• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.610-614
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• 2006

In this paper the advanced method of geodesic active contours was developed for the task of building detection from aerial and satellite images. Automatic extraction of man-made structures including buildings, building blocks or roads from remote sensing data is useful for land use mapping, scene understanding, robotic navigation, image retrieval, surveillance, emergency management procedures, cadastral etc. A level set method based on a region-driven segmentation model was implemented with which building boundaries were detected, through this curve propagation technique. The essence of this approach is to optimize the position and the geometric form of the curve by measuring information along that curve, and within the regions that compose the image partition. To this end, one can consider uniform intensities inside objects and the background. Thus, given an initial position of the curve, one can determine global, region-driven functions and provide a statistical description of the inside and outside object area. The calculus of variations and a gradient descent method was used to optimize the variational functional by an iterative steady state process. Experimental results demonstrate the potential of the proposed processing scheme.

• 전자공학회논문지SC
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• 제48권5호
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• pp.25-30
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• 2011

본 논문은 교통 감시를 수행하는 고정 카메라에서, 움직이는 물체들의 궤적을 사용자가 입력한 사용자 지정 경로를 바탕으로 그 정상/비정상성을 판별하는 방법을 제안한다. 제안된 방법은 입력된 경로 정보를 미리 정해진 규칙에 따라 각각의 이동 물체에 대한 비정상성(abnormality)을 계산하고 이를 임계값(Threshold)과 비교하여 비정상 행위를 판별해낸다. 사용자의 경로 정보 입력 기능을 이용하기 때문에 기존의 방법들에서 사용한, 계산량과 시간 소모가 크며 학습 데이터에 의해 그 성능이 크게 영향을 받는 정상 행위 (normal behavior) 모델링 단계를 배제하여 보다 빠르고 정확한 판별 결과를 제공한다. 뿐만 아니라 단순히 지정된 규칙만을 이용하지 않고 주어진 환경에 따라 규칙을 변형 적용하여 보다 강인한 판별 결과를 제공한다. 실험 결과는 본 논문에서 제안한 방법이 각종 교통 상황에서 발생하는 불법 및 비정상 교통 행위를 강인하게 판별해 냄을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.378-390
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• 2015

Image projectors can turn any surface into a display. Integrating a surface projection with a user interface transforms it into an interactive display with many possible applications. Hand gesture interfaces are often used with projector-camera systems. Hand detection through color image processing is affected by the surrounding environment. The lack of illumination and color details greatly influences the detection process and drops the recognition success rate. In addition, there can be interference from the projection system itself due to image projection. In order to overcome these problems, a gesture interface based on depth images is proposed for projected surfaces. In this paper, a depth camera is used for hand recognition and for effectively extracting the area of the hand from the scene. A hand detection and finger tracking method based on depth images is proposed. Based on the proposed method, a touch interface for the projected surface is implemented and evaluated.

• 대한바이러스학회지
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• 제27권2호
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• pp.137-141
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• 1997

We evaluated Immunochromatographic assay kit to screen HBsAg in human serum. When the reference HBsAg was applyed to ICA, HA and EIA kits, the limit of detection for HBsAg were found out to be 4, 2 and 0.25 ng/ml respectively. But ICA kit required 5 minutes to read the result whereas HA and EIA kit more than one hour. The sensitivity was 97% (29 of 30 samples) and the specificity 100% (45 samples) compared with conventional EIA. The ICA kit needs no instrument or machine to perform the test contrary to the conventional methods. Therefore, this rapid and sensitive ICA kit can be used for HBsAg-screening, especially in the emergency room and in the scene of the accident.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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• pp.302-320
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• 2017

In this paper, an innovative robust feature detection and matching strategy for visual odometry based on stereo image sequence is proposed. First, a sparse multiscale 2D local invariant feature detection and description algorithm AKAZE is adopted to extract the interest points. A robust feature matching strategy is introduced to match AKAZE descriptors. In order to remove the outliers which are mismatched features or on dynamic objects, an improved random sample consensus outlier rejection scheme is presented. Thus the proposed method can be applied to dynamic environment. Then, geometric constraints are incorporated into the motion estimation without time-consuming 3-dimensional scene reconstruction. Last, an iterated sigma point Kalman Filter is adopted to refine the motion results. The presented ego-motion scheme is applied to benchmark datasets and compared with state-of-the-art approaches with data captured on campus in a considerably cluttered environment, where the superiorities are proved.

• 센서학회지
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• 제21권3호
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• pp.211-216
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• 2012

Fires are the most common disaster and early fire detection is of great importance to minimize the consequent damage. Simple sensors including smoke detectors are widely used for the purpose but they are able to sense fires only at close proximity. Recently, due to the rapid advances of relevant technologies, vision-based fire sensing has attracted growing attention. In this paper, a novel visual sensing technique to automatically detect fire is presented. The proposed technique consists of multiple steps of image processing: pixel-level, block-level, and frame level. At the first step, fire flame pixel candidates are selected based on their color values in YIQ space from the image of a camera which is installed as a vision sensor at a fire scene. At the second step, the dynamic parts of flames are extracted by comparing two consecutive images. These parts are then represented in regularly divided image blocks to reduce pixel-level detection error and simplify following processing. Finally, the temporal change of the detected blocks is analyzed to confirm the spread of fire. The proposed technique was tested using real fire images and it worked quite reliably.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2009년도 춘계학술대회
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• pp.219-222
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• 2009

Rapid object recognition is one of the main stream research themes focusing to reveal how human recognizes object and interacts with environment in natural world. This field of study is of consequence in that it is highly important in evolutionary perspective to quickly see the external objects and judge their characteristics to plan future reactions. In this study, we investigated how human detect natural scene objects and categorize them in a limited time frame. We applied Magnetoencepahlogram (MEG) while participants were performing detection (e.g. object vs. texture) or basic-level categorization (e.g. cars vs. dogs) tasks to track the dynamic interaction in human brain for rapid object recognition process. The results revealed that detection and categorization involves different temporal and functional connections that correlated for the successful recognition process as a whole. These results imply that dynamics in the brain are important for our interaction with environment. The implication from this study can be further extended to investigate the effect of subconscious emotional factors on the dynamics of brain interactions during the rapid recognition process.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 추계학술대회
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• pp.97-100
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• 2013

본 논문은 복잡한 배경 영상에서 필기체가 아닌 수평, 수직 성분이 많이 포함된 문자 검출 방법을 제안한다. 본 논문에서 검출하고자 하는 문자는 코너 성분이 많이 밀집되어 있으며 배경 영상은 그에 비해 코너 성분이 적고 드문드문하다는 특징을 이용하여 먼저 해리스 코너 검출기를 이용하여 전체 영상에서 코너를 검출한다. 검출된 코너들의 위치 정보를 이용해 밀집되어 있는 코너들을 클러스터링 함으로써 문자 영역을 검출한다. 검출된 문자 영역간의 위치 정보와 히스토그램 분포를 비교하여 비슷한 특징을 갖는 영역들을 합치고 문자 성분의 특징을 갖지 않는 영역은 필터링 하여 문자 영역을 개선한다. 문자 영역에서 R채널, G채널, B채널 각각의 채널에 대한 히스토그램 분포를 분석하여 문자를 검출한다.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.490-498
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• 2011

In this paper, we present a two-stage vision-based approach to detect multi views of pedestrian in road scene images. The first stage is HG (Hypothesis Generation), in which potential pedestrian are hypothesized. During the hypothesis generation step, we use a vertical, horizontal edge map, and different colors between road background and pedestrian's clothes to determine the leg position of pedestrian, then a novel symmetry peaks processing is performed to define how many pedestrians is covered in one potential candidate region. Finally, the real candidate region where pedestrian exists will be constructed. The second stage is HV (Hypothesis Verification). In this stage, all hypotheses are verified by Support Vector Machine for classification, which is robust for multi views of pedestrian detection and recognition problems.

• 한국산업융합학회 논문집
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• 제24권5호
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.