• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.364-367
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• 1994

In this paper, we carried out some experiments on the Korean vowel recognition using the fractal dimension of the speech signals. We chose the Mincowski-Bouligand dimensioni as the fractal dimension, and computed it using the morphological covering method. For our experiments, we used both the fractal dimension and the LPC cepstrum which is conventionally known to be one of the best parameters for speech recognition, and examined the usefulness of the fractal dimension. From the vowel recognition experiments under various consonant contexts, we achieved the vowel recognition error rats of 5.6% and 3.2% for the case with only LPC cepstrum and that with both LPC cepstrum and the fractal dimension, respectively. The results indicate that the incorporation of the fractal dimension with LPC cepstrum gies more than 40% reduction in recognition errors, and indicates that the fractal dimension is a useful feature parameter for speech recognition.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.533-535
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• 2006

In this paper, we experiment a face recognition rate of the shaded faces except to low dimension feature vectors; first, second, third dimension. It is known to robust the face recognition against illumination. But, it isn't obvious what is effect to recognition in terms of low dimension. We are analysis to the effect of low dimension(first, second, third dimension, and combination of these) under the shaded faces.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.65-70
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• 2013

Recently, the field of computer vision has been actively researched through digital image which can be easily generated as the development and expansion of digital camera technology. Especially, research that extracts and utilizes the feature in image has been actively carried out. The image stitching is a method that creates the high resolution image using features extract and match. Image stitching can be widely used in military and medical purposes as well as in variety fields of real life. In this paper, we have proposed efficient image stitching method using fast feature descriptor extraction and matching based on SURF algorithm. It can be accurately, and quickly found matching point by reduction of dimension of feature descriptor. The feature descriptor is generated by classifying of unnecessary minutiae in extracted features. To reduce the computational time and efficient match feature, we have reduced dimension of the descriptor and expanded orientation window. In our results, the processing time of feature matching and image stitching are faster than previous algorithms, and also that method can make natural-looking stitched image.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.199-231
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• 2010

This study tried to contrast the relation-based and the dimension-based explanations and to suggest its implications on the noun-noun conceptual combination. In experiment 1, we investigated whether the dimension-based approach and intra-conceptual explanation can explain both thematic relational and property interpretations of conceptual combinations based upon the intrinsic and extrinsic features of constituent concepts. We defined intrinsic(or extrinsic) concepts according to the degree of dependency on intrinsic(or extrinsic) features. Property interpretation was facilitated when modifiers were the intrinsic concepts. This result implies that processing of conceptual combination can be influenced by the structures and information of constituent concepts. In experiment 2, exocentricity of the concepts used in Gagne(2000) was examined to reanalyze her data according to the dimension-based approach. The exocentricity was higher when the concepts were combined by their relational connections. Results of experiment 1 and 2 suggest the possibility that both approaches can be integrated through the diversities of information involved during interpreting conceptual combination. Implications and future directions of this study were discussed.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.518-523
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• 2001

This paper proposes the new method which is adopted in extracting character features and recognizing characters using fractal dimension of the Chaos theory which highly recolonizes a minute difference with strange attractor created from Henon system. This paper implements a high precision character recognition system. firstly, it gets features of mesh, projection and cross distance feature from character images. And their feature is converted into data of time series. Then using modified Henon system suggested in this paper, each characters attractor about standard Korean Character, KSC 5601 is reconstructed. Secondly, in order to analyze the Chaotic degree of each characters attractor, it gets last features of character image after calculating box-counting Dimension, Natural Measure, Information Bit, Information Dimension which are meant fractal dimension. An experimental result shows 97.49% character classification rates for 2350 Korean characters using proposed method in this paper.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.1
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• pp.33-40
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• 2012

This paper proposes a method to implement biological early warning systems(BEWS). This system generates periodically data event using a monitoring daemon and it extracts the feature parameters from this data sets. The feature parameters are derived with 6 variables, x/y coordinates, distance, absolute distance, angle, and fractal dimension. Specially by using the fractal dimension theory, the proposed algorithm define the input features represent the organism characteristics in non-toxic or toxic environment. And to find a moderate algorithm for learning the extracted feature data, the system uses an extended learning algorithm(UChoo) popularly used in machine learning. And this algorithm includes a learning method with the extended data expression to overcome the BEWS environment which the feature sets added periodically by a monitoring daemon. In this algorithm, decision tree classifier define class distribution information using the weight parameter in the extended data expression. Experimental results show that the proposed BEWS is available for environmental toxicity detection.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1140-1148
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• 1994

In this paper, we carried out some experiments on the Korean vowel recognition using the fractal dimension of the speech signals. We chose the Minkowski-Bouligand dimension as the fractal dimension, and computed it using the morphological covering method. For our experiments, we used both the fractal dimension and the LPC cepstrum which is conventionally known to be one of the best parameters for speech recognition, and examined the usefulness of the fractal dimension. From the vowel recognition experiments under various consonant contexts, we achieved the vowel recognition error rates of 5.6% and 3.2% for the case with only LPC cepstrum and that with both LPC cepstrum and the fractal dimension, respectively. The results indicate that the incorporation of the fractal dimension with LPC cepstrum gives more than 40% reduction in recognition errors, and indicates that the fractal dimension is a useful feature parameter for speech recognition.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.77-80
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• 1991

This paper proposes a new feature extraction method which is effectively used in character recognition, and validate the effectiveness through various computational methods for similiarity degree. To get feature vectors used in this method, Hough transform is applied to character image, which is used for edge extraction in image processing. By that transformation technique, strokes could be extracted and feature vectors constructed suitably. The characteristic of this method is solving the difficulties in stroke extraction through transform space analysis, which is induced by noise and blurring, and representing high recognition rate 99.3% within 10 candidates in relative low dimension.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.294-297
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• 1996

This paper proposes the method to recognize of time series data based on the chaotic feature extraction. Features extract from time series data using the chaotic time series data analysis and the pattern recognition process is using a neural network classifier. In experiment, EEG(electroencephalograph) signals are extracted features by correlation dimension and Lyapunov experiments, and these features are classified by multilayer perceptron neural networks. Proposed chaotic feature extraction enhances recognition results from chaotic time series data.