• ETRI Journal
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• v.33 no.3
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• pp.415-422
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• 2011

In this paper, we present a novel vision-based method of recognizing finger actions for use in electronic appliance interfaces. Human skin is first detected by color and consecutive motion information. Then, fingertips are detected by a novel scale-invariant angle detection based on a variable k-cosine. Fingertip tracking is implemented by detected region-based tracking. By analyzing the contour of the tracked fingertip, fingertip parameters, such as position, thickness, and direction, are calculated. Finger actions, such as moving, clicking, and pointing, are recognized by analyzing these fingertip parameters. Experimental results show that the proposed angle detection can correctly detect fingertips, and that the recognized actions can be used for the interface with electronic appliances.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.2
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• pp.52-61
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• 2006

For the detection of moving objects, background subtraction methods are widely used. An adaptive Gaussian mixture model combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, probabilistic learning approach does not work well in high traffic regions. In this paper, we Propose a reliable learning method of complex and dynamic backgrounds in high traffic regions.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.787-795
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• 2008

Human recognition on camera is an interesting topic in computer vision. While fingerprint and face recognition have been become common, gait is considered as a new biometric feature for distance recognition. In this paper, we propose a gait recognition algorithm based on the knee angle, 2 feet distance, walking velocity and head direction of a person who appear in camera view on one gait cycle. The background subtraction method firstly use for binary moving object extraction and then base on it we continue detect the leg region, head region and get gait features (leg angle, leg swing amplitude). Another feature, walking speed, also can be detected after a gait cycle finished. And then, we compute the errors between calculated features and stored features for recognition. This method gives good results when we performed testing using indoor and outdoor landscape in both lateral, oblique view.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1496-1504
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• 2004

In this paper, we propose an algorithm for initializing a target vehicle detection, tracking the vehicle and estimating the distance from it on the stereo images acquired from a forward-looking stereo camera mounted on a road driving vehicle. The process of vehicle detection extracts road region using lane recognition and searches vehicle feature from road region. The distance of tracking vehicle is estimated by TSS correlogram matching from stereo Images. Through the simulation, this paper shows that the proposed method segments, matches and tracks vehicles robustly from image sequences obtained by moving stereo camera.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.217-220
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• 2001

In this paper, we propose a segmentation method of moving object using the spatio-temporal information in image sequences. Proposed method consists of motion detection step using difference image, region segmentation step using k-means algorithm, motion estimation step and segmenting step using intensity and motion information. Experimental results show that the method is capable of segmenting variously moving objects in image sequences.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.89.1-89.1
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• 2013

We present a multiwavelength study of the X-class flare, which occurred in active region (AR) NOAA 11339 on 3 November 2011. The EUV images recorded by SDO/AIA show the activation of a remote filament (located north of the AR) with footpoint brightenings about 50 min prior to the flare occurrence. The kinked filament rises-up slowly and after reaching a projected height of ~49 Mm, it bends and falls freely near the AR, where the X-class flare was triggered. Dynamic radio spectrum from the Green Bank Solar Radio Burst Spectrometer (GBSRBS) shows simultaneous detection of both positive and negative drifting pulsating structures (DPSs) in the decimetric radio frequencies (500-1200 MHz) during the impulsive phase of the flare. The global negative DPSs in solar flares are generally interpreted as a signature of electron acceleration related to the upward moving plasmoids in the solar corona. The EUV images from AIA $94{\AA}$ reveal the ejection of multiple plasmoids, which move simultaneously upward and downward in the corona during the magnetic reconnection. The estimated speeds of the upward and downward moving plasmoids are ~152-362 and ~83-254 km/s, respectively. These observations strongly support the recent numerical simulations of the formation and interaction of multiple plasmoids due to tearing of the current-sheet structure. On the basis of our analysis, we suggest that the simultaneous detection of both the negative and positive DPSs is most likely generated by the interaction/coalescence of the multiple plasmoids moving upward and downward along the current-sheet structure during the magnetic reconnection process. Moreover, the differential emission measure (DEM) analysis of the active region reveals presence of a hot flux-rope structure (visible in AIA 131 and $94{\AA}$) prior to the flare initiation and ejection of the multi-temperature plasmoids during the flare impulsive phase.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1166-1174
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• 2017

In general, the detection of the vehicle license plate is a previous step of license plate recognition and has been actively studied for several decades. In this paper, we propose an algorithm to detect a license plate area of a moving vehicle from a video captured by a fixed camera installed on the road using the Convolution Neural Network (CNN) technology. First, license plate images and non-license plate images are applied to a previously learned CNN model (AlexNet) to extract and classify features. Then, after detecting the moving vehicle in the video, CNN detects the license plate area by comparing the features of the license plate region with the features of the license plate area. Experimental result shows relatively good performance in various environments such as incomplete lighting, noise due to rain, and low resolution. In addition, to protect personal information this proposed system can also be used independently to detect the license plate area and hide that area to secure the public's personal information.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.2
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• pp.213-218
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• 2013

In this paper, a video based ship detection method is proposed to monitor port efficiently. Visual saliency map algorithm and mean shift algorithm is applied to detect moving ships don't include background information which is difficult to track moving ships. It is easy to detect ships at the port using saliency map algorithm, because it is very effective to extract saliency object from background. To remove background information in the saliency region, image segmentation and clustering using mean shift algorithm is used. As results of detecting simulation with images of a camera installed at the harbor, it is shown that the proposed method is effective to detect ships.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.40-50
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• 2007

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

• Journal of Platform Technology
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• v.10 no.2
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• pp.10-19
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• 2022

Moving assistant devices for people who are difficult to move are becoming electric-powered and automated. These moving assistant devices are not suitable for moving stairs at which the height between floor surfaces is different because these devices are designed and manufactured for flatland moving. An electric-powered and automated moving assistant device should change direction or stop when it approaches stairs in a movement direction. If the user or automatic control system does not change direction or stop in time, a moving assistant device can roll over or collide with stairs. In this paper, we propose a stairs detection method by using oblique distance measured by one sensor tilted to flatland. The method proposed in this paper can detect upward or downward stairs by using a difference between a predicted and measured oblique distance in considering a tilted angle of a sensor for measuring an oblique distance and installation height of the sensor on a moving object. Before the device enters a stairs region, if our proposed method provides information about detected stairs to a device's controller, the controller can do adequate action to avoid the accident.