Proposed Matching Algorithm Research Articles

A Spherical Model Based Keypoint Descriptor and Matching Algorithm for Omnidirectional Images

Omnidirectional images generally have nonlinear distortion in radial direction. Unfortunately, traditional algorithms such as scale-invariant feature transform (SIFT) and Descriptor-Nets (D-Nets) do not work well in matching omnidirectional images just because they are incapable of dealing with the distortion. In order to solve this problem, a new voting algorithm is proposed based on the spherical model and the D-Nets algorithm. Because the spherical-based keypoint descriptor contains the distortion information of omnidirectional images, the proposed matching algorithm is invariant to distortion. Keypoint matching experiments are performed on three pairs of omnidirectional images, and comparison is made among the proposed algorithm, the SIFT and the D-Nets. The result shows that the proposed algorithm is more robust and more precise than the SIFT, and the D-Nets in matching omnidirectional images. Comparing with the SIFT and the D-Nets, the proposed algorithm has two main advantages: (a) there are more real matching keypoints; (b) the coverage range of the matching keypoints is wider, including the seriously distorted areas.

사회문제 해결형 기술수요 발굴을 위한 키워드 추출 시스템 제안

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1

EMBEDDED FINGERPRINT MATCHING ON SMART CARD

This paper describes an embedded minutia-based matching algorithm using the reference point neighborhoods minutiae. The proposed matching algorithm is implemented in restricted environments such as smart card devices requiring careful monitoring of both memory and processing time usage. The proposed algorithm uses a circular tessellation to encode fingerprint features in neighborhood minutia localization binary codes. The objective of the present study is the development of a new matching approach which reduces both computing time and required space memory for fingerprint matching on Java Card. The main advantage of our approach is avoiding the implicit alignment of fingerprint images during the matching process while improving the fingerprint verification accuracy. Tests carried out on the public fingerprint databases DB1-a and DB2-a of FVC2002 have shown the effectiveness of our approach.

Face recognition for newborns

Mixing, abduction and illegal adoption of newborns is a global problem and the research done to solve this problem is minimal. Traditional methods of biometric and non-biometric techniques have their own limitations and fail to provide enough level of security for newborns. The work on adult face recognition has been an active research area in recent years and automatic face recognition for newborns is least reported in the literature. Therefore it is imperative to initiate research, so that future face recognition algorithms will be able to solve this important problem for identification of newborns. The contributions of this research are (i) proposed method, which mitigates the effect of the covariates of newborn face; (ii) providing an analytical and experimental underpinning effect of different facial expressions exhibited by newborns; (iii) preparing newborn face database of 280 individuals with slight variations in pose, illumination and expression. The accuracy of the proposed matching algorithms on the newborn face database with neutral expression is 87.04% and this negates the notion that all newborns look alike.

Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

Extracting well-distributed, reliable, and precisely aligned point pairs for accurate image registration is a difficult task, particularly for multisource remote sensing images that have significant illumination, rotation, and scene differences. The scale-invariant feature transform (SIFT) approach, as a well-known feature-based image matching algorithm, has been successfully applied in a number of automatic registration of remote sensing images. Regardless of its distinctiveness and robustness, the SIFT algorithm suffers from some problems in the quality, quantity, and distribution of extracted features particularly in multisource remote sensing imageries. In this paper, an improved SIFT algorithm is introduced that is fully automated and applicable to various kinds of optical remote sensing images, even with those that are five times the difference in scale. The main key of the proposed approach is a selection strategy of SIFT features in the full distribution of location and scale where the feature qualities are quarantined based on the stability and distinctiveness constraints. Then, the extracted features are introduced to an initial cross-matching process followed by a consistency check in the projective transformation model. Comprehensive evaluation of efficiency, distribution quality, and positional accuracy of the extracted point pairs proves the capabilities of the proposed matching algorithm on a variety of optical remote sensing images.

