• Journal of the Institute of Electronics Engineers of Korea SD
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• v.48 no.6
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• pp.15-24
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• 2011

As increase awareness of intelligent vehicle systems, many kinds of lane detection algorithm have been proposed. General boundary extraction method can bring good result in detection of lane on the road. But a shadow on the road, or other boundaries, such as horizontal lines can be detected. The method using morphological operations was used to extract information about Lane. By applying HSV color model for color information of lane, the candidate of the lane can be extracted. In this paper, the lane detection region was set by Hough transformation using the candidate of the lane. By extracting lane markings on the lane detection region, lane detection method can bring good result.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.55-62
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• 2013

This paper aims to detect Speed Bump by using Omni-directional Camera and to suggest Real-time update scheme of Speed Bump through Vision Based Approach. In order to detect Speed Bump from sequence of camera images, noise should be removed as well as spot estimated as shape and pattern for speed bump should be detected first. Now that speed bump has a regular form of white and yellow area, we extracted speed bump on the road by applying erosion and dilation morphological operations and by using the HSV color model. By collecting huge panoramic images from the camera, we are able to detect the target object and to calculate the distance through GPS log data. Last but not least, we evaluated accuracy of obtained result and detection algorithm by implementing SLAMS (Simultaneous Localization and Mapping system).

• Journal of the Korean institute of surface engineering
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• v.54 no.5
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• pp.278-284
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• 2021

Corrosion of steel plate is common phenomenon which results in the gradual destruction caused by a wide variety of environments. Corrosion monitoring is the tracking of the degradation progress for a long period of time. Corrosion on steel plate appears as a discoloration and any irregularities on the surface. In this study, we developed a quantitative evaluation method of the rust formed on steel plate by using k-means clustering from the corroded area in a given image. The k-means clustering for automated corrosion detection was based on the GrabCut segmentation and Gaussian mixture model(GMM). Image color of the corroded surface at cut-edge area was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space.

• The Transactions of the Korea Information Processing Society
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• v.7 no.9
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• pp.3037-3047
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• 2000

This paper describes a method of tracking a close leading vehicle by color image processing using the pairs of tail and brake lights. which emit red light and are housed on the rear of the vehicle in stop-and-go traffic condition. In the color image converted as an HSV color model. candidate regions of rear lights are identified using the color features of a pair of lights. Then. the pair of tailor brake lights are detected by means of the geometrical features and location features for the pattern of the tail and brake lights. The location of the leading vehicle can be estimated by the location of the detected lights and the vehicle can be tracked continuously. It is also possible to detect the braking status of the leading vehicle by measuring the change in HSV color components of the pair of lights detected. In the experiment. this method tracked a leading vehicle successfully from urban road images and was more useful at night than in the daylight. The KAV-Ill (Korea Autonomous Vehicle- Ill) equipped with a color vision system implementing this algorithm was able to follow a leading vehicle autonomously at speeds of up to 15km!h on a paved road at night. This method might be useful for developing an LSA (Low Speed Automation) system that can relieve driver's stress in the stop-and-go traffic conditions encountered on urban roads.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.1
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• pp.57-64
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• 2013

In this paper, we propose the classification method of medicine bottle images using the features with color and size information. It is difficult to classify with size feature only, because there are many similar sizes of bottles. Therefore, we suggest a classification method based on color information, which robust to illumination variations. First, we extract MBR(Minimum Boundary Rectangle) of medicine bottle area using Binary Threshold of Red, Green, and Blue in image and classify images with size. Then, hue information and RGB color average rate are used to classify image, which features are robust to lighting variations. Finally, using SURF(Speed Up Robust Features) algorithm, corresponding image can be found from candidates with previous extracted features. The proposed method makes to reduce execution time and minimize the error rate and is confirmed to be reliable and efficient from experiment.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.65-84
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• 2006

In this paper, we present an improved image formation model and propose a color image enhancement based on the model. In the presented image formation model, an input image is represented as a product of global illumination, local illumination, and reflectance. In the proposed color image enhancement, an input RGB color image is converted into an HSV color image. Under the assumption of white-light illumination, the H and S component images are remained as they are and the V component image only is enhanced based on the image formation model. The global illumination is estimated by applying a linear LPF with wide support region to the input V component image and the local illumination by applying a JND (just noticeable difference)-based nonlinear LPF with narrow support region to the processed image, where the estimated global illumination is eliminated from the input V component image. The reflectance is estimated by dividing the input V component image by the estimated global and local illuminations. After performing the gamma correction on the three estimated components, the output V component image is obtained from their product. Histogram modeling is next executed such that the final output V component image is obtained. Finally an output RGB color image is obtained from the H and S component images of the input color image and the final output V component image. Experimental results for the test image DB built with color images downloaded from NASA homepage and MPEG-7 CCD color images show that the proposed method gives output color images of very well-increased global and local contrast without halo effect and color shift.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06a
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• pp.271-272
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• 2008

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1603-1613
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• 2017

This paper proposes a method for effectively segmenting the hand region using a widely popular RGB color-based webcam. This performs the empirical preprocessing method four times to remove the noise. First, we use Gaussian smoothing to remove the overall image noise. Next, the RGB image is converted into the HSV and the YCbCr color model, and global fixed binarization is performed based on the statistical value for each color model, and the noise is removed by the bitwise-OR operation. Then, RDP and flood fill algorithms are used to perform contour approximation and inner area fill operations to remove noise. Finally, ROI (hand region) is selected by eliminating noise through morphological operation and determining a threshold value proportional to the image size. This study focuses on the noise reduction and can be used as a base technology of gesture recognition application.

• Journal of the Korea Computer Graphics Society
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• v.27 no.2
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• pp.1-11
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• 2021

In a virtual environment, a 3D workspace and 3D interaction are possible, but most virtual reality applications use a 2D color picker. This paper implements a 3D color picker based on 3D color space in a virtual environment, and compares color selection performance with the existing 2D color picker. The 3D color picker is intuitive by using the 3D color space as it is, and it can position the 3D pointer at a specific point in the color space using a controller, which is a virtual reality device, so a user can select a color in one step. On the other hand, the 2D color picker has the advantage of being familiar with existing users who work with colors in a computer environment, but has a disadvantage that requires several steps of user interaction since it has to set color properties through 2D interfaces. Based on user experiments, we confirmed the usefulness of a 3D color picker in addition to a 2D color picker in a virtual environment, and it was possible to perform natural 3D work in a virtual environment using the 3D color picker.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.547-555
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• 2014

This paper presents a method for hand segmentation using depth information, and adaptive threshold by means of histogram analysis and color clustering in HSV color model. We consider hand area as a nearer object to the camera than background on depth information. And the threshold of hand color is adaptively determined by clustering using the matching of color values on the input image with one of the regions of hue histogram. Experimental results demonstrate 95% accuracy rate. Thus, we confirmed that the proposed method is effective for hand segmentation in variations of hand color, scale, rotation, pose, different lightning conditions and any colored background.