Point Pattern Matching Algorithm Research Articles

A three-dimensional polyhedral unit model for grain boundary structure in fcc metals

One of the biggest challenges in developing truly bottom-up models for the performance of polycrystalline materials is the lack of robust quantitative structure–property relationships for interfaces. As a first step in analyzing such relationships, we present a polyhedral unit model to classify the geometrical nature of atomic packing along grain boundaries. While the atomic structure in disordered systems has been a topic of interest for many decades, geometrical analyses of grain boundaries has proven to be particularly challenging because of the wide range of structures that are possible depending on the underlying macroscopic crystallographic character. In this article, we propose an algorithm that can partition the atomic structure into a connected array of three-dimensional polyhedra, and thus, present a three-dimensional polyhedral unit model for grain boundaries. A point-pattern matching algorithm is also provided for quantifying the distortions of the observed grain boundary polyhedral units. The polyhedral unit model is robust enough to capture the structure of high-Σ, mixed character interfaces and, hence, provides a geometric tool for comparing grain boundary structures across the five-parameter crystallographic phase-space. Since the obtained polyhedral units circumscribe the voids present in the structure, such a description provides valuable information concerning segregation sites within the grain boundary. We anticipate that this technique will serve as a powerful tool in the analysis of grain boundary structure. The polyhedral unit model is also applicable to a wide array of material systems as the proposed algorithm is not limited by the underlying lattice structure.

Point-pattern matching based on point pair local topology and probabilistic relaxation labeling

This paper presents a robust point-pattern matching (PPM) algorithm, in which the invariant feature and probabilistic relaxation labeling are combined to improve the assignment accuracy and efficiency. A local feature descriptor, namely, point pair local topology (PPLT), is proposed first. The feature descriptor is defined by histogram which is constructed using the weighting of distance measures and angle measures based on local point pair. We use the matching scores of point pair local topology descriptor’s statistic test to define new compatibility coefficients. Then, the robust support functions are constructed based on the obtained compatibility coefficients. Finally, according to the relaxed iterations of matching probability matrix and the mapping constraints required by the bijective correspondence, the correct matching results are obtained. A number of comparison and evaluation experiments on both synthetic point sets and real-world data demonstrate that the proposed algorithm performs better in the presence of outliers and positional jitter. In addition, it achieves the superior performance under similarity and even nonrigid transformation among point sets in the meantime compared with state-of-the-art approaches.

Whisker spot patterns: a noninvasive method of individual identification of Australian sea lions (Neophoca cinerea).

Reliable methods for identification of individual animals are advantageous for ecological studies of population demographics and movement patterns. Photographic identification, based on distinguishable patterns, unique shapes, or scars, is an effective technique already used for many species. We tested whether photographs of whisker spot patterns could be used to discriminate among individual Australian sea lion (Neophoca cinerea). Based on images of 53 sea lions, we simulated 5,000 patterns before calculating the probability of duplication in a study population. A total of 99% (± 1.5 SD) of patterns were considered reliable for a population of 50, 98% (± 1.7 SD) for 100, 92% (± 4.7 SD) for 500, and 88% (± 5.7 SD) for 1,000. We tested a semiautomatic approach by matching 16 known individuals at 3 different angles (70°, 90°, and 110°), 2 distances (1 and 2 m), and 6 separate times over a 1-year period. A point-pattern matching algorithm for pairwise comparisons produced 90% correct matches of photographs taken on the same day at 90°. Images of individuals at 1 and 2 m resulted in 89% correct matches, those photographed at different angles and different times (at 90°) resulted in 48% and 73% correct matches, respectively. Our results show that the Chamfer distance transform can effectively be used for individual identification, but only if there is very little variation in photograph angle. This point-pattern recognition application may also work for other otariid species.

Ant colony optimization based binary search for efficient point pattern matching in images

Point Pattern Matching (PPM) is a task to pair up the points in two images of a same scene. There are many existing approaches in literature for point pattern matching. However, the drawback lies in the high complexity of the algorithms. To overcome this drawback, an Ant Colony Optimization based Binary Search Point Pattern Matching (ACOBSPPM) algorithm is proposed. According to this approach, the edges of the image are stored in the form of point patterns. To match an incoming image with the stored images, the ant agent chooses a point value in the incoming image point pattern and employs a binary search method to find a match with the point values in the stored image point pattern chosen for comparison. Once a match occurs, the ant agent finds a match for the next point value in the incoming image point pattern by searching between the matching position and maximum number of point values in the stored image point pattern. The stored image point pattern having the maximum number of matches is the image matching with the incoming image. Experimental results are shown to prove that ACOBSPPM algorithm is efficient when compared to the existing point pattern matching approaches in terms of time complexity and precision accuracy.