Iris Recognition Using Possibilistic Fuzzy Matching on Local Features

In this paper, we propose a novel possibilistic fuzzy matching strategy with invariant properties, which can provide a robust and effective matching scheme for two sets of iris feature points. In addition, the nonlinear normalization model is adopted to provide more accurate position before matching. Moreover, an effective iris segmentation method is proposed to refine the detected inner and outer boundaries to smooth curves. For feature extraction, the Gabor filters are adopted to detect the local feature points from the segmented iris image in the Cartesian coordinate system and to generate a rotation-invariant descriptor for each detected point. After that, the proposed matching algorithm is used to compute a similarity score for two sets of feature points from a pair of iris images. The experimental results show that the performance of our system is better than those of the systems based on the local features and is comparable to those of the typical systems.

A flexible edge matching technique for object detection in dynamic environment

Considering the robustness, stability and reduced volume of data, researchers have focused on using edge information in various video processing applications including moving object detection, tracking and target recognition. Though the edge information is more robust compared to intensity, it also exhibits variations in different frames due to illumination change and noise. In addition to this, the amount of variation varies from edge to edge. Thus, without making use of this variability information, it is difficult to obtain an optimal performance during edge matching. However, traditional edge pixel-based methods do not keep structural information of edges and thus they are not suitable to extract and hold this variability information. To achieve this, we represent edges as segments that make use of the structural and relational information of edges to allow extraction of this variability information. During edge matching, existing algorithms do not handle the size, positional and rotational variations to deal with edges of arbitrary shapes. In this paper, we propose a knowledge-based flexible edge matching algorithm where knowledge is obtained from the statistics on the environmental dynamics, and flexibility is to deal with the arbitrary shape and the geometric variations of edges by making use of this knowledge. In this paper, we detailed the effectiveness of the proposed matching algorithm in moving object detection and also indicated its suitability in other applications like target detection and tracking.

Retailer-supplier matching: an application of the deferred acceptance algorithm

In this paper, we apply matching theory to supply chain coordination. We present mathematical optimisation models similar to the newsvendor problem to provide appropriate conditions for retailer-supplier matching. In particular, our matching algorithm, compared to the general matching theory, has uniquely been affected by contract sizes and ordering sequences. We also study that our matching application guarantees stable and optimal outcomes. Numerical examples with various parameter settings are provided to test the feasibility of the matching algorithms. We find that we can avoid the worst matching case when we use the proposed matching algorithms.

A flexible sequence alignment approach on pattern mining and matching for human activity recognition

This paper proposes a flexible sequence alignment approach for pattern mining and matching in the recognition of human activities. During pattern mining, the proposed sequence alignment algorithm is invoked to extract out the representative patterns which denote specific activities of a person from the training patterns. It features high performance and robustness on pattern diversity. Besides, the algorithm evaluates the appearance probability of each pattern as weight and allows adapting pattern length to various human activities. Both of them are able to improve the accuracy of activity recognition. In pattern matching, the proposed algorithm adopts a dynamic programming based strategy to evaluate the correlation degree between each representative activity pattern and the observed activity sequence. It can avoid the trouble on segmenting the observed sequence. Moreover, we are able to obtain recognition results continuously. Besides, the proposed matching algorithm favors recognition of concurrent human activities with parallel matching. The experimental result confirms the high accuracy of human activity recognition by the proposed approach.

Video frame-matching algorithm using dynamic programming

We propose a frame-matching algorithm for video sequences, when a video sequence is modified from its original through frame removal, insertion, shuffling, and data compression. The proposed matching algorithm defines an effective matching cost function and minimizes cost using dynamic programming. Experimental results show that the proposed algorithm provides a significantly lower probability of matching errors than the conventional algorithm.

Similarity Measure Functions for Strategy-Based Biometrics

Functioning of a biometric system in large part depends on the performance of the similarity measure function. Frequently a generalized similarity distance measure function such as Euclidian distance or Mahalanobis distance is applied to the task of matching biometric feature vectors. However, often accuracy of a biometric system can be greatly improved by designing a customized matching algorithm optimized for a particular biometric application. In this paper we propose a tailored similarity measure function for behavioral biometric systems based on the expert knowledge of the feature level data in the domain. We compare performance of a proposed matching algorithm to that of other well known similarity distance functions and demonstrate its superiority with respect to the chosen domain. Keywords—Behavioral Biometrics, Euclidian Distance, Matching, Similarity Measure.

