• Journal of Advanced Navigation Technology
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• v.9 no.1
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• pp.10-18
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• 2005

These studies developed system as well as its algorithm which can measure traffic flow and vehicle speed on the highway as well as road by using industrial television(ITV) system. This algorithm used the real time processing of dynamic images. The processing algorithm of dynamic images is developed and proved its validity by frame grabber. Frame grabber can process the information of a small number of sample points only instead of the whole pixel of the images. In the techniques of this algorithm, we made approximate contour of vehicle by allocating sampling points in cross-direction of image, and recognized top of contour of vehicle. Applying these technique, we measured the number of passing vehicles of one lane as well as multilane. Speed of each vehicle is measured by computing the time difference between a pair of sample points on two sample points lines.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.875-880
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• 2013

When we generate 3D video that provides immersive and realistic feeling to users, depth information of the scene is essential. Since the resolution of the depth map captured by a depth sensor is lower than of the color image, we need to upsample the low-resolution depth map for high-resolution 3D video generation. In this paper, we propose a depth upsampling method using depth-discontinuity information. Using the high-resolution color image and the low-resolution depth map, we detect depth-discontinuity regions. Then, we define an energy function for the depth map upsampling and optimize it using the belief propagation method. Experimental results show that the proposed method outperforms other depth upsampling methods in terms of the bad pixel rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1748-1759
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• 2018

Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the $5{\times}5$ neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.5 s.305
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• pp.51-58
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• 2005

This paper proposes an efficient algorithm for extracting a tumor region from an breast ultrasound volume image by using rate-distortion (R-D) based seeded region growing. In the proposed algorithm the rate and the distortion represent the roughness of the contour and the dissimilarity of pixels in a region, respectively. Staring from an initial seed region set in each cutting plane of a volume, a pair of the seed region and one of adjacent regions whose R-D cost is minimal is searched and then they are merged into a new updated seed region. This procedure is recursively performed until the averaged R-D cost values per the number of contour pixels in the seed region becomes maxim. As a result, the final seed region has good pixel homogeneity and a much smooth contour. Finally, the tumor volume is extracted using the contours of the final seed regions in all the cutting planes. Experimental results show that the averaged error rate of the proposed method is shown to be below 4%.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.6
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• pp.42-48
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• 2007

In x-ray imaging system, two kinds of noises are involved. First, the charge generated from the radiation interaction with the detector during exposure. Second, the signal is then added by readout electronics noise. But, x-ray images are not modeled by Gaussian noise but as the realization of a Poisson process. In this paper, we apply a new approach to remove Poisson noise from medical X-ray image in the wavelet domain, the applied methods shows more excellent results in cone-beam CT.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.69-77
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• 2008

An algorithm for tracking the trace of structured light is proposed to obtain depth information accurately. The technique is based on the fact that the pixel location of light in an image has a unique association with the object depth. However, sometimes the projected light is dim or invisible due to the absorption and reflection on the surface of the object. A dynamic programming approach is proposed to solve such a problem. In this paper, necessary mathematics for implementing the algorithm is presented and the projected laser light is tracked utilizing a dynamic programming technique. Advantage is that the trace remains integrity while many parts of the laser beam are dim or invisible. Experimental results as well as the 3-D restoration are reported.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.103-111
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• 2020

Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Journal of Power System Engineering
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• v.15 no.1
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• pp.72-77
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• 2011

The handling ability of containers at the terminal strongly depends on the performance of the cargo handling system such as RTGC(Rubber Tired Gantry Crane). This paper introduces a new guide line position measurement method using a camera for the RTGC which plays a important role in the harbor area. Because the line tracking is the basic technique for control system design of RTGC, it is necessary to develop a useful and reliable measurement system. If the displacement and angle of the RTGC relative to a guide line as trajectory to follow is obtained, the position of RTGC is calculated. Therefore, in this paper, a camera-based measurement system is introduced. The proposed measurement system is robust against light fluctuation and cracks of the guideline. This system consists of a camera and a PC which are installed at the lower side of the RTGC. Two edges of the guide line are detected from an input image taken by the camera, and these positions are determined in a Hough parameter space by using the Hough transformation method. From the experimental results, high accurate standard deviations were found as 0.98 pixel of the displacement and 0.24 degree of the angle, including robustness against lighting fluctuation and cracks of the guide line also.

• Journal of Korea Multimedia Society
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• v.4 no.2
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• pp.116-129
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• 2001

To improve the performance of the inverse halftoning, smoothing masks should be designed optimally by using the accurate information of halftone cells. In this thesis, the method of energy minimization is so defined as to determine the exact information of halftone cell. A heuristic search method is proposed to obtain efficiently the parameters of halftone cells which determine the minimum energy. A halftone-peak modeling method with several functions is proposed and used to get initial values of the parameters. The dimension decomposition technique is also adopted to speed up the search process of energy minimization. Several experiments show that the proposed method extracts correct location of the seed pixel of the halftone cell and the extracted information of the halftone cell can be used to get more exactly smoothed color images. The proposed method can be applied to extract the texture patterns, to separate channel images of a scanned color halftone image, and to extract the moire area in an image.