• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.714-718
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• 2009

3D reconstruction of a human face from an image sequence remains an important problem in computer vision. We propose a method, based on a factorization algorithm, that reconstructs a 3D face model from short image sequences exhibiting rotational motion. Factorization algorithms can recover structure and motion simultaneously from one image sequence, but they usually require that all feature points be well tracked. Under rotational motion, however, feature tracking often fails due to occlusion and frame out of features. Additionally, the paucity of images may make feature tracking more difficult or decrease reconstruction accuracy. The proposed 3D reconstruction approach can handle short image sequences exhibiting rotational motion wherein feature points are likely to be missing. We implement the proposal as a reconstruction method; it employs image sequence division and a feature tracking method that uses Active Appearance Models to avoid the failure of feature tracking. Experiments conducted on an image sequence of a human face demonstrate the effectiveness of the proposed method.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• Smart Media Journal
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• v.12 no.3
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• pp.30-37
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• 2023

Alzheimer's disease (AD) symptoms are being treated by early diagnosis, where we can only slow the symptoms and research is still undergoing. In consideration, using T1-weighted images several classification models are proposed in Machine learning to identify AD. In this paper, we consider the improvised feature selection, to reduce the complexity by using wrapping techniques and Restricted Boltzmann Machine (RBM). This present work used the subcortical and cortical features of 278 subjects from the ADNI dataset to identify AD and sMRI. Multi-class classification is used for the experiment i.e., AD, EMCI, LMCI, HC. The proposed feature selection consists of Forward feature selection, Backward feature selection, and Combined PCA & RBM. Forward and backward feature selection methods use an iterative method starting being no features in the forward feature selection and backward feature selection with all features included in the technique. PCA is used to reduce the dimensions and RBM is used to select the best feature without interpreting the features. We have compared the three models with PCA to analysis. The following experiment shows that combined PCA &RBM, and backward feature selection give the best accuracy with respective classification model RF i.e., 88.65, 88.56% respectively.

• IE interfaces
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• v.16 no.spc
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• pp.87-92
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• 2003

Progressive mesh representation and generation have become one of the most important issues in network-based computer graphics. However, current researches are mostly focused on triangular mesh models. On the other hand, solid models are widely used in industry and are applied to advanced applications such as product design and virtual assembly. Moreover, as the demand to share and transmit these solid models over the network is emerging, the generation and the transmission of progressive solid models depending on specific engineering needs and purpose are essential. In this paper, we present a Cellular Topology-based approach to generating and transmitting progressive solid models from a feature-based solid model for internet-based design and collaboration. The proposed approach introduces a new scheme for storing and transmitting solid models over the network. The Cellular Topology (CT) approach makes it possible to effectively generate progressive solid models and to efficiently transmit the models over the network with compact model size.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.22-34
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• 2020

Three-dimensional (3D) computer-aided design (CAD) models require different levels of detail (LODs) depending on their purpose. Therefore, it is beneficial to automatically simplify 3D CAD assembly models to meet the desired LOD. Feature-based 3D CAD assembly models typically have the lowest and highest feasible limits of LOD during simplification. In order to help users obtain a feasible simplification result, we propose a method to simplify feature-based 3D CAD assembly models by determining the lowest and highest limits of LOD. The proposed method is verified through experiments using a simplification prototype implemented as a plug-in type module on Siemens NX.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.1
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• pp.109-118
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• 2023

Data-based analysis methods have become used more for estimating or predicting housing prices, and neural network models and decision trees in the field of big data are also widely used more and more. Neural network models are often evaluated to be superior to existing statistical models in terms of estimation or prediction accuracy. However, there is ambiguity in determining the input feature of the input layer of the neural network model, that is, the type and number of input features, and decision trees are sometimes used to overcome these disadvantages. In this paper, we evaluate the existing methods of using decision trees and propose the method of using decision trees to prioritize input feature selection in neural network models. This can be a complementary or combined analysis method of the neural network model and decision tree, and the validity was confirmed by applying the proposed method to house price estimation. Through several comparisons, it has been summarized that the selection of appropriate input characteristics according to priority can increase the estimation power of the model.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.177-182
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• 2022

