• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.379-384
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• 2009

A human gaze detection and tracing method is importantly required for HMI(Human-Machine-Interface) like a Human-Serving robot. This paper proposed a novel three-dimension (3D) human gaze estimation method by using a face recognition, an orientation estimation and SVMs (Support Vector Machines). 2,400 images with the pan orientation range of $-90^{\circ}{\sim}90^{\circ}$ and tilt range of $-40^{\circ}{\sim}70^{\circ}$ with intervals unit of $10^{\circ}$ were used. A stereo camera was used to obtain the global coordinate of the center point between eyes and Gabor filter banks of horizontal and vertical orientation with 4 scales were used to extract the facial features. The experiment result shows that the error rate of proposed method is much improved than Liddell's.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.219-222
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• 2001

Moving objects in video data are main elements for video analysis and retrieval. In this paper, we propose a new algorithm for tracking and segmenting moving objects in color image sequences that include complex camera motion such as zoom, pan and rotating. The Proposed algorithm is based on the Mean-shift color segmentation and stochastic region matching method. For segmenting moving objects, each sequence is divided into a set of similar color regions using Mean-shift color segmentation algorithm. Each segmented region is matched to the corresponding region in the subsequent frame. The motion vector of each matched region is then estimated and these motion vectors are summed to estimate global motion. Once motion vectors are estimated for all frame of video sequences, independently moving regions can be segmented by comparing their trajectories with that of global motion. Finally, segmented regions are merged into the independently moving object by comparing the similarities of trajectories, positions and emerging period. The experimental results show that the proposed algorithm is capable of segmenting independently moving objects in the video sequences including complex camera motion.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.9-11
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• 2005

We propose a new content-based image retrieval using a block color co-occurrence matrix (BCCM) and pattern correlogram. In the proposed method, the color feature vectors are extracted by using BCCM that represents the probability of the co-occurrence of two mean colors within blocks. Also the pattern feature vectors are extracted by using pattern correlogram which is combined with spatial correlation of pattern. In the proposed pattern correlogram method. after block-divided image is classified into 48 patterns with respect to the change of the RGB color of the image, joint probability between the same pattern from the surrounding blocks existing at the fixed distance and the center pattern is calculated. Experimental results show that the proposed method can outperform the conventional methods as regards the precision and the size of the feature vector dimension.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.6B
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• pp.1021-1027
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• 2000

In this paper, we propose two new detection-estimation based algorithms that effectively remove impulsive noises in severely corrupted color images. The existing methods for enhancing corrupted color images with impulsive noises commonly possess the inherent problems of excessive computing time and smoothing out edges. However, since our proposed algorithms classify corrupted pixels first in each channel or in each pixel and then perform marginal or vector median filtering only for them, are computationally efficient and preserve edges well. In addition, since there are no appropriate criteria to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor to compare the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than the existing methods in terms of both objective and subjective evaluat ons.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.49-52
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• 2000

The Estimation method for spectral reflectance of the object using five-band and nine-band have been developed. The five-band acquisition are required of five or three times same work for color image acquisition process. To solve the above problems, we proposed a new method that can be reconstructed spectral reflectance of object. The proposed method was to classify same hues corresponding a color stimulus, by using hue angle and chroma vector of a color stimulus. The reconstruction of spectral reflectance was examined by computer simulation, and evaluated by MSE(Mean Square Error) and color difference between the original and reconstructed spectral reflectance.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.289-293
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• 2008

In this paper, we propose the hardware architecture to accelerate 2D Vector graphics process for mobile devices. we propose the Transformation Unit Architecture that considerates the operation dependency. It has 3 cycles excution time and uses 2 multipliers and 2 adders. Proposed paint generation unit uses a LUT method, so it does not execute color interpolation which needs to be calculated every time. The proposed OpenVG 1.0 Accelerator achieved a 2.85 times faster performance in a tiger model.

• Proceedings of the Korea Institute of Fire Science and Engineering Conference
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• 2008.04a
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• pp.135-138
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• 2008

Many Victims and property damage are caused in fires. In this paper, an flame detection algorithm is proposed to early alarm fires. The proposed flame detection algorithm is based on 2-stage decision strategy of video processing. The first decision is to check with color distribution of input vidoe. In the second, the candidated region is settled as fire region with activity. As a result of simulation, it is shown that the proposed algorithm is useful for fire recognition.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.165-170
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1312-1317
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.