• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.19-29
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• 2003

This paper presents a novel hybrid classifier for face recognition with artificially generated virtual training samples. We utilize both the nearest neighbor approach in feature angle space and a connectionist model to obtain a synergy effect by combining the results of two heterogeneous classifiers. First, a classifier called the nearest feature angle (NFA), based on angular information, finds the most similar feature to the query from a given training set. Second, a classifier has been developed based on the recall of stored frontal projection of the query feature. It uses a frontal recall network (FRN) that finds the most similar frontal one among the stored frontal feature set. For FRN, we used an ensemble neural network consisting of multiple multiplayer perceptrons (MLPs), each of which is trained independently to enhance generalization capability. Further, both classifiers used the virtual training set generated adaptively, according to the spatial distribution of each person's training samples. Finally, the results of the two classifiers are combined to comprise the best matching class, and a corresponding similarit measure is used to make the final decision. The proposed classifier achieved an average classification rate of 96.33% against a large group of different test sets of images, and its average error rate is 61.5% that of the nearest feature line (NFL) method, and achieves a more robust classification performance.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.401-408
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• 2013

In this paper, we propose a method that automatically detects organs having similar appearances in medical images by learning both context and appearance features. Since only the appearance feature is used to learn the classifier in most existing detection methods, detection errors occur when the medical images include multiple organs having similar appearances. In the proposed method, based on the probabilities acquired by the appearance-based classifier, new classifier containing the context feature is created by iteratively learning the characteristics of probability distribution around the interest voxel. Furthermore, both the efficiency and the accuracy are improved through 'region based voting scheme' in test stage. To evaluate the performance of the proposed method, we detect femur and tibia which have similar appearance from SKI10 knee joint dataset. The proposed method outperformed the detection method only using appearance feature in aspect of overall detection performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.75-85
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• 2010

This paper presents a robust 3D model hashing based on object feature vector for 3D content authentication. The proposed 3D model hashing selects the feature objects with highest area in a 3D model with various objects and groups the distances of the normalized vertices in the feature objects. Then we permute groups in each objects by using a permutation key and generate the final binary hash through the binary process with the group coefficients and a random key. Therefore, the hash robustness can be improved by the group coefficient from the distance distribution of vertices in each object group and th hash uniqueness can be improved by the binary process with a permutation key and a random key. From experimental results, we verified that the proposed hashing has both the robustness against various mesh and geometric editing and the uniqueness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.12
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• pp.2844-2851
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• 2015

How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.38.1-38.1
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• 2008

Tides caused by the Galactic gravitational field affect the current dynamical structure of globular clusters in the Galaxy. Indeed, the observed feature of tidal tails stretching beyond globular clusters' tidal radii provides a key information of interaction with the gravitational field of the Galaxy and kinematical orbit of the clusters, which can be an evidence of the merging scenario of the Galaxy formation and evolution. To find such a tidal feature, we have studied spatial density distribution of stars around five globular clusters in the Galactic halo and one cluster in the Galactic bulge, for which we have used wide-field deep photometric data of gri and JHK bands obtained from the MegaCam and WIRCam of the CFHT. Applying the statistical contrast filtering of field stars in the color-magnitude plane of detected stars around five halo clusters, we have found features of tidal tails for four clusters M53, M15, NGC 5053, and NGC 5466. The detected over-density tidal features are well aligned with the cluster's orbits and stretched into the direction of the Galactic center. Statistical analysis indicate that these tidal tails are believed to be cluster stars that have escaped due to the tidal effects to the clusters. A similar tidal feature to that of halo clusters is also detected for the bulge cluster NGC 6626, while the over-density feature seems to be extended into the Galactic plane rather than into the orbital direction and the Galactic center. Conclusively, our result adds further observational evidence of the merging scenario of the Galaxy formation and evolution.