Grid-based matching for full-field large-area deformation measurement

Grid-based measurement can facilitate metrology and inspection of flexible electronics manufacturing. Multiple fundamental difficulties, however, arise in the large-area and full-field deformation measurement of deformable grid patterns including noise, occlusions, and artifacts. This paper addresses one of the key issues in deformation measurement: the registration and matching of deformed grid patterns. The emphasis is on accurate and robust periodicity tracing registration and constellation matching algorithms for grid pattern fidelity. The registration algorithm uses deviation metrics in deformed grids to estimate global translation, rotation and scaling; the matching algorithm uses the constellation reference grid to mine buried deformed point patterns. Using synthetic data, the validity of the registration algorithm is proved by registering noisy deformed grid patterns with various distortion scales and transformations; the validity of the matching algorithm is proved by matching deformed grid point patterns with various distortion scales, extra point rates and missing point rates. Compared to established non-rigid registration and point pattern matching algorithms, our algorithms demonstrate higher speed, sub-pixel accuracy and robustness in the matching of highly-deformed and noisy grids.

A point pattern matching algorithm based on QR decomposition

A new method for point pattern matching is proposed in this paper. First, we show how the orthogonal-triangular decomposition (QR decomposition) can be used for point pattern matching. Second, in order to improve the matching accuracy, we propose a simple but robust point-matching method relying on finding the first K similar neighbors emerging from candidate matches, to embed the QR decomposition method within the framework of iterative matching. The proposed method, when applied to a wide experimental data has shown higher accuracy than the existing methods in this area.

Efficient point pattern matching algorithm for planar point sets under transform of translation, rotation and scale

Point pattern matching is an important topic in computer vision and pattern recognition, and finds many applications such as image registration, motion detection, object tracking and pose estimation. In this paper, we propose an efficient algorithm for determining correspondence between two planar point sets under transform of translation, rotation and scale. This algorithm randomly selects some points of a set and extracts their neighbor points. It views the selected points and their neighbor points as local point patterns, and finds the local matched patterns in the other set. Point pattern matching is finally achieved by counting the unique point number of those local matched point patterns with the same transform parameters. Many experiments are conducted to validate efficiency of the proposed algorithm. Running time comparisons with a well-known point pattern matching algorithm are also done and the results show that the proposed algorithm is faster than the compared algorithm.

LED Chips Locating Algorithm Based on Wavelet Transformation

LED chips position is of crucial significance in chip testing, scribing, die spreading, and die bonding. It’s a great solution to indicate electrical characteristics of chips, examine whether the chip pins are up to the standard, and distinguish LED chip quality. Concerning this, an LED chip positioning method based on wavelet transform is proposed in this paper. Firstly, CCD, light and motion control module are adopted to construct and acquire the visual system of LED chip images. Then the images are processed with lowpass filtering and normalization to obtain Hi-Q chips image, and image features are extracted by further using multi-scale wavelet transform. Lastly, high accuracy positioning of LED chips is achieved by employing high-accuracy point pattern matching algorithm. Experimental results show that the LED chip image positioning error is less than 1μm and the position speed is faster than 5 particles per second, which offer new approaches for high-accuracy chip positioning system of detection machines, sorting machines, die bonders, etc.

Image registration using a point-line duality based line matching method

In the paper, a Point-Line Duality (PLD), i.e. a line in the image (x–y) space corresponds to a point in the dual (θ–ρ) space, based line matching method is proposed for image registration. First, edge points are detected in a template image and a target image. The edge points are linked and segmented into chains. The chains are fitted to lines, and the lines are mapped to dual points in the dual space. To improve stability and efficiency, a point merging algorithm is proposed to deal with the fragmentary line segments that should belong to a single line. As a result, a line matching problem is converted to a point pattern matching problem. Finally, a point pattern matching algorithm is proposed to determine registration parameters and to determine matched line pairs. Experimental results demonstrate that the proposed method is effective for images under rigid body transformation, occlusion, and illumination change.

Point Pattern Matching algorithm based on Point Pair Topological Characteristics and Spectral Matching

Most of the Point Pattern Matching (PPM) algorithm performs poorly when the noise of the point’s position and outliers exist. This paper presents a novel and robust PPM algorithm which combined Point Pair Topological Characteristics (PPTC) and Spectral Matching (SM) together to solve the afore mentioned issues. In which PPTC, a new shape descriptor, is firstly proposed. A new comparability measurement based on PPTC is defined as the matching probability. Finally, the correct matching results are achieved by the spectral matching method. The synthetic data experiments show its robustness by comparing with the other state-of-art algorithms and the real world data experiments show its effectiveness.

Point Pattern Matching Algorithm for Planar Point Sets under Euclidean Transform

Point pattern matching is an important topic of computer vision and pattern recognition. In this paper, we propose a point pattern matching algorithm for two planar point sets under Euclidean transform. We view a point set as a complete graph, establish the relation between the point set and the complete graph, and solve the point pattern matching problem by finding congruent complete graphs. Experiments are conducted to show the effectiveness and robustness of the proposed algorithm.

Computer-aided photo-identification system with an application to polar bears based on whisker spot patterns

Abstract Ecologists often rely on unique natural markings to identify individual free-ranging animals without disturbing them. We developed a computer-aided photo-identification system for identifying polar bears (Ursus maritimus) based on whisker spot pattern recognition. We automated our system so that the selection of 3 reference points on the input image is the only manual step required during image preprocessing. Our pattern-matching algorithm is unique in that the variability within spot patterns is considered fully rather than representing them as points and applying a point-pattern matching algorithm. We also measured the reliability of our method as probabilities of true positives and false positives using photographs of various qualities taken at different angles. When we excluded photographs of poor quality and angle the probability of true positives was >80% at a false positive probability of 10%. A new photograph could be preprocessed in <1 min and tested against a reference library of 100 in...