Study on resource service match and search in manufacturing grid system

Resource service match and search (RSMS) is the core to realize manufacturing grid (MGrid) resource scheduling. In order to realize effectively RSMS between resource demanders and providers, a RSMS framework is proposed and the key technologies to realize it are studied. The describing information of resource services are classified into four categories: (a) word concept information, (b) sentence information, (c) number information, including number interval and fuzzy number, and (d) entity class (or data structure) information. The similarity matching algorithms of each kind of describing information are investigated, respectively, including word matching algorithms, sentence matching algorithms, number matching algorithms, and entity class matching algorithms. Based on the proposed matching algorithms, the match and search processes of MGrid resource services are divided into four phases: first, matching the basic information of resource services, such as service name and service description, namely, basic matching; second, matching the inputs and outputs information of resource services, namely, I/O matching; third, matching the quality of service (QoS) information of resource services, namely QoS matching; last, combining the above three matching results and generating an integrated matching result, namely, integrated matching. The matching functions and algorithms of each phase are described in detail. A case study illustrates the application of proposed methods, and the accuracy and efficiency of the proposed method are measured.

Partner Selection Based on Optimal Power Allocation in Cooperative-Diversity Systems

We study how to appropriately match users for two-user cooperative-diversity (CD) systems deploying an optimal-power-allocation (OPA) strategy, considering both the amplify and forward and the regenerate and forward CD schemes. The OPA strategies, which can minimize the total energy consumption for the cooperating pair, are derived for both CD schemes. Then, we study the location of the optimal partner for a user and develop a novel nonbipartite stable matching algorithm with a performance that is very close to the optimal maximum weighted (MW) matching algorithm. The proposed matching algorithm is simple to implement, and its computational complexity is one order lower than that of the optimal MW matching algorithm. Numerical results show that with the OPA and the proposed matching algorithm, a 9~10 dB CD gain can be achieved, which is equivalent to prolonging the cell-phone-battery recharge time by about ten times.

Fingerprint Matching Algorithm using String-Based MHC Detector Set

Fingerprints have been widely used in the biometric authentication because of its performance, uniqueness and universality. Lately, the speed of identification has become a very important aspect in the fingerprint-based security applications. Also, the reliability still remains the main issue in the fingerprint identification. A fast and reliable fingerprint matching algorithm based on the process of the 'self-nonself' discrimination in the biological immune system was proposed. The proposed algorithm is organized by two-matching stages. The 1st matching stage utilized the self-space and MHC detector string set that are generated from the information of the minutiae and the values of the directional field. The 2nd matching stage was made based on the local-structure of the minutiae. The proposed matching algorithm reduces matching time while maintaining the reliability of the matching algorithm.

Effective Approaches for Low Temperature Polysilicon TFT LCD Post-Mapping Yield Control Problem

Low-temperature polysilicon (LTPS) technology has drawn the attention of many display manufacturers because it has several potential advantages over amorphous thin-film-transistor liquid crystal display TFT LCD). The mapping operation matches one TFT and one color-filter (CF) glass plate together to form one LCD plate. Each plate contains a certain number of cells that are independent devices. The matched LCD cell is good only when the TFT and CF cell match is good. When only a TFT or CF cell is good, there is a yield loss. The sorter is a robot used in LCD manufacturing systems to achieve higher yield in matching TFT and CF plates. This sorter contains several ports that can transfer CF glasses from CF cassettes to match TFT glasses. This is an important determinant for post-mapping yield. This study first proposes a linear programming formulation to optimize the plates-matching problem for the various ports. Next, we propose an algorithm to reduce the number of ways for choosing different matched objects when the number of matched cassettes is large. This algorithm avoids computer overload and provides an excellent solution. The empirical results illustrate the efficiency and effectiveness of the proposed approaches. Note to Practitioners - Yield is an important competitiveness determinant for thin-film-transistor liquid crystal display (TFT LCD) factories. TFT-LCD contains three major manufacturing sectors: the array, cell and module assembly processes. The yield loss from the cell process is one of the most critical steps. This study first formulates a linear programming for the TFT color-filter plates-matching problem in the cell process and a reduction algorithm then is developed to reduce the computational efforts. Our proposed approach is workable and it does not require a significant investment to produce yield improvement by applying the proposed matching algorithm.

