• Proceedings of the KSRS Conference
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• v.2
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• pp.772-775
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• 2006

Muilti-dimensional feature vector matching algorithm uses multiple features such as intensity, gradient, variance, first or second derivative of a pixel to find correspondence pixels in stereo images. In this paper, we proposed a new method for adjusting automatically the weight of feature in multi-dimensional feature vector matching considering sharpeness of a pixel in feature vector distance curve. The sharpeness consists of minimum and maximum vector distances of a small window mask. In the experiment we used IKONOS satellite stereo imagery and obtained accurate matching results comparable to the manual weight-adjusting method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.108-116
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• 2008

In this paper, we propose an algorithm that can estimate the distance using disparity based on stereo vision system, even though the obstacle is located in long ranges as well as short ranges. We use sub-pixel interpolation to minimize quantization errors which deteriorate the distance accuracy when calculating the distance with integer disparity, and also we use enhanced adaptive fuzzy strong tracking Kalman filter(EAFSTKF) to improve the distance accuracy and track the path optimally. The proposed method can solve the divergence problem caused by nonlinear dynamics such as various vehicle movements in the conventional Kalman filter(CKF), and also enhance the distance accuracy and reliability. Our simulation results show that the performance of our method improves by about 13.5% compared to other methods in point of root mean square error rate(RMSER).

• Korean Journal of Remote Sensing
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• v.36 no.1
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• pp.55-65
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• 2020

Image classification needs the spectral similarity comparison between spectral features of each pixel and the representative spectral features of each class. The spectral similarity is obtained by computing the spectral feature vector distance between the pixel and the class. Each spectral feature contributes differently in the image classification depending on the class separability of the spectral feature, which is computed using a suitable vector distance measure such as the Bhattacharyya distance. We propose a method to determine the weight value of each spectral feature in the computation of feature vector distance for the similarity measurement. The weight value is determined by the ratio between each feature separability value to the total separability values of all the spectral features. We created ten spectral features consisting of seven bands of Landsat-8 OLI image and three indices, NDVI, NDWI and NDBI. For three experimental test sites, we obtained the overall accuracies between 95.0% and 97.5% and the kappa coefficients between 90.43% and 94.47%.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.542-551
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• 2005

A new distance measurement method with the use of a single camera and a rotating mirror is presented. A camera in front of a rotating mirror acquires a sequence of reflected images, from which distance information is extracted. The distance measurement is based on the idea that the corresponding pixel of an object point at a longer distance moves at a higher speed in a sequence of images in this type of system setting. Distance measurement based on such pixel movement is investigated. Like many other image-based techniques, this presented technique requires matching corresponding points in two images. To alleviate such difficulty, two kinds of techniques of image tracking through the sequence of images and the utilization of multiple sets of image frames are described. Precision improvement is possible and is one attractive merit. The presented approach with a rotating mirror is especially suitable for such multiple measurements. The imprecision caused by the physical limit could be improved through making several measurements and taking an average. In this paper, mathematics necessary for implementing the technique is derived and presented. Also, the error sensitivities of related parameters are analyzed. Experimental results using the real camera-mirror setup are reported.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.03b
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• pp.5-5
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• 2010

The depth of the object pointed by the laser beam is computed depending on the pixel position on the CCD There involved several number of internal and external parameters such as inter-pixel distance, focal length, position and orientation of the system components in the depth measurement error. In this paper, it is shown through the error sensitivity analysis of the parameters that the most important parameters in the sense of error sources are the angle of the laser beam and the inter pixel distance. Also, the calibration technique to minimize their effect for the depth computation is proposed.

• Journal of Information Technology Applications and Management
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• v.10 no.3
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• pp.45-54
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• 2003

Security system with web camera remarkably has been developed at an Internet era. Using transmitted images from remote camera, the system can recognize current situation and take a proper action through web. Existing motion detection methods use simply difference image, background image techniques or block matching algorithm which establish initial block by set search area and find similar block. But these methods are difficult to detect exact motion because of useless noise. In this paper, the proposed method is updating changed background image as much as $N{\times}M$pixel mask as time goes on after get a difference between imput image and first background image. And checking image pixel can efficiently detect motion by computing fixed distance pixel instead of operate all pixel.

• 한국정보디스플레이학회:학술대회논문집
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• 2006.08a
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• pp.621-624
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• 2006

Pixel structure of the fringe-field switching (FFS) mode to improve transmittance has been studied. New FFS structures related to the electrode structure at the edges of pixel, and the pixel electrode width and distance between electrodes was optimized. The former improves transmittance by minimizing the region of disclination lines. The latter improves it by increasing light efficiency above center of electrodes and also response time.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.49-52
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• 2002

This paper presents a fast algorithm for face detection from color images on internet. We use Mahalanobis distance between standard skin color and actual pixel color on IQ color space to segment skin color regions. The skin color regions are the candidate face region. Further, the locations of eyes and mouth regions are found by computing average pixel values on horizontal and vertical pixel lines. The geometry of mouth and eye locations is compared to the standard face geometry to eliminate false face regions. Our Method is simple and fast so that it can be applied to face search engine for internet.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1667-1671
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• 2015

Multi-level holographic data storage is a candidate for the next generation data storage system, since it can store more than one bit per pixel. It is possible to increase the number of codewords if the number of levels is increased, and the code with an appropriate selection of codewords can also increase the minimum distance. In this paper, we propose three multi-level modulation codes of the code rate 1 bit/pixel and compare the performance according to the minimum distance. The result shows that the code with small number of levels is better than that of large number of levels because it is hard to detect threshold value.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.379-383
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• 2015

This paper presents an approach to detect object distance with the use of the recently developed low-cost Kinect sensor. The technique is based on Kinect color depth-image processing and can be used to design various computer-vision applications, such as object recognition, video surveillance, and autonomous path finding. The proposed technique uses keypoint feature detection in the Kinect depth image and advantages of depth pixels to directly obtain the feature distance in the depth images. This highly reduces the computational overhead and obtains the pixel distance in the Kinect captured images.