• Journal of Institute of Control, Robotics and Systems
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• v.21 no.8
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• pp.766-772
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• 2015

This paper proposes an object detection method based on motion estimation using background subtraction in the fisheye images obtained through omni-directional camera mounted on the vehicle. Recently, most of the vehicles installed with rear camera as a standard option, as well as various camera systems for safety. However, differently from the conventional object detection using the image obtained from the camera, the embedded system installed in the vehicle is difficult to apply a complicated algorithm because of its inherent low processing performance. In general, the embedded system needs system-dependent algorithm because it has lower processing performance than the computer. In this paper, the location of object is estimated from the information of object's motion obtained by applying a background subtraction method which compares the previous frames with the current ones. The real-time detection performance of the proposed method for object detection is verified experimentally on embedded board by comparing the proposed algorithm with the object detection based on LKOF (Lucas-Kanade optical flow).

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Journal of the Korean Society of Radiology
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• v.79 no.6
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• pp.340-347
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• 2018

Purpose: To evaluate value of image subtraction for identifying hepatocellular carcinoma (HCC) capsule on gadoxetic acid-enhanced MR images. Materials and Methods: This study involved 108 patients at risk of HCC preoperatively examined using gadoxetic acid-enhanced MRI with hepatic resection between May 2015 and February 2017. We evaluated qualities of subtraction images and presence of capsular appearance on portal venous or transitional phases conventional and subtraction images. We assessed effect of capsular appearance on subtraction images on HCC. Results: After excluding 1 patient who had treated by transarterial chemoembolization prior to surgery and 33 patients with unsatisfactory subtraction image qualities, 82 focal hepatic lesions (73 HCC, 5 non-HCC malignancies, and 4 benign) from 74 patients were analyzed. Regarding detection of capsules, sensitivity, accuracy, and area under the receiver operating characteristic curve (AUC) on subtraction images were significantly higher than those on conventional images (95.4%, 89.0%, and 0.80, respectively; p < 0.001), though specificities were same (64.7%). For diagnosis of HCC, sensitivity, accuracy, and AUC on subtraction images were significantly higher than on conventional images (82.2%, 79.3%, and 0.69, respectively; p = 0.011), though specificities were identical (55.6%). Conclusion: Portal venous or transitional phase gadoxetic acid-enhanced MRI subtraction images could improve detection of HCC capsule.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.151-156
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• 2018

In this paper, a method to classify objects in outdoor CCTV images using Convolutional Neural Network(CNN) and background subtraction is proposed. Object candidates are extracted using background subtraction and they are classified with CNN to detect objects in the image. At the end, computation complexity is highly reduced in comparison to other object detection algorithms. A database is constructed by filming alleys and playgrounds, places where crime occurs mainly. In experiments, different image sizes and experimental settings are tested to construct a best classifier detecting person. And the final classification accuracy became 80% for same camera data and 67.5% for a different camera.

• Journal of Electrical Engineering and Technology
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• v.10 no.1
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• pp.372-378
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• 2015

Background subtraction is the first processing stage in video surveillance. It is a general term for a process which aims to separate foreground objects from a background. The goal is to construct and maintain a statistical representation of the scene that the camera sees. The output of background subtraction will be an input to a higher-level process. Background subtraction under dynamic environment in the video sequences is one such complex task. It is an important research topic in image analysis and computer vision domains. This work deals background modeling based on modified adaptive Gaussian mixture model (GMM) with three temporal differencing (TTD) method in dynamic environment. The results of background subtraction on several sequences in various testing environments show that the proposed method is efficient and robust for the dynamic environment and achieves good accuracy.

