• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.512-517
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• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.4
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• pp.17-22
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• 2002

This paper used an active contour model which was based on level set algorithms and bidirectional curve evolution theory in order to track the shape of the heart acting continuously. Most active contour models would be failed in boundary extraction because of their unstable movement in the edge gap locations. In this paper, we suggest a new active contour model using only image intensity value and additional constraint needed for stable extraction. Our model was successfully run on either shape extraction or object tracking without any position constraints of initial curve. Also demonstrated stable movements and showed good results at weak or missing boundary locations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.28-35
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• 2006

Statistical models of shape variability based on active shape models (ASMs) have been successfully utilized to perform segmentation and recognition tasks in two-dimensional (2D) images. Three-dimensional (3D) model-based approaches are more promising than 2D approaches since they can bring in more realistic shape constraints for recognizing and delineating the object boundary. For 3D model-based approaches, however, building the 3D shape model from a training set of segmented instances of an object is a major challenge and currently it remains an open problem in building the 3D shape model, one essential step is to generate a point distribution model (PDM). Corresponding landmarks must be selected in all1 training shapes for generating PDM, and manual determination of landmark correspondences is very time-consuming, tedious, and error-prone. In this paper, we propose a novel automatic method for generating 3D statistical shape models. Given a set of training 3D shapes, we generate a 3D model by 1) building the mean shape fro]n the distance transform of the training shapes, 2) utilizing a tetrahedron method for automatically selecting landmarks on the mean shape, and 3) subsequently propagating these landmarks to each training shape via a distance labeling method. In this paper, we investigate the accuracy and compactness of the 3D model for the human liver built from 50 segmented individual CT data sets. The proposed method is very general without such assumptions and can be applied to other data sets.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.225-226
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• 2012

Nowadays the widely used media in architecture include visualizations, animations and three-dimensional models. An optimized 3D digital method using active CAM(Computer Aided Manufacturing) and CNC(Computerized Numerical Control) imaging is developed for accurate shape and 3D measurements in freeform buildings in this paper. In contrast to a conventional building using auto CAD system and others, the proposed active digital optimization is based on a combination of 3D numerical data and parametric 3D model. The objective of this paper is therefore to present digital optimization method for constructability of freeform building. The 3D digital optimization method is appropriate to serious variations in freeform shape. The developed digital optimization method is necessary to be carried out to verify the robustness and accuracy for constructability.

• Korean Journal of Computational Design and Engineering
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• v.12 no.5
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• pp.330-343
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• 2007

In this paper, we present a new surface content completion system that can effectively repair both shape and appearance from scanned, incomplete point set inputs. First, geometric holes can be robustly identified from noisy and defective data sets without the need for any normal or orientation information. The geometry and texture information of the holes can then be determined either automatically from the models' context, or manually from users' selection. After identifying the patch that most resembles each hole region, the geometry and texture information can be completed by warping the candidate region and gluing it onto the hole area. The displacement vector field for the exact alignment process is computed by solving a Poisson equation with boundary conditions. Out experiments show that the unified framework, founded upon the techniques of deformable models and PDE modeling, can provide a robust and elegant solution for content completion of defective, complex point surfaces.

• Journal of Powder Materials
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• v.24 no.1
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• pp.17-23
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• 2017

In this study, a finite element analysis approach is proposed to predict the fluid-structure interaction behavior of active materials for lithium-ion batteries (LIBs), which are mainly composed of graphite powder. The porous matrix of graphite powder saturated with fluid electrolyte is considered a representative volume element (RVE) model. Three different RVE models are proposed to consider the uncertainty of the powder shape and the porosity. P-wave modulus from RVE solutions are analyzed based on the microstructure and the interaction between the fluid and the graphite powder matrix. From the results, it is found that the large surface area of the active material results in low mechanical properties of LIB, which leads to poor structural durability when subjected to dynamic loads. The results obtained in this study provide useful information for predicting the mechanical safety of a battery pack.

• Smart Structures and Systems
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• v.24 no.1
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• pp.111-125
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• 2019

Metallic shape memory alloys present fascinating physical properties such as their super-elastic behavior in austenite phase, which can be exploited for providing a structure with both a self-centering capability and an increased ductility. More or less accurate numerical models have been introduced to model their behavior along the last 25 years. This is the reason for which the literature is rich of suggestions/proposals on how to implement this material in devices for passive and semi-active control. Nevertheless, the thermo-mechanical coupling characterizing the first-order martensite phase transformation process results in several macroscopic features affecting the alloy performance. In particular, the effects of day-night and winter-summer temperature excursions require special attention. This aspect might imply that the deployment of some devices should be restricted to indoor solutions. A further aspect is the dependence of the behavior from the geometry one adopts. Two fundamental lacks of symmetry should also be carefully considered when implementing a SMA-based application: the behavior in tension is different from that in compression, and the heating is easy and fast whereas the cooling is not. This manuscript focuses on the passive devices recently proposed in the literature for civil engineering applications. Based on the challenges above identified, their actual feasibility is investigated in detail and their long term performance is discussed with reference to their fatigue life. A few available semi-active solutions are also considered.

• Smart Structures and Systems
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• v.19 no.6
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• pp.633-641
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• 2017

The complexity of macro-fiber composite (MFC) materials increasing the difficulty in simulation and analysis of MFC integrated structures. To give an accurate prediction of MFC bonded smart structures for the simulation of shape and vibration control, the paper develops a linear electro-mechanically coupled static and dynamic finite element (FE) models based on the first-order shear deformation (FOSD) hypothesis. Two different types of MFCs are modeled and analyzed, namely MFC-d31 and MFC-d33, in which the former one is dominated by the $d_{31}$ effect, while the latter one by the $d_{33}$ effect. The present model is first applied to an MFC-d33 bonded composite plate, and then is used to analyze both active shape and vibration control for MFC-d31/-d33 bonded plate with various piezoelectric fiber orientations.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.829-840
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• 2013

In this paper, we propose a new lung segmentation method for chest x-ray images which can take both global and local properties into account. Firstly, the initial lung segmentation is computed by applying the active shape model (ASM) which keeps the shape of deformable model from the pre-learned model and searches the image boundaries. At the second segmentation stage, we also applied the localizing region-based active contour model (LRACM) for correcting various regional errors in the initial segmentation. Finally, to measure the similarities, we calculated the Dice coefficient of the segmented area using each semiautomatic method with the result of the manually segmented area by a radiologist. The comparison experiments were performed using 5 lung x-ray images. In our experiment, the Dice coefficient with manually segmented area was $95.33%{\pm}0.93%$ for the proposed method. Effective segmentation methods will be essential for the development of computer-aided diagnosis systems for a more accurate early diagnosis and prognosis regarding lung cancer in chest x-ray images.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.139-146
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• 2014

This paper proposes an efficient method for automatically distinguishing various facial expressions. To recognize the emotions from facial expressions, the facial images are obtained by digital cameras, and a number of feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by statistical pattern classifiers such Naive Bayes, MLP (multi-layer perceptron), and SVM (support vector machine). Based on the experimental results with 5-fold cross validation, SVM was the best among the classifiers, whose performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.