• 한국정보디스플레이학회:학술대회논문집
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• 2002.08a
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• pp.887-890
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• 2002

In this paper, a new 3D robot vision system using the hierarchical opto-digital algorithm is proposed and implemented. From some experimental results with the 20 frames of the stereo input image pairs, the proposed system is found to be able to effectively extract the area where the target object is located from the stereo input image regardless of the background noises.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.680-685
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• 1994

The acoustic phenomena in the actual sound systems involve a variety of compound problems. In this paper, the well-known Bayes' theorem is first employed and expanded into orthonormal and non-orthonomal series forms matched to the digital processing of lower and higher order statistical informations and the noisy observations. Proposed on-line algorithms of digital filter type are applied to the actual state estimation for a reverberation characteristics in a room under contamination of background noises.

• Journal of Korea Multimedia Society
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• v.13 no.1
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• pp.47-57
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• 2010

The object detection is essential for identifying objects, location information, and user context-aware in the image. In this paper, we propose a robust object detection system. The System linearly transforms learning data obtained from the background images to Principal components. It organizes the Eigen-background with the selected Principal components which are able to discriminate between foreground and background. The Fuzzy-C-means (FCM) carries out clustering for images with inputs from the Eigen-background information and classifies them into objects and backgrounds. It used various patterns of backgrounds as learning data in order to implement a system applicable even to the changing environments, Our system was able to effectively detect partial movements of a human body, as well as to discriminate between objects and backgrounds removing noises and shadows without anyone frame image for fixed background.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.743-748
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• 2009

In this paper, we propose an effective method to extract background components in automated vision inspection system for polarized film used in TFT LCD display panels. The test image signals are typically composed of three components such as ununiform background, random noises and target defect signals. It is important to analyze the background signal for accurate extraction of defect components. Two dimensional continuous wavelets with first derivative gaussian is used. This methods can be applied for reliable extraction of defect signal by elimination of the background signal from the original image. The proposed method outperforms over conventional FFT methods.

• Journal of Digital Contents Society
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• v.15 no.4
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• pp.449-455
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• 2014

Detection of moving objects is a fundamental task in most of the computer vision applications, such as video surveillance, activity recognition and human motion analysis. This is a difficult task due to many challenges in realistic scenarios which include irregular motion in background, illumination changes, objects cast shadows, changes in scene geometry and noise, etc. In this paper, we propose an foreground extraction algorithm based on codebook, a database of information about background pixel obtained from input image sequence. Initially, we suppose a first frame as a background image and calculate difference between next input image and it to detect moving objects. The resulting difference image may contain noises as well as pure moving objects. Second, we investigate a codebook with color and brightness of a foreground pixel in the difference image. If it is matched, it is decided as a fault detected pixel and deleted from foreground. Finally, a background image is updated to process next input frame iteratively. Some pixels are estimated by input image if they are detected as background pixels. The others are duplicated from the previous background image. We apply out algorithm to PETS2009 data and compare the results with those of GMM and standard codebook algorithms.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.2
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• pp.167-176
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• 2009

In this paper, a system that detects driver's drowsiness has been implemented based on the automatic extraction and the tracking of pupils. The research also focuses on the compensation of illumination and reduction of background noises that naturally exist in the driving condition. The system, that is based on the principle of Haar-like feature, automatically collects data from areas of driver's face and eyes among the complex background. Then, it makes decision of driver's drowsiness by using recognition of characteristics of pupils area, detection of pupils, and their movements. The implemented system has been evaluated and verified the practical uses for the prevention of driver's drowsiness.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.17-22
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• 2013

In this paper, we propose a concrete crack detection method using shape properties. It is based on morphology algorithm and crack features. We assume that an input image is contaminated by various noises. Thus, we use a morphology operator and extract patterns of crack. It segments cracks and background using opening and closing operations. Morphology based segmentation is better than existing integration methods using subtraction in detecting a crack it has small width. Also, it is robust to noisy environment. The proposed algorithm classifies the segmented image into crack and background using shape properties of crack. This method calculates values of properties such as the number of pixels and the maximum length of the segmented region. Also, pixel counts of clusters are considered. We decide whether the segmented region belongs to cracks according to those data. Experimental results show that our proposed crack detection method has better results than those by existing detection methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.1
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• pp.123-130
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• 2000

In this paper, we propose an extracting method of moving object contour using active contour model from image sequences acquired by fixed camera. We use an adaptive background model for robust processing in surrounding conditions. Object segmentation model detects pixels thresholded from local difference image between background and current image and extracts connected regions. Noises in boundary area of moving object we eliminated by morphological filter. The contour of segmented object is corrected by using active contour model for extracting accurate boundary of moving object. We apply the proposed method to highway image sequences and show the results of simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.1335-1340
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• 2001

The information technology brought us many kinds of conveniences to our life, but it also caused social problems such as privacy interference, unexpected personal information leaks, and nose generation by telephonic talks, etc. In this paper, the microphone system of the cellular phone is developed to prevent these problems caused by progress of information technology. The developed system was designed to detect only acoustic signals from a human being in the presence of various kinds of background noises. A windscreen was designed by use of micro-channels to eliminate the popping noise by the wind from the mouth of a speaker and four microphone array and signal processing techniques are applied to reduce background noise. The impact of the developed system was evaluated by experimental tests. The results show that the system can improve the required functions considerably.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.1-10
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• 2018

This paper presents an autonomous detection and aiming point computation of a battle tank by using RGB images. Maximally stable extremal regions algorithm was implemented to find features of the tank, which are matched with images extracted from streaming video to figure out the region of interest where the tank is present. The median filter was applied to remove noises in the region of interest and decrease camouflage effects of the tank. For the tank segmentation, k-mean clustering was used to autonomously distinguish the tank from its background. Also, both erosion and dilation algorithms of morphology techniques were applied to extract the tank shape without noises and generate the binary image with 1 for the tank and 0 for the background. After that, Sobel's edge detection was used to measure the outline of the tank by which the aiming point at the center of the tank was calculated. For performance measurement, accuracy, precision, recall, and F-measure were analyzed by confusion matrix, resulting in 91.6%, 90.4%, 85.8%, and 88.1%, respectively.