• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.327-334
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• 2024

In this paper, we developed a system with a near-infrared LED light source with a wavelength of 950nm to acquire palm vein images and a white LED light source to acquire palm creases based on Raspberry Pi. In addition, we implemented a unique pattern-extractable image processing technology that can prevent counterfeiting and enhance security of mixed creases and palmprints through image pre-processing (Gray scaling, Histogram Equalization, Blurring, Thresholding, Thinning) for the acquired vein and palm images, and secured a source technology that can be used in a security-enhanced system.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1171-1176
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• 2003

In this paper, we propose an effective bar code detection algorithm using the feature analysis and the labeling. After computing the direction of pixels using four line operators, we obtain the histogram about the direction of pixels by a block unit. We calculate the difference between the maximum value and the minimum value of the histogram and consider the block that have the largest difference value as the block of the bar code region. We get the line passing by the bar code region with the selected block but detect blocks of interest to get the more accurate line. The largest difference value is used to decide the threshold value to obtain the binary image. After obtaining a binary image, we do the labeling about the binary image. Therefore, we find blocks of interest in the bar code region. We calculate the gradient and the center of the bar code with blocks of interest, and then get the line passing by the bar code and detect the bar code. As we obtain the gray level of the line passing by the bar code, we grasp the information of the bar code.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.320-323
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• 2014

Today is going with the advancement of intelligent transportation systems and traffic environment and helping to provide safe and convenient service through a mobile device work with the popularization of the vehicle black box. The traffic flow by a variety of causes is constantly changing, it is often unable to prepare the driver, depending on external factors can not be controlled by the power of the public, leading to a major accident. The system needs to pass the real-time weather data in the inter-operator to prevent this. The proposed detection algorithm weather information delivered real-time weather information for this paper. The weather condition is detected by using the contrast between the histogram of the motion of the wiper and the clear day algorithm. In general, the wiper is worked in extreme weather conditions that will have a value different contrast due to rain or snow. Situation was considered clear, snowy conditions, such as using it on a rainy situation. First, designated as ROI (Region Of Interest) of the minimum area that can be detected in order to reduce the amount of calculation for the wiper, the wiper, which was detected through the operation of the threshold Thresholding the brightness of the vehicle wiper. In addition, we distinguish the value of each meteorological situation by using contrast. Results was obtained to 80% for the snow conditions, a rainy situation.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.181-188
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• 2012

An effective extraction method for extracting a coated tongue is proposed in this paper, which is used as the diagnostic criteria in the tongue diagnosis. Proposed method uses the fluorescence response characteristics of the coated tongue that is occurred by using the ultraviolet light. Specially, this method can solved the previous problems including the issue in the limits of the diagnosis environment and in the objectivity of the diagnosis results. In our method, original tongue image is acquired by using the ultraviolet light, and binarization is performed by thresholding a valley-points in the histogram that corresponds to the color difference of tongue body and tongue coating. Final view image is presented to the oriental doctor, after applying the canny-edge algorithm to the binary image, and edge image is added to the original image. In order to evaluate the performance of the our proposed method, after building a various tongue image, we compared the true region of coated tongue by the oriental doctor's hand with the extracted region by the our method. As a result, the proposed method showed the average 87.87% extraction ratio. The shape of the extracted coated tongue region showed also significantly higher similarity.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.105-114
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• 2015

This paper proposes methods to remove background area and segment region of interest (ROI) in chest X-ray images. Conventional algorithms to improve detail or contrast of images normally utilize brightness and frequency information. If we apply such algorithms to the entire images, we cannot obtain reliable visual quality due to unnecessary information such as background area. So, we propose two effective algorithms to remove background and segment ROI from the input X-ray images. First, the background removal algorithm analyzes the histogram distribution of the input X-ray image. Next, the initial background is estimated by a proper thresholding on histogram domain, and it is removed. Finally, the body contour or background area is refined by using a popular guided filter. On the other hand, the ROI, i.e., lung segmentation algorithm first determines an initial bounding box using the lung's inherent location information. Next, the main intensity value of the lung is computed by vertical cumulative sum within the initial bounding box. Then, probable outliers are removed by using a specific labeling and the pre-determined background information. Finally, a bounding box including lung is obtained. Simulation results show that the proposed background removal and ROI segmentation algorithms outperform the previous works.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3106-3106
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• 2001