• Imaging Science in Dentistry
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• v.31 no.1
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• pp.23-34
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• 2001

Purpose : To develop a new subtraction program for registering digital periapical images based on the correspondence of anatomic structures. Materials and Methods: The digital periapical images were obtained by Digora system with Rinn XCP equipment after translation of 1-16 mm, and rotation of 2-20° at the premolar and molar areas of the human dried mandible. The new subtraction program, NIH Image program and Emago/Advanced program were compared by the peak-signal-to noise ratio (PSNR). Results : The new subtraction program was superior to NIH Images program and Emagol Advanced program up to 16 mm translation and horizontal angulation up to 4°. Conclusion: The new subtraction program can be used for subtracting digital periapical images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.73-80
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• 2004

In this paper, we propose a medical image processing method for detection of periodontal disease. The proposed method is the method of an automatic image alignment and detection of minute changes, to overcome defects in the conventional subtraction radiography by digital image processing technique, that is necessary for getting subtraction image and ROI(Region of Interest) focused on a selection method using the structured features in target images. And the method services accuracy, consistency and objective information or data to results. In result, easily and visually we can identify minute differences in the affected parts whether they have problems or not, and using application system.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.29 no.2
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• pp.423-433
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• 1999

Purpose : To assess the methods for the clinical evaluation of the longitudinal bone changes after implantation of tooth ash-plaster mixture into the defect area of human jaws. Materials and methods : Tooth ash-plaster mixtures were implanted into the defects of 8 human jaws. 48 intraoral radiograms taken with copper step wedge as reference at soon, 1st, 2nd, 4th, and 6th week after implantation of mixture were used. X-ray taking was standardized by using Rinn XCP device customized directly to the individual dentition with resin bite block. The images inputted by Quick scanner were digitized and analyzed by NIH image program. Cu­equivalent values were measured at the implanted sites from the periodic digital images. Analysis was performed by the bidirectional subtraction with color enhancement and the surface plot of resliced contiguous image. The obtained results by the two methods were compared with Cu­equivalent value changes. Results : The average determination coefficient of Cu-equivalent equations was 0.9988 and the coefficient of variation of measured Cu values ranged from 0.08~0.10. The coefficient of variation of Cu-equivalent values measured at the areas of the mixture and the bone by the conversion equation ranged from 0.06 ~0.09. The analyzed results by the bidirectional subtraction with color enhancement were coincident with the changes of Cu-equivalent values. The surface plot of the resliced contiguous image showed the three dimensional view of the longitudinal bone changes on one image and also coincident with Cu-equivalent value changes after implantation. Conclusion : The bidirectional subtraction with color enhancement and the surface plot of the resliced contiguous image was very effective and reasonable to analyze clinically and qualitatively the longitudinal bone change. These methods are expected to be applicable to the non-destructive test in other fields.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.133-138
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• 2016

Motion detection algorithms based on difference image are classified into background subtraction and previous frame subtraction. 1) Background subtraction is a convenient and effective method for detecting foreground objects in a stationary background. However in real world scenarios, especially outdoors, this restriction, (i.e., stationary background) often turns out to be impractical since the background may not be stable. 2) Previous frame subtraction is a simple technique for detecting motion in an image. The difference between two frames depends upon the amount of motion that occurs from one frame to the next. Both these straightforward methods fail when the object moves very "slightly and slowly". In order to efficiently deal with the problem, in this paper we present an algorithm for motion detection that incorporates "reflected light area" and "difference image". This reflected light area is generated during the frame production process. It processes multiplex difference image and AND-arithmetic of bitwise. This process incorporates the accuracy of background subtraction and environmental adaptability of previous frame subtraction and reduces noise generation. Also, the performance of the proposed method is demonstrated by the performance assessment of each method using Gait database sample of CASIA.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.138-142
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• 2016

Dual-energy digital radiography (DEDR) has been used for detecting lesions within the body using energy subtraction methods. The purpose of this study was to acquire optimal bone and tissue image by changing physical factors such as Tube voltage (kVp) and add filters, and then we compared with the predicted values using SRS-78 program and experimental results. For that purpose, we acquired images according to changes in physical parameters of various materials since we had to acquire the optimal bone and tissue image using energy subtraction. Used phantom consists of aluminum and polymethyl methacrylate (PMMA) and a comparison of image optimization was measured by contrast-to-noise ratio (CNR). In results, first of all, we confirmed that a subtraction image from 50 kVp image and 120 kVp image is optimal bone and tissue image. Also when we added a 10 mm Aluminum add filter, we expected it is a result of the optimal bone and tissue image. Besides, we confirmed these results are consistent with the predicted optimized condition by SRS-78 program.. In conclusion, we indicated that we can acquire optimal bone and tissue image by controling physical factors such as kVp, add filters through this study. Also we expected that DEDR will contribute to the field of medical imaging technology.