• Title/Summary/Keyword: focus-feature

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A Study on Feature Extraction Using High-Resolution Satellite Image Data (고해상도 위성 영상데이터를 이용한 지형요소 추출에 관한 연구)

  • 김상철;신석효;안기원;이건기;서두천
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2003.04a
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    • pp.181-185
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    • 2003
  • Recently, in accordance with supplying high-resolution satellite images which as IKONOS, KVR-1000, and Quick Bird, the use of satellite images have increased in the study which extraction of features from high-resolution satellite images is becoming a new research focus. In this study, using generally involves such as image segmentation, filtering and sobel operator and thinning in image processing for extraction of feature from satellite image. We apply this method to extraction of feature which need to the revision of map from high-resolution IKONOS satellite image data, we verified the capability of extraction of feature and application using satellite image and proposed a plan for the study in the future.

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Spatial Information Based Simulator for User Experience's Optimization

  • Bang, Green;Ko, Ilju
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.3
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    • pp.97-104
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    • 2016
  • In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

A Prosodic Study of Focus in English Relative Sentences (영어 관계사 문장의 초점에 관한 운율 연구)

  • Ahn, Gil-Soon;Jeon, Pyung-Man;Kim, Hyun-Gee
    • Speech Sciences
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    • v.8 no.4
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    • pp.207-214
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    • 2001
  • This study describes the focus in nine structure types of English relative clauses (SS, SO, SP, PS, PO, PP, OS, OO, OP), classified according to the grammatical role of both the head that the relative clause modifies and the gap within the relative clause. The informants for this study are 2 middle school students, 4 high school students in four formal classroom in Korea and 2 native speakers. To obtain the accurate intonation patterns, Visi-Pitch II Model 3300 was used for data analyses. Major findings are as follows: (1) The feature of the intonation in English relative clauses showed prosodic prominence at the head, but the English learners in Korea didn't show prosodic prominence; (2) the fact that all heads have prosodic prominence says that the head in relative clauses has prosodic focus; (3) in the fact that the English learners have flat pitch in the whole sentences, the problem of intonation education is found out.

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Semantic Segmentation of Agricultural Crop Multispectral Image Using Feature Fusion (특징 융합을 이용한 농작물 다중 분광 이미지의 의미론적 분할)

  • Jun-Ryeol Moon;Sung-Jun Park;Joong-Hwan Baek
    • Journal of Advanced Navigation Technology
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    • v.28 no.2
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    • pp.238-245
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    • 2024
  • In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

  • Yeom, Ha-Neul;Hwang, Myunggwon;Hwang, Mi-Nyeong;Jung, Hanmin
    • Journal of Information Science Theory and Practice
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    • v.2 no.3
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    • pp.29-39
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    • 2014
  • In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.

Incorporation of IMM-based Feature Compensation and Uncertainty Decoding (IMM 기반 특징 보상 기법과 불확실성 디코딩의 결합)

  • Kang, Shin-Jae;Han, Chang-Woo;Kwon, Ki-Soo;Kim, Nam-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37 no.6C
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    • pp.492-496
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    • 2012
  • This paper presents a decoding technique for speech recognition using uncertainty information from feature compensation method to improve the speech recognition performance in the low SNR condition. Traditional feature compensation algorithms have difficulty in estimating clean feature parameters in adverse environment. Those algorithms focus on the point estimation of desired features. The point estimation of feature compensation method degrades speech recognition performance when incorrectly estimated features enter into the decoder of speech recognition. In this paper, we apply the uncertainty information from well-known feature compensation method, such as IMM, to the recognition engine. Applied technique shows better performance in the Aurora-2 DB.

Speaker Adaptation using ICA-based Feature Transformation (ICA 기반의 특징변환을 이용한 화자적응)

  • Park ManSoo;Kim Hoi-Rin
    • MALSORI
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    • no.43
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    • pp.127-136
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    • 2002
  • The speaker adaptation technique is generally used to reduce the speaker difference in speech recognition. In this work, we focus on the features fitted to a linear regression-based speaker adaptation. These are obtained by feature transformation based on independent component analysis (ICA), and the transformation matrix is learned from a speaker independent training data. When the amount of data is small, however, it is necessary to adjust the ICA-based transformation matrix estimated from a new speaker utterance. To cope with this problem, we propose a smoothing method: through a linear interpolation between the speaker-independent (SI) feature transformation matrix and the speaker-dependent (SD) feature transformation matrix. We observed that the proposed technique is effective to adaptation performance.

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A Study on Feature-Based Visual Servoing Control of Robot System by Utilizing Redundant Feature

  • Han, Sung-Hyun;Hideki Hashimoto
    • Journal of Mechanical Science and Technology
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    • v.16 no.6
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    • pp.762-769
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    • 2002
  • This paper presents how effective it is to use many features for improving the speed and accuracy of visual servo systems. Some rank conditions which relate the image Jacobian to the control performance are derived. The focus is to describe that the accuracy of the camera position control in the world coordinate system is increased by utilizing redundant features in this paper. It is also proven that the accuracy is improved by increasing the number of features involved. Effectiveness of the redundant features is evaluated by the smallest singular value of the image Jacobian which is closely related to the accuracy with respect to the world coordinate system. Usefulness of the redundant features is verified by the real time experiments on a Dual-Arm robot manipulator made by Samsung Electronic Co. Ltd..

The Variation of Prosody by Focus (의미의 강조에 의한 운율특징 -음향음성학적 관점에 의한 분석-)

  • Kim Seonhi
    • MALSORI
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    • no.40
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    • pp.51-63
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    • 2000
  • There are sentences where sentence stress is imposed on a specific word. These sentences are called 'focused sentences'. The purpose of this paper is to investigate the variation of pitch, duration, amplitude in focused words. It is noted that pitch of a focused word is higher than that of unfocused words irrespective of the accentual pattern, and that contour tones such as HL or LH are realized longer when these tones appear in focused words. Not only the noun but also the following particle like '-boda' is higher when these words are in focus. Hence pitch is proved to be the most salient prosodic feature of the focused sentence.

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