• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.705-711
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• 2003

An automatic face and facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in !be image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the face identification system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.12
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• pp.6009-6027
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• 2019

Support vector machine (SVM) classifiers have been widely used for object detection. These methods usually locate the object by finding the region with maximal score in an image. With bag-of-features representation, the SVM score of an image region can be written as the sum of its inside feature-weights. As a result, the searching process can be executed efficiently by using strategies such as branch-and-bound. However, the feature-weight derived by optimizing region classification cannot really reveal the category knowledge of a feature-point, which could cause bad localization. In this paper, we represent a region in an image by a collection of local feature-points and determine the object by the region with the maximum posterior probability of belonging to the object class. Based on the Bayes' theorem and Naive-Bayes assumptions, the posterior probability is reformulated as the sum of feature-scores. The feature-score is manifested in the form of the logarithm of a probability ratio. Instead of estimating the numerator and denominator probabilities separately, we readily employ the density ratio estimation techniques directly, and overcome the above limitation. Experiments on a car dataset and PASCAL VOC 2007 dataset validated the effectiveness of our method compared to the baselines. In addition, the performance can be further improved by taking advantage of the recently developed deep convolutional neural network features.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.331-335
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• 2008

In this paper, we parallelized a feature point extraction algorithm to reduce panorama image generation time in multi-core environment. While we compose a panorama image with several images, the step to extract feature points of each picture is needed to find overlapped region of pictures. To perform rapidly feature extraction stage which requires much calculation, we developed a parallel algorithm to extract feature points and examined the performance using CBE(Cell Broadband Engine) which is asymmetric multi-core architecture. As a result of the exam, the algorithm we proposed has a property of linear scalability-the performance is increased in proportion the number of processors utilized. In this paper, we will suggest how Image processing operation can make high performance result in multi-core environment.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.1
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• pp.64-75
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• 2010

This study introduces a complete feature map building method of sonar sensors for mobile robots. This method enhances the reality of feature maps by extracting even circle features as well as line and point features from sonar data. Edge features are, moreover, generated by combining line features close to circle features extracted around comer sites. The uncertainties of the specular reflection phenomenon and wide beam width of sonar data can be, therefore, reduced through this map building method. The experimental results demonstrate a practical validity of the proposed method in those environments.

• Phonetics and Speech Sciences
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• v.1 no.4
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• pp.111-117
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• 2009

In this paper, a new speech endpoint detector in noisy environment is proposed. According to the previous research, the energy feature in the speech region is easily distinguished from that in the speech absent region. In conventional method, the endpoint can be found by applying the edge detection filter that finds the abrupt changing point in feature domain. However, since the frame energy feature is unstable in noisy environment, the accurate edge detection is not possible. Therefore, in this paper, the novel feature extraction method based on spectrum envelop pattern is proposed. Then, the edge detection filter is applied to the proposed feature for detection of the endpoint. The experiments are performed in the car noise environment and a substantial improvement was obtained over the conventional method.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.3
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• pp.30-36
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• 2004

The exchange of model design data among heterogeneous CAD systems is very difficult because each CAD system has different data structures suitable for its own functions. STEP represents product information in a common computer-interpretable form that is required to remain complete and consistent when the product information is needed to be exchanged among different computer systems. However, STEP has complex architecture to represent point, line, curve and vectors of element. Moreover it can't represent geometry data of feature based models. In this study, a structure of XML document that represents geometry data of feature based models as neutral format has been developed. To use the developed XML document, a converter also has been developed to exchange modules so that it can exchange feature based data models among heterogeneous CAD systems. Developed XML document and Converter have been applied to commercial CAD systems.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.434-440
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• 2003

The exchange of model design date among heterogeneous CAD systems is a difficult task because each system has different data structures suitable for its own functions. STEP has been able to represent product information as a common computer-interpretable form that is required to remain complete and consistent when the product informant is needed to be exchanged among different computer system. However, STEP has difficult architecture in is representing point, line, curve and vectors of element, more over it can't represent geometry data of feature based models. In this study, a structure of XML document that represents geometry data of feature based models as neutral format has been developed. To use the developed XML document, a Converter has also been developed to exchange modules so that it can exchange feature based data models among heterogeneous CAD systems. Aa for evaluation of the developed XML document and Converter, Solidworks and SolidEdge are selected.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.353-358
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• 2002

A feature-based inspection planning system is proposed in this research to develop more efficient measuring methodology for the OMM (On-machine measurement) for complicated workpiece having many primitive form features. This paper focuses on the development of the CAIP (computer-aided inspection system) methodologies. The optimum inspection sequences for the features are determined by analyzing the feature information such as the nested relations and the possible probe approaching directions of the features, and forming feature groups. A series of heuristic rules are developed to accomplish it. Also, each feature is decomposed into its constituent geometric elements, and then the number of sampling points, the locations of the measuring point, the optimum probing path are determined by applying the fuzzy logic, Hammersley's method, and the TSP algorithm. To verify the proposed methodologies, simulations are carried out and the results are analyzed.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.41-48
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• 2018

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.2
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• pp.12-17
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• 2010

An automatic facial feature points(FFPs) detection system is proposed. A face is represented as a graph where the nodes are placed at facial feature points(FFPs) labeled by their Gabor features and the edges are describes their spatial relations. An innovative flexible feature matching is proposed to perform features correspondence between models and the input image. This matching model works likes random diffusion process in the image space by employing the locally competitive and globally corporative mechanism. The system works nicely on the face images under complicated background, pose variations and distorted by facial accessories. We demonstrate the benefits of our approach by its implementation on the system.