Imaging spectrometry or hyperspectral imaging is a recent development that makes possible quantitative and qualitative measurement for food quality and safety. This paper presents the research results that a hyperspectral imaging system can be used effectively for detecting fecal (from duodenum, cecum, and colon) and ingesta contamination on poultry carcasses from the different feed meals (wheat, mile, and corn with soybean) for poultry safety inspection. A hyperspectral imaging system has been developed and tested for the identification of fecal and ingesta surface contamination on poultry carcasses. Hypercube image data including both spectral and spatial domains between 430 and 900 nm were acquired from poultry carcasses with fecal and ingesta contamination. A transportable hyperspectral imaging system including fiber optically fabricated line lights, motorized lens control for line scans, and hypercube image data from contaminated carcasses with different feeds are presented. Calibration method of a hyperspectral imaging system is demonstrated using different lighting sources and reflectance panels. Principal Component and Minimum Noise Fraction transformations will be discussed to characterize hyperspectral images and further image processing algorithms such as image band ratio of dual-wavelength images and its histogram stretching with thresholding process will be demonstrated to identify fecal and ingesta materials on poultry carcasses. This algorithm could be further applied for real-time classification of fecal and ingesta contamination on poultry carcasses in the poultry processing line.

• Journal of Korean Society for Geospatial Information Science
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• v.2 no.2 s.4
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• pp.131-142
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• 1994

Generally, there are two methods which generates the base map of Geo-Spatial Information System(GSIS). one is the digitizing of existing map, and the other is the analytical plotting method editing data acquired by sensors using computers. But the analytical plotting method and method of the digitizing of existing map is technically complex and has the disadvantages in the costs and time. The subject region of study(the Kwangyang province), was photographed by aircraft, and photographing scale was 1/6,000. Then this area was divided into two specific regions, the residential area, and the agricultural area. In this study, we developed the algorithm that generated base map of database in GSIS from the aerial photo. This algorithm is as followed. First, the digital aerial photos were generated using these aerial photos. Second, these digital aerial photos were enhanced by implementing the histogram equalization. Third, the objects of the enhanced images were extracted by implementing thresholding and edged detection techiques of image segmentation. Finally, these images could be used to updated the base map of database in GSIS. The result obtained from this study showed that method used by this study were more efficient than existing method in costs and time.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.216-224
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• 2013

The within-class variance of Otsu's method is moderate but improper in expressing class statistical distributions. Otsu's method uses a variance to represent the distribution of each class. The variance utilizes a distance square from the mean to a data. This process is not proper in denoting a real class statistical distribution because of the distance square. In this paper, to express more exact class statistical distributions, the within-class standard deviation as a criterion for threshold selection is proposed and then the optimal threshold is determined by minimizing it. In order to have validity, it is shown through the experimental results that the proposed method was more superior to the counterparts.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.73-79
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• 2014

In this paper, we proposed the mechanism which can measure traffic flow and vehicle speed on the highway as well as road by using the video and image processing to detect and track cars in a video sequence. The proposed mechanism uses the first few frames of the video stream to estimate the background image. The visual tracking system is a simple algorithm based on the sum of absolute frame difference. It subtracts the background from each video frame to produce foreground images. By thresholding and performing morphological closing on each foreground image, the proposed mechanism produces binary feature images, which are shown in the threshold window. By measuring the distance between the "first white line" mark and the "second white line"mark proceeding, it is possible to find the car's position. Average velocity is defined as the change in position of an object divided by the time over which the change takes place. The results of proposed mechanism agree well with the measured data, and view the results in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.684-689
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• 2005

In this paper, we propose car license plate recognition using morphological information and an enhanced neural network. Morphological information on horizontal and vertical edges was used to extract the license plate from a car image. We used a contour tracking algorithm combined with the method of histogram and location information to extract individual characters in the extracted plate. The enhanced neural network is proposed for recognizing them, which has the method of combining the ART-1 and the supervised teaming method. The proposed method has applied to real world car images. The experimental results show that the proposed method has better the extraction rates than the methods with information of the thresholding, the RGB and the HSI, respectively. And the proposed neural network has better recognition performance than the conventional neural networks.