature extraction in object detection and recognition problems.

Automated landmark points detection by using a mixture of approaches: the vole-teeth case

This paper deals with the automated detection of a closed curve’s dominant points. We treat a curve as a 1-D function of the arc length. The problem of detecting dominant points is translated into seeking the extrema of the corresponding 1-D function. Three approaches for automated dominant points detection are presented: (1) an approach based on fitting polynomial, (2) an approach using 1-D computer registration and (3) an innovative approach based on a multi-resolution scheme, zero-crossing and hierarchical clustering. Afterwards, two methods are introduced based on the linearly and non-linearly mixing the results from the three approaches. We then mix the results in a mean-square error sense by using the linear and non-linear fittings, respectively. We experimentally demonstrate the problem of detecting 21 landmarks on 38 vole-teeth that by mixing, the detection accuracy is improved by up to 41.47 % with respect to the results for individual approaches, as applied within the mixture.

A novel framework for making dominant point detection methods non-parametric

Most dominant point detection methods require heuristically chosen control parameters. One of the commonly used control parameter is maximum deviation. This paper uses a theoretical bound of the maximum deviation of pixels obtained by digitization of a line segment for constructing a general framework to make most dominant point detection methods non-parametric. The derived analytical bound of the maximum deviation can be used as a natural bench mark for the line fitting algorithms and thus dominant point detection methods can be made parameter-independent and non-heuristic. Most methods can easily incorporate the bound. This is demonstrated using three categorically different dominant point detection methods. Such non-parametric approach retains the characteristics of the digital curve while providing good fitting performance and compression ratio for all the three methods using a variety of digital, non-digital, and noisy curves.

On the Detection of Visual Features from Digital Curves Using a Metaheuristic Approach

In computational shape analysis a crucial step consists in extracting meaningful features from digital curves. Dominant points are those points with curvature extreme on the curve that can suitably describe the curve both for visual perception and for recognition. Many approaches have been developed for detecting dominant points. In this paper we present a novel method that combines the dominant point detection and the ant colony optimization search. The method is inspired by the ant colony search (ACS) suggested by Yin in [1] but it results in a much more efficient and effective approximation algorithm. The excellent results have been compared both to works using an optimal search approach and to works based on exact approximation strategy.

On the Detection of Visual Features from Digital Curves Using a Metaheuristic Approach

In computational shape analysis a crucial step consists in extracting meaningful features from digital curves. Dominant points are those points with curvature extreme on the curve that can suitably describe the curve both for visual perception and for recognition. Many approaches have been developed for detecting dominant points. In this paper we present a novel method that combines the dominant point detection and the ant colony optimization search. The method is inspired by the ant colony search (ACS) suggested by Yin in [1] but it results in a much more efficient and effective approximation algorithm. The excellent results have been compared both to works using an optimal search approach and to works based on exact approximation strategy.

Planar Contour Primitive Recognition of Thin Sheet Part Dimensional Inspection System

According to the two indices of inspection accuracy and inspection speed, a planar contour primitive recognition method of thin sheet part dimensional inspection system based on curvature and HOUGH transform is proposed. A contour point classification algorithm based on neighborhood values is developed, and a curvature threshold method is selected to filter the contour points, and a projection height method is selected to distinguish the property of the primitive and classify the contour points, and the straight line primitive and arc primitive segmentation and merging algorithms are constructed respectively by HOUGH transform. The inspection accuracy and inspection speed of the proposed method are compared and analyzed by contrast experiments between the proposed method and four dominant point detection methods such as Chung & Tsai method and so on. The dominant point detection ability of the proposed method is tested by a simulation planar contour which includes all kinds of dominant points. The experimental results indicate that the proposed method can recognize primitives exactly, the inspection speed is fast and the universality is good.