• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.53-63
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• 2008

In this paper, we propose the regional travel time measurement algorithm using the sequence pattern matching to the type of vehicles between the origin of the region and the end of the region, that could be able to overcome the limit of conventional method such as Probe Car Method or AVI Method by License Plate Recognition. This algorithm recognizes the vehicles as a sequence group with a definite length, and measures the regional travel time by searching the sequence of the origin which is the most highly similar to the sequence of the end. According to the assumption of similarity cost function, there are proposed three types of algorithm, and it will be able to estimate the average travel time that is the most adequate to the information providing period by eliminating the abnormal value caused by inflow and outflow of vehicles. In the result of computer simulation by the length of region, the number of passing cars, the length of sequence, and the average maximum error rate are measured within 3.46%, which means that this algorithm is verified for its superior performance.

• Journal of IKEEE
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• v.28 no.3
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• pp.390-396
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• 2024

Vision Transformer (ViT) learns relationships between patches, but it may overlook important features such as color, texture, and boundaries, which can result in performance limitations in fields like medical imaging or facial recognition. To address this issue, this study proposes the Pairwise Attention Reinforcement (PAR) model. The PAR model takes both the training image and a reference image as input into the encoder, calculates the similarity between the two images, and matches the attention score maps of images with high similarity, reinforcing the matching areas of the training image. This process emphasizes important features between images and allows even subtle differences to be distinguished. In experiments using clock-drawing test data, the PAR model achieved a Precision of 0.9516, Recall of 0.8883, F1-Score of 0.9166, and an Accuracy of 92.93%. The proposed model showed a 12% performance improvement compared to API-Net, which uses the pairwise attention approach, and demonstrated a 2% performance improvement over the ViT model.

• Journal of Digital Convergence
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• v.11 no.2
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• pp.299-307
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• 2013

The aim of this paper is the improvement of identification performance and the reduction of computational quantities in the face recognition system based on SIFT-Grid. Firstly, we propose a composition method of integrated template by removing similar SIFT keypoints and blending different keypoints in variety training images of one face class. The integrated template is made up of computation of similarity matrix and threshold-based histogram from keypoints in a same sub-region which divided by applying SIFT-Grid of training images. Secondly, we propose a computation method of similarity for identify of test image from composed integrated templates efficiently. The computation of similarity is performed that a test image to compare one-on-one with the integrated template of each face class. Then, a similarity score and a threshold-voting score calculates according to each sub-region. In the experimental results of face recognition tasks, the proposed methods is founded to be more accurate than both two other methods based on SIFT-Grid, also the computational quantities are reduce.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.1-10
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• 2007

In this paper, we propose a face recognition system using AAM and Gabor feature vectors. EBGM, which is prominent among face recognition algorithms employing Gabor feature vectors, requires localization of facial feature points where Gabor feature vectors are extracted. However, localization of facial feature points employed in EBGM is based on Gator jet similarity and is sensitive to initial points. Wrong localization of facial feature points affects face recognition rate. AAM is known to be successfully applied to localization of facial feature points. In this paper, we propose a facial feature point localization method which first roughly estimate facial feature points using AAM and refine facial feature points using Gabor jet similarity-based localization method with initial points set by the facial feature points estimated from AAM, and propose a face recognition system based on the proposed localization method. It is verified through experiments that the proposed face recognition system using the combined localization performs better than the conventional face recognition system using the Gabor similarity-based localization only like EBGM.

• The KIPS Transactions:PartA
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• v.10A no.4
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• pp.357-368
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• 2003

Evaluation of shape similarity for 3D models is essential in many areas - medicine, mechanical engineering, molecular biology, etc. Moreover, as 3D models are commonly used on the Web, many researches have been made on the classification and retrieval of 3D models. In this paper, we describe methods for 3D shape representation and major concepts of similarity evaluation, and analyze the key features of recent researches for shape comparison after classifying them into four categories including multi-resolution, topology, 2D image, and statistics based methods. In addition, we evaluated the performance of the reviewed methods by the selected criteria such as uniqueness, robustness, invariance, multi-resolution, efficiency, and comparison scope. Multi-resolution based methods have resulted in decreased computation time for comparison and increased preprocessing time. The methods using geometric and topological information were able to compare more various types of models and were robust to partial shape comparison. 2D image based methods incurred overheads in time and space complexity. Statistics based methods allowed for shape comparison without pose-normalization and showed robustness against affine transformations and noise.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.89-99
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• 2015