Finger Print Recognition using Discrete Wavelet Transform

The most common approach for fingerprint analysis is using minutiae that identifies corresponding features and evaluates the resemblance between two fingerprint impressions. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem. Finger print recognition can be done effectively using texture classification approach. Important aspect here is appropriate selection of features that recognize the finger print. We propose an effective combination of features for multi-scale and multi-directional recognition of fingerprints. The features include standard deviation, kurtosis, and skewness . We apply the method by analyzing the finger prints with discrete wavelet transform (DWT) . We used Canberra distance metric for similarity comparison between the texture classes. We trained 30 images and obtained an overall performance up to 96%.

FINGER PRINT RECOGNITION USING DISCRETE WAVELET TRANSFORM

The most common approach for fingerprint analysis is using minutiae that identifies corresponding features and evaluates the resemblance between two fingerprint impressions. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem. Finger print recognition can be done effectively using texture classification approach. Important aspect here is appropriate selection of features that recognize the finger print. We propose an effective combination of features for multi-scale and multi-directional recognition of fingerprints. The features include standard deviation, kurtosis, and skewness . We apply the method by analyzing the finger prints with discrete wavelet transform (DWT) . We used Canberra distance metric for similarity comparison between the texture classes. We trained 30 images and obtained an overall performance up to 96%.

A Memetic Fingerprint Matching Algorithm

Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching between two minutiae sets. Within the MFMA, we first introduce an efficient matching operation to produce an initial population of local alignment configurations by examining local features of minutiae. Then, we devise a hybrid evolutionary procedure by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal or near optimal global alignment. Finally, we define a reliable matching function for fitness computation. The proposed algorithm was evaluated by means of a series of experiments conducted on the FVC2002 database and compared with previous work. Experimental results confirm that the MFMA is an effective and practical matching algorithm for fingerprint verification. The algorithm is faster and more accurate than a traditional genetic-algorithm-based method. It is also more accurate than a number of other methods implemented for comparison, though our method generally requires more computational time in performing fingerprint matching.

Motion analysis of articulated objects from monocular images

This paper presents a new method of motion analysis of articulated objects from feature point correspondences over monocular perspective images without imposing any constraints on motion. An articulated object is modeled as a kinematic chain consisting of joints and links, and its 3D joint positions are estimated within a scale factor using the connection relationship of two links over two or three images. Then, twists and exponential maps are employed to represent the motion of each link, including the general motion of the base link and the rotation of other links around their joints. Finally, constraints from image point correspondences, which are similar to that of the essential matrix in rigid motion, are developed to estimate the motion. In the algorithm, the characteristic of articulated motion, i.e., motion correlation among links, is applied to decrease the complexity of the problem and improve the robustness. A point pattern matching algorithm for articulated objects is also discussed in this paper. Simulations and experiments on real images show the correctness and efficiency of the algorithms.

Particle swarm optimization for point pattern matching

The technique for point pattern matching (PPM) is essential to many image analysis and computer vision tasks. Given two point patterns, the PPM technique finds an optimal transformation for one point pattern such that a distance measure from the transformed point pattern to the other is minimized. This paper presents a new PPM algorithm based on particle swarm optimization (PSO). The set of transformation parameters is encoded as a real-valued vector called particle. A swarm of particles are initiated at random and fly through the transformation space for targeting the optimal transformation. The proposed algorithm is validated through both synthetic datasets and real fingerprint images. The experimental results manifest that the PSO-based method is robust against practical scenarios such as positional perturbations, contaminations, and drop-outs from the point sets. The PSO algorithm is also shown to be superior to a genetic algorithm and a simulated annealing algorithm on both effectiveness and efficiency.

Combinatorial Geometry Problems in Pattern Recognition

Combinatorial geometry problems motivated by point pattern matching algorithms are considered, and the classical exact matching situation and several variants are discussed.

Point pattern matching algorithm invariant to geometrical transformation and distortion

In this study, we propose a new point pattern matching algorithm based on minimum feature relations. The matching algorithm can be divided into two phases: relaxation process (RP) and select-match-pair process (SMPP). Some experiments are also performed to demonstrate the feasibility and accuracy of the proposed algorithm.

Parameterized point pattern matching and its application to recognition of object families

The problem of recognizing and localizing objects that can vary in parameterized ways is considered. To achieve this goal, a concept of parameterized point pattern is introduced to model parameterized families of such objects, and a parameterized point pattern matching algorithm is proposed. A parameterized point pattern is a very flexible concept that can be used to model a large class of parameterized objects, such as a pair of scissors with rotating blades. The proposed matching algorithm is formulated as a tree search procedure, and it generates all maximum matchings satisfying a condition called delta -boundedness. Several pruning methods based on the condition of delta -boundedness and their efficient computing techniques are given. The proposed matching algorithm is applied to a real shape matching problem in order to check the validity of the approach.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>