A deep neural network (DNN) includes variables whose values keep on changing with the training process until it reaches the final point of convergence. These variables are the co-efficient of a polynomial expression to relate to the feature extraction process. In general, DNNs work in multiple 'dimensions' depending upon the number of channels and batches accounted for training. However, after the execution of feature extraction and before entering the SoftMax or other classifier, there is a conversion of features from multiple N-dimensions to a single vector form, where 'N' represents the number of activation channels. This usually happens in a Fully connected layer (FCL) or a dense layer. This reduced 2D feature is the subject of study for our analysis. For this, we have used the FCL, so the trained weights of this FCL will be used for the weight-class correlation analysis. The popular DNN models selected for our study are ResNet-101, VGG-19, and GoogleNet. These models' weights are directly used for fine-tuning (with all trained weights initially transferred) and scratch trained (with no weights transferred). Then the comparison is done by plotting the graph of feature distribution and the final FCL weights.

• The KIPS Transactions:PartD
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• v.15D no.6
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• pp.803-814
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• 2008

Product line architectures include variable parts to be selected according to product specific requirements. In order to derive architectures that are valid for a particular product from product line architectures, variabilities of product line architectures must be systematically managed. In this paper, we adopt an approach to variability management of product line architectures through an explicit mapping between a feature model and product line architecture models. If this mapping is incorrect or there exists inconsistency among product line architectural elements, variabilities of product line architectures cannot be managed correctly. Therefore, this paper formally defines product line architectural models in terms of conceptual, process, deployment, and module views, and mapping relationships between the feature model and the architectural models. Consistency rules for correct variability management of product line architectures are defined in terms of consistency in each of product line architecture model, consistency between different architectural view models, and consistency between a feature model and product line architectural models. These consistency rules provide a theoretical foundation for deriving valid product architecture from product line architectures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.8
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• pp.3984-4005
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• 2017

Image recognition has become an increasingly important topic for its wide application. It is highly challenging when facing to large-scale database with large variance. The recognition systems rely on a key component, i.e. the low-level feature or the learned mid-level feature. The recognition performance can be potentially improved if the data distribution information is exploited using a more sophisticated way, which usually a function over hidden variable, model parameter and observed data. These methods are called generative score space. In this paper, we propose a discriminative extension for the existing generative score space methods, which exploits class label when deriving score functions for image recognition task. Specifically, we first extend the regular generative models to class conditional models over both observed variable and class label. Then, we derive the mid-level feature mapping from the extended models. At last, the derived feature mapping is embedded into a discriminative classifier for image recognition. The advantages of our proposed approach are two folds. First, the resulted methods take simple and intuitive forms which are weighted versions of existing methods, benefitting from the Bayesian inference of class label. Second, the probabilistic generative modeling allows us to exploit hidden information and is well adapt to data distribution. To validate the effectiveness of the proposed method, we cooperate our discriminative extension with three generative models for image recognition task. The experimental results validate the effectiveness of our proposed approach.

• Korean Journal of Computational Design and Engineering
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• v.10 no.4
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• pp.262-273
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• 2005

For CAD data users, few things are as frustrating as receiving CAD data that is unusable due to poor data quality. Users waste time trying to get better data, fixing the data, or even rebuilding the data from scratch from paper drawings or other sources. Most related works and commercial tools handle the boundary representation (B-Rep) shape of CAD models. However, we propose a design history?based approach for healing CAD model errors. Because the design history, which covers the features, the history tree, the parameterization data and constraints, reflects the design intent, CAD model errors can be healed by an interdependency analysis of the feature commands or of the parametric data of each feature command, and by the reconstruction of these feature commands through the rule-based reasoning of an expert system. Unlike other B Rep correction methods, our method automatically heals parametric feature models without translating them to a B-Rep shape, and it also preserves engineering information.