Fingerprint Matching Algorithm Using String-Based MHC Detector Set

Fingerprints have been widely used in the biometric authentication because of its performance, uniqueness and universality. Lately, the speed of identification has become a very important aspect in the fingerprint-based security applications. Also, the reliability still remains the main issue in the fingerprint identification. A fast and reliable fingerprint matching algorithm based on the process of the "self-nonself" discrimination in the biological immune system was proposed. The proposed algorithm is organized by two-matching stages. The 1st matching stage utilized the self-space and MHC detector string set that are generated from the information of the minutiae and the values of the directional field. The 2nd matching stage was made based on the local-structure of the minutiae. The proposed matching algorithm reduces matching time while maintaining the reliability of the matching algorithm.

ANFIS-based fingerprint-matching algorithm

A new method for fingerprint identification is proposed, using an ANFIS-based matching algorithm, which is suitable for large-scale identification systems. In this algorithm, the Gabor transform is used to extract features of fingerprints, while ANFIS is trained to identify the resultant Gabor features. Experimental verifications show that this proposed matching algorithm has high accuracy.

Object Recognition Using Hausdorff Distance and Image Matching Algorithm

The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.

Hybrid stereo matching with a new relaxation scheme of preserving disparity discontinuity

A hybrid stereo matching algorithm using a combined edge- and region-based method is proposed to take the advantage of each technique, i.e. an exactly matched point and a full resolution disparity map. Region-based matching is typically more efficient than edge-based matching, however, a region-based matcher lacks the capability of generating an accurate fine resolution disparity map. The generation of such a map can be better accomplished by using edge-based techniques. Accordingly, regions and edges both play important and complimentary roles in a binocular stereo process. Since it is crucial that an efficient and robust stereo system utilizes the most appropriate set of primitives, a nonlinear Laplacian filter is modified to extract proper primitives. Since each pixel value of a second-order differentiated image includes important information for the intensity profile, information such as edge-, signed-, and zero-pixels obtained by the modified nonlinear Laplacian filter, is used to determine the matching strategy. Consequently, the proposed matching algorithm consists of edge-, signed-, and zero- or residual-pixel matching. Different matching strategies are adopted in each matching step. Adaptive windows with variable sizes and shapes are also used to consider the local information of the pixels. In addition, a new relaxation scheme, based on the statistical distribution of matched errors and constraint functions which contain disparity smoothness, uniqueness, and discontinuity preservation, is proposed to efficiently reduce mismatched points in unfavorable conditions. Unlike conventional relaxation schemes, the erosion in an abrupt area of a disparity map is considerably reduced because a discontinuity preservation factor based on a survival possibility function is added to the proposed relaxation. The relaxation scheme can be applied to various methods, such as block-, feature-, region-, object-based matching methods, and so on, by modifying the excitatory set of the smoothness constraint function. Experimental results show that the proposed matching algorithm is effective for various images, even if the image has a high content of noise and repeated patterns. The convergence rate of the relaxation and the output quality are both improved.

Stereo Matching Using Robust Estimators and Line Masks

Previous area-based stereo matching algorithms find the disparity by first computing the sum of squared differences (SSD) between corresponding points using a rectangular window, and then searching the position of the minimum SSD within the disparity range. These algorithms generate relatively many matching errors around depth discontinuities, since the SSD function may fail to search for the minimum because of varying disparity profiles in such areas. In this paper, in order to improve the matching accuracy around the depth discontinuities, a new correlation function based on robust estimation technique is proposed for stereo matching. In addition, while previous stereo algorithms utilize a single rectangular window for computing the correlation function, the proposed matching algorithm utilizes 4-directional line masks additionally to reduce the matching errors further. It has been turned out that the proposed algorithm reduces matching errors around depth discontinuities significantly. Experimental results are presented in this paper, comparing the performance of the proposed technique with those of previous algorithms using both synthetic and real images.