• KIPS Transactions on Computer and Communication Systems
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• v.9 no.2
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• pp.25-30
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• 2020

The thermal image system was widely used in the defence-related industry because of detect infrared light from the object without light. but, as the demand in the security system and automobile market increases, the civilian industry are expanding to the private sector. There are difficult to apply various requirement because of previous systems are based by FPGA, so it need new system that apply to various requirement. The proposed paper is thermal image processing system using common image processor. It has various requirement and scalable to support image input/output interface and device driver. If it is used to proposed system, it reduce development cost and period than previous system based FPGA. Because there has very high accessibility. In addition, it expect to have satisfaction of customer requirements, development cost, development period, release date of product.

• Journal of the Korean Wood Science and Technology
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• v.38 no.6
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• pp.498-506
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• 2010

To use domestic softwood for structural lumber, appropriate grading system for quality, production and distribution condition of domestic lumber should be prepared. Kim et al. developed an automatic image processing system for grading domestic structural lumber (2009a and b). This study was carried out to investigate optimal image merging conditions for improving performance of image input system which is the key technique of image processing system, developed in the previous paper. To merge digital images of Korean larch lumber, choosing the green channel information of obtained image data showed the most accurate merging performance. As a pre-treatment process, applying Y-derivative Sharr's kernel filter could improve the image merging accuracy, but the effect of camera calibration was imperceptible. The optimal size of template image was verified as 30 pixel widths and 150 pixel heights. When applying the above mentioned conditions, the error length of images was 3.1 mm and the processing time was 9.7 seconds in average.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.2
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• pp.70-76
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• 2012

In this paper, an automatic extraction method of glomerulus region from human renal tissue image is presented. The important information reflecting the state of kidneys richly included in the glomeruli, so it should be the first step to extract the glomerulus region from the renal tissue image for the further quantitative analysis of the renal condition. Especially, there is no clear difference between the glomerulus and other tissues, so the glomerulus region can not be easily extracted from its background by the existing segmentation methods. The outer edge of a glomerulus region is regarded as a common property for the regions of this kind ; a two- dimensional Gaussian distribution is used to convolve with an original image first and then the image is thresholded at this blurred image ; a closed curve corresponding to the outer edge can be obtained by usual pattern processing skills like thinning, branch-cutting, hole-filling etc., Finally, the glomerulus region can be obtained by extracting the area in the original image surrounded by the closed curve. The glomerulus regions are correctly extracted by 85 percentages and experimental results show the proposed method is effective.

• 한국전자현미경학회:학술대회논문집
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• 2005.11a
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• pp.164-169
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2012.10a
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• pp.141-142
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• 2012

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.419-420
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• 2009

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.743-744
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• 2006

This paper presents implementation of parallel processing system for image processing. The parallel processing system proposed consisted of 16 processing elements, and multi-access memory system, and interface modules. The multi-access memory system we introduced is made up of a memory module selection, a data routing module, and an address calculation and routing module.

• Annual Conference of KIPS
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• 2019.05a
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• pp.431-434
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• 2019

In the case of blur processing, it is common to use a tool such as Photoshop to perform processing manually. However, it can be considered very efficient if the blur is processed at one time in the object detection process. Based on this point, we can use the object detection model to blur the objects during the process. The object detection is performed by using the YOLO [3] model. If such blur processing is used, it may be additionally applied to streaming data of video or image.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.173-176
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• 2008

Lake studies play an important role in water management, ecology, and other environmental issues. Typically, monitoring lake levels is the first step on the lake studies. However, for the Prairie Pothole Region (PPR) of North America having millions of small lakes and potholes, on-site measurement for lake levels is almost impossible with the conventional gage stations. Therefore, we employed Geographic Information System (GIS) and remote sensing approach with the Shuttle Radar Topography Mission data to extract lake levels. Several image processing techniques were used to extract lake levels for January, 2000 as a one-time snapshot which will be useful in historic lake level reconstruction. This study is associated with other remote sensing datasets such as Landsat imagery and Digital Orthophoto Quadrangle (DOQ). In this research, firstly, image processing techniques like FFT filtering, Lee-sigma, masking with Canny Edge Detector, and contouring were tested for lake level estimation. The semi-automated contouring technique was developed to accomplish the bulk processing for large amount of lakes in this region. Also, effectiveness of each method for bulk processing was evaluated.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.8
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• pp.707-716
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• 2013

Tire noise is divided into two parts. One is pattern noise the other one is road noise. Pattern noise primarily occurs in over 500 Hz frequency but road noise occurs mainly in low frequency. It is important to develop a technology to predict the pattern noise at the design stage. Prediction technology of pattern noise has been developed by using image processing. Shape of tire pattern is computed by using imaging signal processing. Its results are different with the measured one. Therefore, the prediction of actual measured pattern noise is valuable. In the signal processing theory is applied to calculate the impulse response for the measurement environment. This impulse response used for the prediction of pattern noise by convolving this impulse response by the results of image processing of tire pattern.