The World Economic Forum and the Korean Ministry of Knowledge Economy have selected big data as one of the top 10 in core information technology. The key of big data is to analyze effectively the properties that do have data. Clustering analysis method of big data techniques is a method of assigning a set of objects into the clusters so that the objects in the same cluster are more similar to each other clusters. Similarity measures being used in the cluster analysis may be classified into various types depending on the nature of the data. In this paper, we studied upper and lower bounds for binary similarity measures with negative matches such as Russel and Rao measure, simple matching measure by Sokal and Michener, Rogers and Tanimoto measure, Sokal and Sneath measure, Hamann measure, and Baroni-Urbani and Buser mesures I, II. And the comparative studies with these measures were shown by real data and simulated experiment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.153-162
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• 2012

In this paper, we propose a new shape similarity measure for M:N polygon pairs regardless of different object cardinalities in the pairs. The proposed method compares the projections of two shape functions onto Zernike polynomial basis functions, where the shape functions were obtained from each overall region of objects, thus not being affected by the cardinalities of object pairs. Moments with low-order basis functions describe global shape properties and those with high-order basis functions describe local shape properties. Therefore several moments up to a certain order where the original shapes were similarly reconstructed can efficiently describe the shape properties thus be used for shape comparison. The proposed method was applied for the building objects in the New address digital map and a car navigation map of Seoul area. Comparing to an overlapping ratio method, the proposed method's similarity is more robust to object cardinality.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.53-60
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• 2021

LOD-based recommender systems usually leverage the data available within LOD datasets, such as DBpedia, in order to recommend items(movies, books, music) to the end users. These systems use a semantic similarity algorithm that calculates the degree of matching between pairs of Linked Data resources. In this paper, we proposed a new approach to measuring semantic distance in an LOD-based recommender system by assigning weights converted from user ratings to links in the LOD graph. The semantic distance measurement model proposed in this paper is based on a processing step in which a graph is personalized to a user through weight calculation and a method of applying these weights to LDSD. The Experimental results showed that the proposed method showed higher accuracy compared to other similar methods, and it contributed to the improvement of similarity by expanding the range of semantic distance measurement of the recommender system. As future work, we aim to analyze the impact on the model using different methods of LOD-based similarity measurement.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.1
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• pp.1-9
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• 2013

This paper proposes a matching algorithm to find corresponding polygon feature sets between heterogeneous digital maps. The algorithm finds corresponding sets in terms of optimizing their shape similarities based on the assumption that the feature sets describing the same entities in the real world are represented in similar shapes. Then, by using a binary code, it is represented that a polygon feature is chosen for constituting a corresponding set or not. These codes are combined into a binary string as a candidate solution of the matching problem. Starting from initial candidate solutions, a genetic algorithm iteratively optimizes the candidate solutions until it meets a termination condition. Finally, it presents the solution with the highest similarity. The proposed method is applied for the topographical and cadastral maps of an urban region in Suwon, Korea to find corresponding polygon feature sets for block areas, and the results show its feasibility. The results were assessed with manual detection results, and showed overall accuracy of 0.946.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.269-277
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• 2006

We propose a new expert system for recovering the broken fragments of relics into an original form using computer graphics and image processing. This paper presents a system with an application to tombstones objects of flat plane with letters carved in for assembling the fragments by placing their respective fragments in the right position. The matching process contains three sub-processes: aligning the front and letters of an object, identifying the matching directions, and determining the detailed matching positions. We apply least squares fitting, vector inner product, and geometric and RGB errors to the matching process. It turned out that 2-D translations via fragments-alignment enable us to save the computational load significantly. Based on experimental results from the damaged cultural fragments, the performance of the proposed method is illustrated.