• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.225-236
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• 2010

This study proposes a method for the recognition of emergency situation according to the bimodal information of camera image sensor and gravity sensor. This method can recognize emergency condition by mutual cooperation and compensation between sensors even when one of the sensors malfunction, the user does not carry gravity sensor, or in the place like bathroom where it is hard to acquire camera images. This paper implemented HMM(Hidden Markov Model) based learning and recognition algorithm to recognize actions such as walking, sitting on floor, sitting at sofa, lying and fainting motions. Recognition rate was enhanced when image feature vectors and gravity feature vectors are combined in learning and recognition process. Also, this method maintains high recognition rate by detecting moving object through adaptive background model even in various illumination changes.

• Convergence Security Journal
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• v.2 no.2
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• pp.99-108
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• 2002

In this paper, I propose a reference image updating algorithm for Intruder Detection System using a difference image method that can reliably separate moving objects from noisy background in the image sequence received from a camera at the fixed position. The proposed algorithm consists of four process determines threshold value and quantization, segmentation of a moving object area, generation of adaptive temporary image that removes a moving object area, and updates reference image using median filtering. The test results show that the proposed algorithm can generate reference image very effectively in the noisy environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.1
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• pp.27-35
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• 2013

This paper proposes a face tracking algorithm for a viewpoint adaptive multi-view synthesis system. The original scene captured by a depth camera contains a texture image and 8 bit gray-scale depth map. From this original image, multi-view images that correspond to the viewer's position can be synthesized using geometrical transformations, such as rotation and translation. The proposed face tracking technique gives a motion parallax cue by different viewpoints and view angles. In the proposed algorithm, the viewer's dominant face, which is established initially from a camera, can be tracked using the statistical characteristics of face colors and deformable templates. As a result, a motion parallax cue can be provided by detecting the viewer's dominant face area and tracking it, even under a heterogeneous background, and synthesized sequences can be displayed successfully.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.4
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• pp.231-238
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• 2017

Detecting small infrared targets from the low-SCR images at a long distance is very hard. The previous Local Contrast Method (LCM) algorithm based on the human visual system shows a superior performance of detecting small targets by a background suppression technique through local contrast measure. However, its slow processing speed due to the heavy multi-scale processing overhead is not suitable to a variety of real-time applications. This paper presents a lightweight real-time small target detection algorithm, called by the Improved Selective Local Contrast Method (ISLCM), to reduce the scale-invariant computational overhead. The proposed ISLCM applies the improved local contrast measure to the predicted selective region so that it may have a comparable detection performance as the previous LCM while guaranteeing low scale-invariant computational load by exploiting both adaptive scale estimation and small target feature feasibility. Experimental results show that the proposed algorithm can reduce its computational overhead considerably while maintaining its detection performance compared with the previous LCM.

• Journal of The Korean Astronomical Society
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• v.40 no.4
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• pp.153-155
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• 2007

We have analyzed H and $K_s$-band images of the Arches cluster obtained using the NIRC2 instrument on Keck with the laser guide star adaptive optics (LGS AO) system. With the help of the LGS AO system, we were able to obtain the deepest ever photometry for this cluster and its neighborhood, and derive the background-subtracted present-day mass function (PDMF) down to $1.3M_{\bigodot}$ for the 5"-9" annulus of the cluster. We find that the previously reported turnover at $6M_{\bigodot}$ is simply due to a local bump in the mass function (MF), and that the MF continues to increase down to our 50 % completeness limit ($1.3M_{\bigodot}$) with a power-law exponent of ${\Gamma}$ = -0.91 for the mass range of 1.3 < M/$M_{\bigodot}$ < 50. Our numerical calculations for the evolution of the Arches cluster show that the ${\Gamma}$ values for our annulus increase by 0.1-0.2 during the lifetime of the cluster, and thus suggest that the Arches cluster initially had ${\Gamma}$ of $-1.0{\sim}-1.1$, which is only slightly shallower than the Salpeter value.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.20-27
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• 2020

Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). In this paper, we proposed a deep learning model that integrates Convolution Neural Network (CNN) with Non-Negative Matrix Factorization (NMF). To improve the separation quality of the NMF, it includes noise update technique that learns and adapts the characteristics of the current noise in real time. The noise update technique analyzes the sparsity and activity of the noise bias at the present time and decides the update training based on the noise candidate group obtained every frame in the previous noise reduction stage. Noise bias ranks selected as candidates for update training are updated in real time with discrimination NMF training. This NMF was applied to CNN and Hidden Markov Model(HMM) to achieve improvement for performance of sound event detection. Since CNN has a more obvious performance improvement effect, it can be widely used in sound source based CNN algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.422-433
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• 2001

In this paper, we propose a flew edge detection method based on water flow model, in which gradient image surface is considered as a 3-dimensional (3-D) geographical feature. The edges of the objects in the background can be detected by the large gradient magnitude areas and to make the edges immersed it is required to invert the gradient image. The proposed edge detector uses a water flow model based enhancement and locally adaptive thresholding technique applied to the inverted gradient image resulting in better noise performance. Computer simulations with a few synthetic and real images show that the Proposed method can extract edge contour effectively.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.42 no.1
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• pp.41-46
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• 2005

A new multiple description selective coding is proposed to overcome noisy channels. Our algorithm adopts an embedded coding scheme, in which subband coefficients are encoded one bit plane at a time using a non-adaptive arithmetic encoder. According to the importance ratio for each region, we code interest regions with more passes than background in order to reconstruct interest regions with higher quality. To overcome channel errors, we adopt multiple description scheme which adds controlled amounts of redundancy to the original data during the compression process. Proposed algorithm achieves better qualify compared with other algorithms especially in the circumstances where very low bit rate coding is required and some regions are more important than other regions.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.447-454
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• 2007

TFT-LCD(Thin Film transistor liquid crystal display) has become actively used front panel display technology with increasing market. Intrinsically there is region of non uniformity with low contrast that to human eye is perceived as defect. As the gray level difference between the defect and the background is hardly distinguishable, conventional thresholding and edge detection techniques cannot be applied to detect the defect. Between the patterned and un-patterned LCD defects, this paper deals with un-patterned LCD defects by using independent component analysis, adaptive thresholding and skewness. Our method showed strong results even on noised LCD images and worked successfully on the manufacturing line.