• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.63-70
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• 2022

Although 2D Object detection has been largely improved in the past years with the advance of deep learning methods and the use of large labeled image datasets, 3D object detection from 2D imagery is a challenging problem in a variety of applications such as robotics, due to the lack of data and diversity of appearances and shapes of objects within a category. Google has just announced the launch of Objectron that has a novel data pipeline using mobile augmented reality session data. However, it also is corresponding to 2D-driven 3D object detection technique. This study explores more mature 2D object detection method, and applies its 2D projection to Objectron 3D lifting system. Most object detection methods use bounding boxes to encode and represent the object shape and location. In this work, we explore a stochastic representation of object regions using Gaussian distributions. We also present a similarity measure for the Gaussian distributions based on the Hellinger Distance, which can be viewed as a stochastic Intersection-over-Union. Our experimental results show that the proposed Gaussian representations are closer to annotated segmentation masks in available datasets. Thus, less accuracy problem that is one of several limitations of Objectron can be relaxed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.709-718
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• 1992

A new edge detection operator superimposing two displaced Gaussian smoothing filters Is proposed as an approximate operator for the DroG(flrst derivative of Gaussian) known as a sub-op-timal step edge detector. The performance of the proposed edge detector Is very close to that of the DroG with the performance criteria . signal to noise ratio, locality, and multiple response. And the computational complexity can be reduced almost by a half of that of DroG, because of the use of common 2-D smoothing filter for DroG and LoG ( Laplacian of Gausslan) systems.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.229-233
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• 2003

In this paper we theoretically analyze the probability of error for M-ary pulse position modulation (PPM) ultra-wideband (UWB) multiple access system using Gaussian monopulse. The optimum detection of UWB signals using M-ary orthogonal PPM in additive white Gaussian noise (AWGN) and multiple access interference (MAI) is considered, then receiver signal to noise power ratio (SNR) and upper bound fur the bit error rate (BER) are derived. Numerical results considering some practical parameters are presented.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.22-29
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• 2010

A real-time detection and tracking of moving objects in video sequences is very important for smart surveillance systems. In this paper, we propose a novel algorithm for the detection of moving objects that is the entropy-based adaptive Gaussian mixture model (AGMM). First, the increment of entropy generally means the increment of complexity, and objects in unstable conditions cause higher entropy variations. Hence, if we apply these properties to the motion segmentation, pixels with large changes in entropy in moments have a higher chance in belonging to moving objects. Therefore, we apply the Clausius entropy theory to convert the pixel value in an image domain into the amount of energy change in an entropy domain. Second, we use an adaptive background subtraction method to detect moving objects. This models entropy variations from backgrounds as a mixture of Gaussians. Experiment results demonstrate that our method can detect motion object effectively and reliably.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.136-141
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• 2011

In this paper, we propose an approach to improve the data detection performance of the acoustic data transmission system based on the modulated complex lapped transform (MCLT). We first present an effective analysis of the features and the detection method of data in the acoustic data transmission system. And then feature vectors which are applied to the Gaussian mixture model (GMM) are selected from relevant parameters of the previous system for the efficient data detection. For the purpose of evaluating the performance of the proposed algorithm, Bit error rate (BER) of the received data was measured at different environments (music genres (rock, pop, classic, jazz) and different distances (1m∼5m) from the loudspeaker to the microphone in a office room) and yields better results compared with the conventional scheme of the acoustic data transmission system based on the MCLT.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2380-2388
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• 2015

A hardware implementation of MOD(moving object detection) algorithm using EGML(effective Gaussian mixture learning)- based background subtraction to detect moving objects in video is described. Some approximations of EGML calculations are applied to reduce hardware complexity, and pipelining technique is adopted to improve operating speed. The MOD processor designed in Verilog-HDL has been verified by FPGA-in-the-loop verification using MATLAB/Simulink. The MOD processor has 2,218 slices on the Virtex5-XC5VSX95T FPGA device and its throughput is 102 MSamples/s at 102 MHz clock frequency. Evaluation results of the MOD processor for 12 images in the IEEE CDW-2012 dataset show that the average recall value is 0.7631, the average precision value is 0.7778 and the average F-measure value is 0.7535.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2153-2160
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• 2015

An analysis of hardware design conditions of moving object detection (MOD) algorithm is described, which is based on effective Gaussian mixture learning (EGML). A simulation model of EGML algorithm is implemented using OpenCV, and the effects of some parameter values on background learning time and MOD sensitivity are analyzed for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-widths of parameters. The proposed fixed-point model of the EGML-based MOD uses only half of the bit-width at the expense of the loss of MOD performance within 0.5% when compared with floating-point MOD results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.1
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• pp.41-50
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• 2014

In this paper, we proposed an error detection in Gaussian normal basis multiplier over $GF(2^n)$. It is shown that by using parity prediction, error detection can be very simply constructed in hardware. The hardware overheads are only one AND gate, n+1 XOR gates, and one 1-bit register in serial multipliers, and so n AND gates, 2n-1 XOR gates in parallel multipliers. This method are detect in odd number of bit fault in C = AB.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.321-325
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• 2004

Retinopathy of Prematurity (ROP) is a common retinal neovascular disorder of premature infants. It can be characterized by inappropriate and disorganized vessel. This paper present a method for blood vessel detection on infant retinal images. The algorithm is designed to detect the retinal vessels. The proposed method applies a Lapalacian of Gaussian as a step-edge detector based on the second-order directional derivative to identify locations of the edge of vessels with zero crossings. The procedure allows parameters computation in a fixed number of operations independent of kernel size. This method is composed of four steps : grayscale conversion, edge detection based on LOG, noise removal by adaptive Wiener filter & median filter, and Otsu's global thresholding. The algorithm has been tested on twenty infant retinal images. In cooperation with the Digital Imaging Research Centre, Kingston University, London and Department of Opthalmology, Imperial College London who supplied all the images used in this project. The algorithm has done well to detect small thin vessels, which are of interest in clinical practice.