• Journal of KIISE:Computer Systems and Theory
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• v.31 no.11
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• pp.611-616
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• 2004

In this paper, we introduce a very fast and efficient fractal coding scheme by using the spatial prediction on ultra-small atomic range blocks. This new approach drastically speeds up the encoding while improving the fidelity and the compression ratio. The affine transformation coefficients between adjacent range blocks induced by this method often have good correlations thereby the compression ratios can further be improved. The proposed method leads to improved rate-distortion performance compared to previously reported pure fractals, and it is faster than other state-of-the-art fractal coding methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.331-341
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• 2019

With the popularity of sensor-rich environments, smartphones have become one of the major platforms for obtaining and sharing information. Since it is difficult to utilize GNSS (Global Navigation Satellite System) inside the area with many buildings, the localization of smartphone in this case is considered as a challenging task. To resolve problem of localization using smartphone a four step image-based localization method and procedure is proposed. To improve the localization accuracy of smartphone datasets, MMS (Mobile Mapping System) and Google Street View were utilized. In our approach first, the searching for candidate matching image is performed by the query image of smartphone's using GNSS observation. