• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.206-208
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• 2000

본 논문은 칼라 정보와 움직임 정보를 이용한 객체 분할 기법의 설계에 대하여 소개한다. 객체 분할 알고리즘은 L*u*v 공간의 칼라 특성과 움직임 특성을 결합하여 설계하였다. 즉 공간 분할은 mean shift 칼라 클러스터링 알고리즘(color clustering algorithm)을 사용하여 중심 칼라 영역에 따라 동일한 칼라 지역으로 통합한다. 시간 분할은 움직임 검출을 위하여 affine six parameter 움직임 모델과 optical flow equation를 이용하여 움직임이 발생한 부분을 검출한다. 다음에 공간 분할과 시간 분할에 따라 결과를 통합하고 MAD(mean absolute difference)를 사용하여 객체를 추출하는 알고리즘을 설계하였다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12C
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• pp.1147-1153
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• 2009

We propose a novel image segmentation and segment-based stereo matching technique using color, depth, and motion information. Proposed technique firstly splits reference images into foreground region or background region using depth information from depth camera. Then each region is segmented into small segments with color information. Moreover, extracted segments in current frame are tracked in the next frame in order to maintain depth consistency between frames. The initial depth from the depth camera is also used to set the depth search range for stereo matching. Proposed segment-based stereo matching technique was compared with conventional one without foreground and background separation and other conventional one without motion tracking of segments. Simulation results showed that the improvement of segment extraction and depth estimation consistencies by proposed technique compared to conventional ones especially at the static background region.

• ETRI Journal
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• v.32 no.6
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• pp.881-890
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• 2010

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE $L^*a^*b^*$ color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.88-97
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• 2000

This paper presents motion estimation method using region segmentation information Motion estimation which is very difficult to be implemented only by software because of intensive computation cost, is implemented by special-purpose hardware in real-time applications In this paper, we propose region based motion estimation algorithm which can reduce the computation cost by using region segmentation information and setting the variable search window compared with FSMA algorithm Secondly, another proposed algorithm is to segment semantic region like face for selective coding and transfer of semantic region using segmented region information This work alms to improving the subjective quality of skin color region or face region m the picture that has slow motion and IS mainly composed of one or two speakers of video conference and video telephony applications.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.129-135
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• 2011

In this paper, we propose the algorithm using optical flow and machine learning-based segmentation for the 3D conversion of 2D video. For the segmentation allowing the successful 3D conversion, we design a new energy function, where color/texture features are included through machine learning method and the optical flow is also introduced in order to focus on the regions with the motion. The depth map are then calculated according to the optical flow of segmented regions, and left/right images for the 3D conversion are produced. Experiment on various video shows that the proposed method yields the reliable segmentation result and depth map for the 3D conversion of 2D video.

• The Transactions of the Korea Information Processing Society
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• v.4 no.9
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• pp.2197-2207
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• 1997

In this paper, we propose a video data segmentation method using edge and color distribution of video frames and also develop a video browser by using the proposed algorithm. To segment a video, we use a 644-bin HSV color histogram and the edge information which generated with automatic threshold method. We consider scene's characteristics by using positions and colo distributions of object in each frame. We develop a hierarchical and a shot-based browser for video browsing. We also show that our proposed method is less sensitive to light effects and more robust to motion effects than previous ones like a histogram-based method by testing with various video data.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.105.2-105
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• 2001

This paper studies continuous Korean Sign Language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is a very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt Hidden Markov Model (HMM). Using these methods, we recognize 5 KSL sentences and obtain 94% recognition ratio.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.461-468
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• 2003

In a sequence of images obtained by surveillance cameras, facial regions appear very small and their colors change abruptly by lighting condition. This paper proposes a new face detection scheme, robust on complex background, small size, and lighting conditions. The proposed method is consisted of three processes. In the first step, the candidates for the face regions are selected using face color distribution and motion information. In the second stage, the non-face regions are removed using face color ratio, boundary ratio, and average of column-wise intensity variation in the candidates. The face regions containing eyes and mouth are segmented and classified, and then they are scored using their topological relations in the last step. To speed up and improve a performance the above process, a block based image segmentation technique is used. The experiments have shown that the proposed algorithm detects faced regions with more than 91% of accuracy and less than 4.3% of false alarm rate.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.306-308
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• 2003

본 논문은 컬러영상내 움직이는 객체의 효과적인 검출을 위해 색상과 위치정보를 적용시킨 K-means 클러스터링 알고리즘을 이용하여 움직이는 객체들을 추출한 방법을 제안하고 있다. 최종 클러스터링된 중심픽셀(prototype)이 갖고있는 RGB 값을 사용해 프레임을 비교해 객체와 배경의 분리를 가능하게 했고 마지막으로 후처리를 이용해 남아있는 배경잡음을 제거하였다. 본 연구의 실험은 여러 교통장면을 포함한 다양한 영상에서 이루어졌으며 실험결과 제안된 알고리즘은 기존의 픽셀이나 블록기반의 방법에 비해 보다 정확한 객체 검출이 가능했으며 한 가지 특징 정보를 사용한 클러스터링에 비해 보다 높은 정확도를 보였다.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.721-728
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• 2005

This paper suggests effective object detection system for moving objects with specified color and motion information. The proposed detection system includes the object extraction and definition process which uses MCN(Multiple Color Normalization) and MCWUPC(Moving Color Weighted Unmatched Pixel Count) computation to decide the existence of moving object and object segmentation technique using signature information is used to exactly extract the objects with high probability. Finally, real time detection system is implemented to verify the effectiveness of the technique and experiments show that the success rate of object tracking is more than $89\%$ of total 120 image frames.