• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.69-72
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• 2002

Human face detection has many applications such as face recognition, face or facial feature tracking, pose estimation, and expression recognition. We present a new method for automatically segmentation and face detection in color images. Skin color alone is usually not sufficient to detect face, so we combine the color segmentation and shape analysis. The algorithm consists of two stages. First, skin color regions are segmented based on the chrominance component of the input image. Then regions with elliptical shape are selected as face hypotheses. They are certificated to searching for the facial features in their interior, Experimental results demonstrate successful detection over a wide variety of facial variations in scale, rotation, pose, lighting conditions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.815-820
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• 2000

The early damage detection of large structures is very important to prevent the disaster due to its global failure. In this paper, a damage detection method of the beam-analogy structure based on the wavelet transformation of mode shape is presented. This can effectively detect the singularity of mode shape caused to the inconsistency of bending moment and shear force at the damaged part using the discrete wavelet and its inverse transforms. To investigate the validity and the applicability of the presented damage detection method, numerical simulation and experiment are carried out for the idealized beam and the real ship structures.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.41-47
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.29-32
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• 2002

This paper suggest target detection algorithm for mobile robot control using color and shape recognition. In many cases, ultrasonic sensor(USS) is used in mobile robot system to measure the distance between obstacles. But with only USS, it may have many restrictions. So we attached CCD camera to mobile robot to overcome its restrictions. If visual information is given to robot system then robot system will be able to accomplish more complex mission successfully. With acquired vision data, robot looks for target by color and recognize its shape.

• Proceedings of the Korea Concrete Institute Conference
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• 2000.10b
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• pp.1303-1308
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• 2000

Detection of square shape voids with size 30$\times$20$\times$5cm and 20$\times$20$\times$20cm in concrete were carried out by ultrasonic image processing technique. The advantages and limitations of this technique for non-destructive inspection of square shape voids in concrete are investigated. In this study, it has been verified that the semi-direct measurement method is more effective than the other methods for detecting the voids in concrete using ultrasonic image processing method.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.104-112
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• 2019

This study proposed a robust detection algorithm. It detects face more stably with respect to changes in light and rotation forthe identification of a face shape. The proposed algorithm uses face shape asinput information in a single camera environment and divides only face area through preprocessing process. However, it is not easy to accurately recognize the face area that is sensitive to lighting changes and has a large degree of freedom, and the error range is large. In this paper, we separated the background and face area using the brightness difference of the two images to increase the recognition rate. The brightness difference between the two images means the difference between the images taken under the bright light and the images taken under the dark light. After separating only the face region, the face shape is recognized by using the self-organization feature map (SOFM) algorithm. SOFM first selects the first top neuron through the learning process. Second, the highest neuron is renewed by competing again between the highest neuron and neighboring neurons through the competition process. Third, the final top neuron is selected by repeating the learning process and the competition process. In addition, the competition will go through a three-step learning process to ensure that the top neurons are updated well among neurons. By using these SOFM neural network algorithms, we intend to implement a stable and robust real-time face shape recognition system in face shape recognition.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.28-36
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• 2013

This study proposed a robust detection algorithm. It detects hands more stably with respect to changes in light and rotation for the identification of a hand shape. Also it satisfies both efficiency of calculation and the function of detection. The algorithm proposed segmented the hand area through pre-processing using a hand shape as input information in an environment with a single camera and then identified the shape using a Self Organized Feature Map(SOFM). However, as it is not easy to exactly recognize a hand area which is sensitive to light, it has a large degree of freedom, and there is a large error bound, to enhance the identification rate, rotation information on the hand shape was made into a database and then a principal component analysis was conducted. Also, as there were fewer calculations due to the fewer dimensions, the time for real-time identification could be decreased.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.180-186
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• 2012

In this paper, a method of edge detection from low contrast and noisy images which contain hexagonal shape is proposed. This method is based on the combination of laplacian gaussian filter and an idea of filters which are dependent on the shape. First, an algorithm which has six masks as its extractors to detect the hexagonal edges especially in the comers is used. Here, two tricom filters are used to detect the tricom joints of hexagons and other four masks are used to enhance the line segments of hexagonal edges. As a natural image, a corneal endothelial cell image which usually has a regular hexagonal shape is selected. The edge detection of hexagonal shapes in this corneal endothelial cell is important for clinical diagnosis. Next, The proposal algorithm and other conventional methods are applied to noisy hexagonal images to evaluate each efficiency. As a result, this proposal algorithm shows a robustness against noises and better detection ability in the aspects of the signal to noise ratio, the edge coineidence ratio and the detection accuracy factor as compared with other conventional methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.1-9
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• 2010

Generally, dynamic hand gesture recognition is formed through preprocessing step, hand tracking step and hand shape detection step. In this paper, we present advanced dynamic hand gesture recognizing method that improves performance in preprocessing step and hand shape detection step. In preprocessing step, we remove noise fast by using dynamic table and detect skin color exactly on complex background for controling skin color range in skin color detection method using YCbCr color space. Especially, we increase recognizing speed in hand shape detection step through detecting Start Image and Stop Image, that are elements of dynamic hand gesture recognizing, using Guideline. Guideline is edge of input hand image and hand shape for comparing. We perform various experiments with nine web-cam video clips that are separated to complex background and simple background for dynamic hand gesture recognition method in the paper. The result of experiment shows similar recognition ratio but high recognition speed, low cpu usage, low memory usage than recognition method using learning exercise.

• Journal of Korea Multimedia Society
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• v.19 no.2
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• pp.418-427
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• 2016

A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.