• Journal of Environmental Science International
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• v.11 no.12
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• pp.1299-1305
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• 2002

Thirty sediment samples of the <63${\mu}{\textrm}{m}$ fraction collected from the southwestern coast of Korea were analysed for their heavy metal (Fe, Mn, Cr, Co, Cu, Ni, Zn and Pb) concentration. The results show that sediment texture plays a controlling role on the total metal concentrations and their spatial distribution. A single lM HCl extraction procedure was used in order to assess the environmental risk of heavy metals in bottom sediments. The non-residual fraction was the most abundant pool for Mn and Pb in most samples, which means that this metals are highly avaliable in these sediments. Cr, Ni, Fe, Co, Zn and Cu were mainly associated with the residual fraction, suggesting that their concentrations are controlled significantly by transport processes with the fine particles as carriers from diffuse pollution source. Concentration enrichment ratios(CER) were calculated from the non-residual contents and their values allowed us to classify the sediments according to their environmental risk.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.159-162
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• 1998

Land cover and land use classification on a huge scale, e.g. national or continental scale, has become more and more important because environmental researches need land cover: And land use data on such scales. We developed a crop field extraction method, which is one of the steps in our land cover classification system for a huge area. Firstly, a crop field model is defined to characterize "crop field" in terms of NDVI value and textual information Textual information is represented by the density of straight lines which are extracted by wavelet transform. Secondly, candidates of NDVI threshold value are determined by "scale-space filtering" method. The most appropriate threshold value among the candidates is determined by evaluating the line density of the area extracted by the threshold value. Finally, the crop field is extracted by applying level slicing to Landsat TM image with the threshold value determined above. The experiment demonstrates that the extracted area by this method coincides very well with the one extracted by visual interpretation.

• Journal of Korean Society on Water Environment
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• v.24 no.4
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• pp.407-414
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• 2008

This study was conducted to evaluate the heavy metal contamination in the soils of Maehyang-ri inland shooting range area. The texture of the Maehyang-ri inland shooting range soil was sandy. Extraction of heavy metals reached quasi-equilibrium within 6 hours using shaking with 0.1 N HCl. 95% and 94% of extraction efficiency was observed for Cu and Pb in the Maehyang-ri shooting range soils, respectively. And Cu and Pb contamination of level of the T-1 region soil was $114.4{\pm}5.7mg/kg$ and $362.3{\pm}20.5mg/kg$. This may be due to the effects of mineralogical factor, soil particle size and un-residual fractions such as exchangeable, carbonate, Fe-Mn oxide and organic+sulfide.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.482-485
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• 2000

A modified watershed transform is proposed which is context-free marker-controlled and minima imposition-free to reduce the over-segmentation and to speedup the transform. In contrast to the conventional methods in which a priori knowledge, such as flat zones, zones of homogeneous texture, and morphological distance, is required for marker extraction, context-free marker extraction is proposed by using the attention operator based on the GST (generalized symmetry transform). By using the context-free marker, the proposed watershed transform exploit marker-constrained labeling to speedup the computation and to reduce the over-segmentation by eliminating the unnecessary geodesic reconstruction such as the minima imposition and thereby eliminating the necessity of the post-processing of region merging. The simulation results show that the proposed method can extract context-free markers inside the objects from the complex background that includes multiple objects and efficiently reduces over-segmentation and computation time.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.248-250
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• 2003

An automatic approach and strategy for extracting building information from aerial images using combined image analysis and interpretation techniques is described in this paper. A dense DSM is obtained by stereo image matching. Multi-band classification, DSM, texture segmentation and Normalised Difference Vegetation Index (NDVI) are used to reveal building interest areas. Then, based on the derived approximate building areas, a shape modelling algorithm based on the level set formulation of curve and surface motion has been used to precisely delineate the building boundaries. Data fusion, based on the Dempster-Shafer technique, is used to interpret simultaneously knowledge from several data sources of the same region, to find the intersection of propositions on extracted information derived from several datasets, together with their associated probabilities. A number of test areas, which include buildings with different sizes, shape and roof colour have been investigated. The tests are encouraging and demonstrate that the system is effective for building extraction, and the determination of more accurate elevations of the terrain surface.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1138-1156
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• 2021

Emotional semantics are the highest level of semantics that can be extracted from an image. Constructing a system that can automatically recognize the emotional semantics from images will be significant for marketing, smart healthcare, and deep human-computer interaction. To understand the direction of image emotion recognition as well as the general research methods, we summarize the current development trends and shed light on potential future research. The primary contributions of this paper are as follows. We investigate the color, texture, shape and contour features used for emotional semantics extraction. We establish two models that map images into emotional space and introduce in detail the various processes in the image emotional semantic recognition framework. We also discuss important datasets and useful applications in the field such as garment image and image retrieval. We conclude with a brief discussion about future research trends.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.260-270
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• 2023

We introduce a novel Visual Inertial Odometry (VIO) algorithm designed to improve the performance of thermal-inertial odometry. Thermal infrared image, though advantageous for feature extraction in low-light conditions, typically suffers from a high noise level and significant information loss during the 8-bit conversion. Our algorithm overcomes these limitations by approximating a 14-bit raw pixel histogram into a Gaussian mixture model. The conversion method effectively emphasizes image regions where texture for visual tracking is abundant while reduces unnecessary background information. We incorporate the robust learning-based feature extraction and matching methods, SuperPoint and SuperGlue, and zero velocity detection module to further reduce the uncertainty of visual odometry. Tested across various datasets, the proposed algorithm shows improved performance compared to other state-of-the-art VIO algorithms, paving the way for robust thermal-inertial odometry.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.761-764
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• 1998

본 논문에서는 CCCD 카메라로부터 획득한 영상시퀀스들에서 인접한 두 영상 사이의 차영상과 얼굴이 가지는 컬러정보를 이용하여 분리한 얼굴 영역에서 양쪽 눈과 입의 위치좌표를 특징점으로 이용하여 입력된 얼굴에 자동정합된 변형가능 템플리트(deformable template)를 가지고 연속된 다음 프레임에서 얼굴 전체를 트래킹하는 알고리듬을 제안한다. 실제 입력영상의 얼굴 영역과 변형 가능 템플리트의 차이를 비교하기 위해 텍스쳐 매핑(Texture mapping)을 도입하여 트래킹의 정확도를 살펴본다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.505-507
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• 2001

최근 여러 멀티미디어 서비스가 활발히 실시되고 있으며 멀티미디어 검색분야도 상당한 연구가 이루어지고 있다. 멀티미디어 검색 중 내용 기반 검색은 기존의 텍스트기반의 여러 단점들을 극복하여 이미지 자체에 있는 여러 정보의 혼합으로 보다 더 정확한 이미지를 찾을 수 있다. 예를 들면, 색상검색이나 질감검색을 이미지 자체내에서 추출해내고 색상과 질감을 같이 표현함으로써 색상만으로 표현할 수 없는 부분을 질감을 참고로 하여 찾을 수 있다. 본 논문에서는 웨이브릿 변환(daubechies 7-9 tab)을 사용하여 질감을 표현하는 특징 추출하는 방법을 제안하고자 한다.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.342-347
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• 1998

An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.