• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1099-1102
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• 2005

In this paper, a new visual servo method, which uses 5 axis picomotor to compensate the misalignment generated between a SLM and a CCD in a holographic storage device, was proposed and the effectiveness of it was proved by the experiment. In a holographic storage device, the data processing is done by the SLM and the CCD, and the shape of data is 2 dimensional binary patterns. Therefore, the exact image matching between the SLM and the CCD is very important, and the mismatching of it causes the errors in the data reconstruction. First, the brief introduction of a holographic data storage is given, then, BER concept which is errors caused by pixel mismatch between the SLM and the CCD is defined. Second, the geometric relation between 5 axis picomotor and the CCD movement is studied. Finally, the visual servo method using 5 axis picomotor to reduce the BER in a holographic storage device is proposed and experimented. From the experiment, we find that about 3% BER improvement is obtained by the proposed method.

• The KIPS Transactions:PartB
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• v.17B no.2
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• pp.115-124
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• 2010

Stereo matching techniques are categorized in two major schemes, local and global matching techniques. In global matching schemes, several investigations are introduced, where cost accumulation is performed in multiple matching lines. In this paper, we introduce a new multi-line stereo matching techniques which expands a conventional single-line matching scheme to multiple one. Matching cost is based on simple normalized cross correlation. We expand the scan-line optimization technique to a multi-line scan-line optimization technique. The proposed technique first generates a reliability image, which is iteratively updated based on the previous reliability measure. After some number of iterations, the reliability image is completed by a hole-filling algorithm. The hole-filling algorithm introduces a disparity score table which records the disparity score of the current pixel. The disparity of an empty pixel is determined by comparing the scores of the neighboring pixels. The proposed technique is tested using the Middlebury and CMU stereo images. The error analysis shows that the proposed matching technique yields better performance than using conventional global matching algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.4
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• pp.387-394
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• 2016

A stereo matching method is one of the ways to obtain a depth value from two dimensional images. This method estimates the depth value of target images using stereo images which have two different viewpoints. In the result of stereo matching, the depth value is represented by a disparity value. The disparity means a distance difference between a current pixel in one side of stereo images and its corresponding point in the other side of stereo images. The stereo matching in a homogeneous region is always difficult to find corresponding points because there are no textures in that region. In this paper, we propose a novel matching equation using the distance transform to estimate accurate disparity values in the homogeneous region. The distance transform calculates pixel distances from the edge region. For this reason, pixels in the homogeneous region have specific values when we apply this transform to pixels in that region. Therefore, the stereo matching method using the distance transform improves the matching accuracy in the homogeneous regions. In addition, we also propose an adaptive matching cost computation using a kernel of one dimensional array depending on the characteristic of regions in the image. In order to aggregate the matching cost, we apply a cross-scale cost aggregation method to our proposed method. As a result, the proposed method has a lower average error rate than that of the conventional method in all regions.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.3
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• pp.32-40
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• 1999

In this paper, we present a fast block matching motion estimation algorithm based on successive elimination algorithm (SEA). Based on the characteristic of center-biased motion vector distribution in the search area, the proposed method improves the performance of the SEA with a reduced the number of the search positions in the search area, In addition, to reduce the computational load, this method is combined with both the reduced bits mean absolute difference (RBMAD) matching criterion which can be reduced the computation complexity of pixel comparison in the block matching and pixel decimation technique which reduce the number of pixels used in block matching. Simulation results show that the proposed method provides better performance than existing fast algorithms and similar to full-search block motion estimation algorithm.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.4
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• pp.16-25
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• 2000

In this paper, a new disparity detection algorithm which is robust to noise is presented. It detects the disparity of an arbitrary pixel through the iterative competition with neighbor pixels in the range of disparity. A diffusion process to improve stereo matching confidence is used prior to detecting disparity of an arbitrary pixel. It is used for aggregating initial matching measure of the difference map. If the image region for matching is too small, a wrong match might be found due to noise. On the contrary, the region is too big, it results in blurring of object boundaries. Therefore, we decide the image region for matching by using the diffusion process for aggregating matching measure, then detect the true disparity with proposed competition method to the distribution of matching measure. Through the proposed method we get the result of improving matching rate of 6.96% with real stereo imge. From the simulation with the stereo imge, the proposed disparity detection method significantly outperforms the conventional method to matching rate.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.52-57
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• 2007

In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method.

• 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.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.84-92
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• 1998

In this paper, we propose the novel cost functions for finding the disparity between the left and the right images in the stereo matching problem. The dynamic programming method is used in solving the stereo matching problem by Cox et al[10]. In the reference[10], only the intensity of the pixels in the epipolar line is used as the cost functions to find the corresponding pixels. We propose the two new cost functions. The information of the slope of the pixel is introduced to the constraints in determining the weights of intensity and direction(the historical information). The pixels with the higher slope are matched mainly by the intensity of pixels. As the slope becomes lower, the matching is performed mainly by the direction. Secondly, the disparity information of the previous epipolar line the pixel is used to find the disparity of the current epipolar line. If the pixel in the left epipolar line, $p-i$ and the pixel in the right epipolar line, $p-j$ satisfy the following conditions, the higher matching probability is given to the pixels, $p-i$ and $p-j$. i) The pixels, $p-i$ and $p-j$ are the pixles on the edges in the left and the right images, respectively. ⅱ) For the pixels $p-k$ and $p-l$ in the previous epipolar line, $p-k$and $p-l$ are matched and are the pixels on the same edge with $p-i$ and $p-j$, respectively. The proposed method compared with the original method[10] finds the better matching results for the test images.

• Proceedings of the KIEE Conference
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• 2000.11d
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• pp.747-749
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• 2000

In this paper, we proposed color stereo matching algorithm using dynamic programming. The conventional gray stereo matching algorithms show blur at depth discontinuities and non-existence of matching pixel in occlusion lesions. Also it accompanies matching error by lack of matching information in the untextured region. This paper defines new cost function makes up for the problems happening in conventional gray stereo matching algorithm. New cost function contain the following properties. I) Edge points are corresponded to edge points. ii) Non-edge points are corresponded to non-edge points. iii) In case of exiting the amount of edges, the cost function has some weight in proportion to path distance. Proposed algorithm was applied in various images obtained by parallel camera model. As the result, proposed algorithm showed improved performance in the aspect of matching error and processing in the occlusion region compared to conventional gray stereo matching algorithms.

• Journal of Biomedical Engineering Research
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• v.29 no.6
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• pp.431-435
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• 2008

Quantitative information of a three dimensional(3D) kinematics of joint is very useful in knee joint surgery, understanding how knee kinematics related to joint injury, impairment, surgical treatment, and rehabilitation. In this paper, an automated 2D/3D image matching technique was developed to estimate the 3D in vivo knee kinematics using dual X-ray images. First, a 3D geometric model of the knee was reconstructed from CT scan data. The 3D in vivo position and orientation of femoral and tibial components of the knee joint could be estimated by minimizing the pixel by pixel difference between the projection images from the developed 3D model and the given X-ray images. The accuracy of the developed technique was validated by an experiment with a cubic phantom. The present 2D/3D image matching technique for the estimation of in vivo joint kinematics could be useful for pre-operative planning as well as post-operative evaluation of knee surgery.