• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.667-675
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• 2013

This paper presents vision-based techniques for underwater landmark detection, map-based localization, and SLAM (Simultaneous Localization and Mapping) in structured underwater environments. A variety of underwater tasks require an underwater robot to be able to successfully perform autonomous navigation, but the available sensors for accurate localization are limited. A vision sensor among the available sensors is very useful for performing short range tasks, in spite of harsh underwater conditions including low visibility, noise, and large areas of featureless topography. To overcome these problems and to a utilize vision sensor for underwater localization, we propose a novel vision-based object detection technique to be applied to MCL (Monte Carlo Localization) and EKF (Extended Kalman Filter)-based SLAM algorithms. In the image processing step, a weighted correlation coefficient-based template matching and color-based image segmentation method are proposed to improve the conventional approach. In the localization step, in order to apply the landmark detection results to MCL and EKF-SLAM, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform in an indoor water tank and the results are discussed.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1425-1432
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• 2010

In large construction site, although soil conversion factor is so significant to preliminary design, operation design and calculating the cost of construction that it is important to take reasonable estimation and application, the standard of soil conversion factor for weathered ground doesn't clearly suggested yet. So in this study, at first we obtain the image using DSLR - high resolution camera and Laser scanner in the Haeng-Bok city constructin site, then analysis the ratio of soil and rock using various image processing method(Sobel method, Laplace method, Highpass filter, Hue and Saturation analysis). Mutual comparation with the result of image processing analysis and manual segmentation of 5case image in the cad. As a result, best image processing method was different for each case. In case of high propotion of rock, Laplace was best and in case of high propotion of soil, Highpass was best, and mixed case Laplace was best.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.2
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• pp.1-8
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• 2003

This paper proposes an object-based image retrieval technique based on the dominant color pair information. Most of existing methods for content based retrieval extract the features from an image as a whole, instead of an object of interest. As a result, the retrieval performance tends to degrade due to the background colors. This paper proposes an object based retrieval scheme, in which an object of interest is used as a query and the similarity is measured on candidate regions of DB images where the object may exist. From the segmented image, the dominant color pair information between adjacent regions is used for selecting candidate regions. The similarity between the query image and DB image is measured by using the color correlogram technique. The dominant color pair information is robust against translation, rotation, and scaling. Experimental results show that the performance of the proposed method has been improved by reducing the errors caused by background colors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.594-601
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• 2009

Shooting simulation systems not only reduce a great amount of expense and time for military exercises but also prevent accidents. In particular, the shooting simulation systems using laser beam have an advantage which is very similar to the shooting exercise that uses real bullets. However, real time technique for laser beam recognition in a target image is necessary. The method proposed in this paper takes a difference image from two adjacent image frames. Then a thresholding is applied on this difference image to discriminate laser beam from background. To decide the threshold value the intensity distribution of background points is modeled assuming normal distribution. Then a noise reduction and a region segmentation are applied on the binary image to find the position of a laser beam. The time complexity of this process depends on the size of an image multiplied by the size of a mask used in the noise reduction process. The experimental result showed that the accuracy of the system was 93.3%. Even in the inaccurate cases the beam was always found in the resultant region.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.89-100
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• 2006

The carotid intima-media thickness (IMT) is very important, because the severity of it is an independent predictor of transient cerebral ischemia, stroke, and coronary events such as myocardial infarction. The conventional image processing to measure the IMT has not been satisfactory, because the methods have relied on the manual section drawing and a regional segmentation by differential estimation. We propose a new image processing technology effective to extract features from the carotid artery image whose pixels have the directional vector properties with composed color distribution. The technique we presented here is not by differential variation but by verification of the layer properties of carotid artery image. Iterated vertical and horizontal analysis and segmentation of the IMT image show the vector characteristics. This new technique makes it possible to cluster the layers statistically, and to classify mathematical correlation between regions and resulting in correct measurements of thickness and its variation. The advantages and effectiveness of this approach are applicable to region process and character extraction of such a vector image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.907-910
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• 2011

This paper proposes a character segmentation algorithm of steel plate images composed of adaptive binarization by the SCW (Sliding Concentric Windows) technique, the object labelling by CCA (Connected Component Analysis), and 2D projection method. The SCW technique carries out the grayscale-to-binary image conversion in consideration of local characteristics of images. The character decision algorithm followed by the labelling technique by CCA (Connected Component Analysis) determines the character area effectively reducing the noise effect. The 2D projection with horizontal and vertical directions produces a tight bounding box for a character based on the cross points. Experimental results indicate that the proposed algorithm segments the characters in steel plate images effectively. The proposed algorithm can be applied to the devices with limited resources due to its excellent performance and low complexity.

• ETRI Journal
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• v.29 no.3
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• pp.353-362
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• 2007

In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low-DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher-order statistics map, which represents the spatial distribution of the high-frequency components, is obtained from an input low-DOF image. The second module locates the block-based OOI for further processing. Using the block-based OOI, the final OOI is obtained with pixel-level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low-DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photo-realistic video scene generation.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.154-154
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• 2017

Root is an important organ of plant that typically lies below the surface of the soil. Root surface determines the ability of plants to absorb nutrient and water from the surrounding soil. This study describes an application of image processing and computer vision which was implemented for rapid measurement of seed germination such as root length, surface area, average diameter, branching points of roots. A CCD camera was used to obtain RGB image of seed germination which have been planted by wet paper in a humidity chamber. Temperature was controlled at approximately 250C and 90% relative humidity. Pre-processing techniques such as color space, binarized image by customized threshold, removal noise, dilation, skeleton method were applied to the obtained images for root segmentation. The various morphological parameters of roots were estimated from a root skeleton image with the accuracy of 95% and the speed of within 10 seconds. These results demonstrated the high potential of computer vision technique for the measurement of seed germination.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.223-227
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• 1996

In this paper we propose an adaptive fast image restoration filter, which is suitable for reducing the blocking artifacts in the compressed image in real-time. The proposed restoration filter is based on the observation that quantization operation in a series of coding process is a nonlinear and many-to-one mapping operator. And then we propose an approximated version of constrained optimization technique as a restoration process for removing the nonlinear and space varying degradation operator. We also propose a novel block classification method for adaptively choosing the direction of a highpass filter, which serves as a constraint in the optimization process. The proposed classification method adopts the bias-corrected maximized likelihood, which is used to determine the number of regions in the image for the unsupervised segmentation. The proposed restoration filter can be realized either in the discrete Fourier transform domain or in the spatial domain in the form of a truncated finite impulse response (FIR) filter structure for real-time processing. In order to demonstrate the validity of the proposed restoration filter experimental results will be shown.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.481-489
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• 2019

Change detection, one of the main applications of multi-temporal satellite images, is an indicator that directly reflects changes in human activity. Change detection can be divided into pixel-based change detection and object-based change detection. Although pixel-based change detection is traditional method which is mostly used because of its simple algorithms and relatively easy quantitative analysis, applying this method in VHR (Very High Resolution) images cause misdetection or noise. Because of this, pixel-based change detection is less utilized in VHR images. In addition, the sensor of acquisition or geographical characteristics bring registration noise even if co-registration is conducted. Registration noise is a barrier that reduces accuracy when extracting spatial information for utilizing VHR images. In this study object-based change detection of VHR images was performed considering registration noise. In this case, object-based change detection results were derived considering various pixel-based change detection methods, and the major voting technique was applied in the process with segmentation image. The final object-based change detection result applied by the proposed method was compared its performance with other results through reference data.