• Journal of the Korean Society for information Management
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• v.19 no.3
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• pp.5-30
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• 2002

In this paper, classification feature is selected with focus of musical content, note sequences pattern, and measures similarity between note sequences followed by constructing clusters by similar note sequences, which is easier for users to search by showing the similar note sequences with the search result in the CBMR system. Experimental document was $\ulcorner$A Dictionary of Musical Themes$\lrcorner$, the index of theme bar focused on classical music and obtained kern-type file. Humdrum Toolkit version 1.0 was used as note sequences treat tool. The hierarchical clustering method is by stages focused on four-type similarity matrices by whether the note sequences segmentation or not and where the starting point is. For the measurement of the result, WACS standard is used in the case of being manual classification and in the case of the note sequences starling from any point in the note sequences, there is used common feature pattern distribution in the cluster obtained from the clustering result. According to the result, clustering with segmented feature unconnected with the starting point Is higher with distinct difference compared with clustering with non-segmented feature.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.37-48
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• 2005

In this paper, a new object detection method for FLIR images is proposed. The proposed method consists of intensity ranking feature and a classification algerian using the feature. The intensity ranking feature is a representation of an image, from which intensity distribution is regularized. Each object candidate region is classified as object or non-object by the proposed classification algorithm which is based on the intensity ranking similarity between the candidate and object training images. Using the proposed algorithm pixel-wise detection results can be obtained without any additional candidate selection algorithm. In experimental results, it is shown that the proposed ranking feature is appropriate for object detection in a FLIR image and some vehicle detection results in the situation of existing noise, scale variation, and rotation of the objects are presented.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.381-391
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• 2001

This paper presents an efficient approach to build an image mosaics from image sequences. Unlike general panoramic stitching methods, which usually require some geometrical feature points or solve the iterative nonlinear equations, our algorithm can directly recover the 8-parameter planar perspective transforms. We use four quasi-feature points in order to compute the projective transform between two images. This feature is based on the graylevel distribution and defined in the overlap area between two images. Therefore the proposed algorithm can reduce the total amount of the computation. We also present an algorithm lot efficiently matching the correspondence of the extracted feature. The proposed algorithm is applied to various images to estimate its performance and. the simulation results present that our algorithm can find the correct correspondence and build an image mosaics.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1540-1549
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• 2017

Objective: Increasing food safety demands in the animal product market have created a need for a system to trace the food distribution process, from the manufacturer to the retailer, and genetic traceability is an effective method to trace the origin of animal products. In this study, we successfully achieved the farm tracing of 6,018 multi-breed pigs, using single nucleotide polymorphism (SNP) markers strictly selected through least absolute shrinkage and selection operator (LASSO) feature selection. Methods: We performed farm tracing of domesticated pig (Sus scrofa) from SNP markers and selected the most relevant features for accurate prediction. Considering multi-breed composition of our data, we performed feature selection using LASSO penalization on 4,002 SNPs that are shared between breeds, which also includes 179 SNPs with small between-breed difference. The 100 highest-scored features were extracted from iterative simulations and then evaluated using machine-leaning based classifiers. Results: We selected 1,341 SNPs from over 45,000 SNPs through iterative LASSO feature selection, to minimize between-breed differences. We subsequently selected 100 highest-scored SNPs from iterative scoring, and observed high statistical measures in classification of breeding farms by cross-validation only using these SNPs. Conclusion: The study represents a successful application of LASSO feature selection on multi-breed pig SNP data to trace the farm information, which provides a valuable method and possibility for further researches on genetic traceability.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.158-166
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• 2002

In this paper, we proposed a contour shape description method which use the CFR(contour fluctuation ratio) feature. The CFR is the ratio of the line length to the curve length of a contour segment. The line length means the distance of two end points on a contour segment, and the curve length means the sum of distance of all adjacent two points on a contour segment. We should acquire rotation and scale invariant contour segments because each CFR is computed from contour segments. By using the interleaved contour segment of which length is proportion to the entire contour length and which is generated from all the points on contour, we could acquire rotation and scale invariant contour segments. The CFR can describes the local or global feature of contour shape according to the unit length of contour segment. Therefore we describe the shape of objects with the feature vector which represents the distribution of CFRs, and calculate the similarity by comparing the feature vector of corresponding unit length segments. We implemented the proposed method and experimented with rotated and scaled 165 fish images of fifteen types. The experimental result shows that the proposed method is not only invariant to rotation and scale but also superior to NCCH and TRP method in the clustering power.