• Information Systems Review
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• v.25 no.1
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• pp.47-73
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• 2023

Management decision-making based on artificial intelligence(AI) plays an important role in helping decision-makers. Business decision-making centered on AI is evaluated as a driving force for corporate growth. AI-based on accurate analysis techniques could support decision-makers in making high-quality decisions. This study proposes an effective decision-making method with the application of multi-label feature selection. In this regard, We present a CFS-BR (Correlation-based Feature Selection based on Binary Relevance approach) that reduces data sets in high-dimensional space. As a result of analyzing sample data and empirical data, CFS-BR can support efficient decision-making by selecting the best combination of meaningful attributes based on the Best-First algorithm. In addition, compared to the previous multi-label feature selection method, CFS-BR is useful for increasing the effectiveness of decision-making, as its accuracy is higher.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.113-115
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• 2005

This thesis presents a method for 3D modeling of facial expression from frontal and mirror-reflected multi-image. Since the proposed system uses only one camera, two mirrors, and simple mirror's property, it is robust, accurate and inexpensive. In addition, we can avoid the problem of synchronization between data among different cameras. Mirrors located near one's cheeks can reflect the side views of markers on one's face. To optimize our system, we must select feature points of face intimately associated with human's emotions. Therefore we refer to the FDP (Facial Definition Parameters) and FAP (Facial Animation Parameters) defined by MPEG-4 SNHC (Synlhetic/Natural Hybrid Coding). We put colorful dot markers on selected feature points of face to detect movement of facial deformation when subject makes variety expressions. Before computing the 3D coordinates of extracted facial feature points, we properly grouped these points according to relative part. This makes our matching process automatically. We experiment on about twenty koreans the subject of our experiment in their late twenties and early thirties. Finally, we verify the performance of the proposed method tv simulating an animation of 3D facial expression.

• International Journal of CAD/CAM
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• v.9 no.1
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• pp.37-46
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• 2010

Existing methods for the registration of blurred images are efficient for the artificially blurred images or a planar registration, but not suitable for the naturally blurred images existing in the real image mosaic process. In this paper, we attempt to resolve this problem and propose a method for a distortion-free stitching of naturally blurred images for image mosaic. It adopts a multi-resolution and robust feature based inter-layer mosaic together. In each layer, Harris corner detector is chosen to effectively detect features and RANSAC is used to find reliable matches for further calibration as well as an initial homography as the initial motion of next layer. Simplex and subspace trust region methods are used consequently to estimate the stable focal length and rotation matrix through the transformation property of feature matches. In order to stitch multiple images together, an iterative registration strategy is also adopted to estimate the focal length of each image. Experimental results demonstrate the performance of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2787-2800
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• 2022

The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.323-329
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• 2015

In this paper we applied pattern recognition approach to detect audio forgery. Classification of the microphone types and models can help determining the authenticity of the recordings. Canonical correlation analysis was applied to extract feature for microphone classification. We utilized the linear dependence between two near-silence regions. To utilize the advantage of multi-feature based canonical correlation analysis, we selected three commonly used features to capture the temporal and spectral characteristics. Using three different microphones, we tested the usefulness of multi-feature based characteristics of canonical correlation analysis and compared the results with single feature based method. The performance of classification rate was carried out using the backpropagation neural network. Experimental results show the promise of canonical correlation features for microphone classification.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.37-41
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• 2014

The vector Taylor series (VTS) based method usually employs clean speech Hidden Markov Models (HMMs) when compensating speech feature vectors or adapting the parameters of trained HMMs. It is well-known that noisy speech HMMs trained by the Multi-condition TRaining (MTR) and the Multi-Model-based Speech Recognition framework (MMSR) method perform better than the clean speech HMM in noisy speech recognition. In this paper, we propose a method to use the noise-adapted HMMs in the VTS-based speech feature compensation method. We derived a novel mathematical relation between the train and the test noisy speech feature vector in the log-spectrum domain and the VTS is used to estimate the statistics of the test noisy speech. An iterative EM algorithm is used to estimate train noisy speech from the test noisy speech along with noise parameters. The proposed method was applied to the noise-adapted HMMs trained by the MTR and MMSR and could reduce the relative word error rate significantly in the noisy speech recognition experiments on the Aurora 2 database.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.833-835
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• 2002

In this paper, we propose multi-resolutional representation of B-rep solid models using the selective Boolean operations on non-manifold geometric models. Since the union and subtraction operations of the selective Boolean operations are commutative, the integrity of the model is guaranteed for reordering design features. A multi-resolution representation is established using a non-manifold merged set model and a feature modeling tree reordered according to some criterion of level of detail (LOD). Then, a solid model for a specified LOD can be extracted from this multi-resolution model using the selective Boolean operations.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.4
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• pp.524-531
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• 2009

In this paper, we propose an automatic image registration method for multi-sensor image fusion such as visible and infrared images. The registration is achieved by finding corresponding feature points in both input images. In general, the global statistical correlation is not guaranteed between multi-sensor images, which bring out difficulties on the image registration for multi-sensor images. To cope with this problem, mutual information is adopted to measure correspondence of features and to select faithful points. An update algorithm for projective transform is also proposed. Experimental results show that the proposed method provides robust and accurate registration results.

• MALSORI
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• no.63
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• pp.113-124
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• 2007

In this paper, we propose a use of confidence vector as an intermediate input feature for multi-stage based speech recognition architecture to improve recognition accuracy. A multi-stage speech recognition structure is introduced as a method to reduce the computational complexity of the decoding procedure and then accomplish faster speech recognition. Conventional multi-stage speech recognition is usually composed of three stages, acoustic search, lexical search, and acoustic re-scoring. In this paper, we focus on improving the accuracy of the lexical decoding by introducing a confidence vector as an input feature instead of phoneme which was used typically. We take experimental results on 220K Korean Point-of-Interest (POI) domain and the experimental results show that the proposed method contributes on improving accuracy.

• Journal of Korea Multimedia Society
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• v.24 no.1
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• pp.1-12
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• 2021

In this paper, we present a new architecture for semantic segmentation. Semantic segmentation aims at a pixel-wise classification which is important to fully understand images. Previous semantic segmentation networks use features of multi-layers in the encoder to predict final results. However, they do not contain various receptive fields in the multi-layers features, which easily lead to inaccurate results for boundaries between different classes and small objects. To solve this problem, we propose a multi-path feature fusion module that allows for features of each layers to contain various receptive fields by use of a set of dilated convolutions with different dilatation rates. Various experiments demonstrate that our method outperforms previous methods in terms of mean intersection over unit (mIoU).