• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.1-6
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• 2019

Foreground/background segmentation in video sequences is often one of the first tasks in machine vision applications, making it a critical part of the system. In this paper, we present an improved sample-based technique that provides robust background image as well as segmentation mask. The conventional multiple interval sampling (MIS) algorithm have suffer from the unbalance of computation time per frame and the rapid change of confidence factor of background pixel. To balance the computation amount, a random-based pixel update scheme is proposed and a spatial and temporal smoothing technique is adopted to increase reliability of the confidence factor. The proposed method allows the sampling queue to have more dispersed data in time and space, and provides more continuous and reliable confidence factor. Experimental results revealed that our method works well to estimate stable background image and the foreground mask.

• Journal of the Korea Society of Computer and Information
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• v.22 no.6
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• pp.131-140
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• 2017

As video equipments such as CCTV are used for various purposes in fields of society, digital video data processing technology such as automatic motion detection is essential. In this paper, we proposed and implemented a more stable and accurate motion detection system based on background subtraction technique. We could improve the accuracy and stability of motion detection over existing methods by efficiently processing color information of digital image data. We divided the procedure of color information processing into each components of color information : brightness component, color component of color information and merge them. We can process each component's characteristics with maximum consideration. Our color information processing provides more efficient color information in motion detection than the existing methods. We improved the success rate of motion detection by our background update process that analyzed the characteristics of the moving background in the natural environment and reflected it to the background image.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.159-168
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• 2019

Accurate forest fires detection algorithms remain a challenging issue, because, some of the objects have the same features with fire, which may result in high false alarms rate. This paper presents a new video-based, image processing forest fires detection method, which consists of four stages. First, a background-subtraction algorithm is applied to detect moving regions. Secondly, candidate fire regions are determined using CIE $L{\ast}a{\ast}b{\ast}$ color space. Thirdly, special wavelet analysis is used to differentiate between actual fire and fire-like objects, because candidate regions may contain moving fire-like objects. Finally, support vector machine is used to classify the region of interest to either real fire or non-fire. The final experimental results verify that the proposed method effectively identifies the forest fires.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.335-337
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• 2002

The aim of this study was investigate the epileptogenic zone in temporal lobe epilepsy (TLE). We evaluated the subtraction image of interictal SPECT from ictal SPECT coregistered to 3-dimensional (3D) MRI, and compared with the normal healthy SPECT using a SPM99. Forty-nine patients with TLE (M:F=28:21, mean age: 33${\pm}$2.1 years) underwent a pairs of ictal and interictal SPECT. We performed subtraction interictal SPECT from ictal SPECT in TLE patients. In addition, using SPM methods and t-statistics, SPECT images of the TLE patients were compared with normal healthy SPECT on a voxel by voxel basis. The voxels with a p-value of less than 0.05, 0.005, 0.001 were considered to be significantly different. The subtraction results by ictal and interictal SPECT coincided with the significant rCBF changes when compare of the normal healthy SPECT using a SPM99. The results suggested that analysis of difference of the two methods using healthy normal SPECT with SPM99 is useful tool in evaluation of seizure focus in epilepsy.

• The Bulletin of The Korean Astronomical Society
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• v.30 no.1
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• pp.33.1-33.1
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• 2005

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.2
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• pp.132-139
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• 1989

We have designed a real-time Cardiac digital substraction angiography system (C-DSA) and tested the system for the diagnosis of Cardiac patients. The system was developed by interfacing a radiographic angiography system to a computerized motional image analysis system. This new cardiac DSA can perform the real time processing of averaging and subtraction of the 32 image frames to measure the volume changes of the left ventricle after elimination of motional artrifacts, caused by the heart contraction of beats per minute in average. Each frame has a resolution of 512 x 512 pixels and 256 gray levels. Two image data with maximal and minimal volume were moved to the interfaced IBM PC computer system by high speed computer link line for computation of the heart's contraction parameters. First, the boundary of the left ventricule was detected using a dynamic programming of the gray levels, and its volume was computered to determine the parameters, such as the maximal volume of end-diastolic volume (EDV), the minimal volume of end systolic volume (ESV), and ejection fraction (EF).

• Imaging Science in Dentistry
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• v.39 no.1
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• pp.35-39
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• 2009

Purpose: The purpose of the experiment was to evaluating the diagnostic ability of dental caries detection using digital subtraction in the artificial caries activity model. Materials and Methods: Digital radiographies of five teeth with 8 proximal surfaces were obtained by CCD sensor (Kodak RVG 6100 using a size #2). The digital radiographic images and subtraction images from artificial proximal caries were examined and interpreted. In this study, we proposed novel caries detection method which could diagnose the dental proximal caries from single digital radiographic image. Results: In artificial caries activity model, the range of lesional depth was $572-1,374{\mu}m$ and the range of lesional area was $36.95-138.52mm^2$. The lesional depth and the area were significantly increased with demineralization time (p<0.001). Furthermore, the proximal caries detection using digital subtraction radiography showed high detection rate compared to the proximal caries examination using simple digital radiograph. Conclusion: The results demonstrated that the digital subtraction radiography from single radiographic image of artificial caries was highly efficient in the detection of dental caries compared to the data from simple digital radiograph.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.202-208
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• 2013

In the image processing for VS (Video Surveillance), the detection of moving entities in a monitored scene is an important step. A background subtraction technique has been widely employed to find the moving entities. However, the extracted foreground regions often include not only real entities but also their cast shadows, and this can cause errors in following image processing steps, such as tracking, recognition, and analysis. In this paper, a novel technique is proposed to determine the shadow pixels of moving objects in the foreground image of a VS camera. Compared to existing techniques where the same decision criteria are applied to all moving pixels, the proposed technique determines shadow pixels using local features based on two facts: First, the amount of pixel intensity drop due to a shadow depends on the intensity level of background. Second, the distribution pattern of pixel intensities remains even if a shadow is cast. The proposed method has been tested at various situations with different backgrounds and moving humans in different colors.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.271-280
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• 2009

In this paper, we propose a smoke detection method using region growing method in outdoor video sequences. Our proposed method is composed of three steps; the initial change area detection step, the boundary finding and expanding step, and the smoke classification step. In the first step, we use a background subtraction to detect changed areas in the current input frame against the background image. In difference images of the background subtraction, we calculate a binary image using a threshold value and apply morphology operations to the binary image to remove noises. In the second step, we find boundaries of the changed areas using labeling algorithm and expand the boundaries to their neighbors using the region growing algorithm. In the final step, ellipses of the boundaries are estimated using moments. We classify whether the boundary is smoke by using the temporal information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.681-684
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• 2015

Magnetization is very important in the ferro-magnetic physics and provides useful informations in the application field of magnetic devices. Generally, the only first acquired domain pattern is not helpful to recognize domain pattern. Many images are needed to visualize domain pattern through image processing. These images were obtained a 8-bit digital camera. The operation was the subtraction of pixel values of multi domain imanges from the images with 255 of pixel value, which was obtained in the saturated state of magnetic materials. The magnetic domain images was visualized gradually with increasing the number of subtracion operation. LABVIEW was used as an image processing tool and the optic microscope with a polarizer was used in this experiment.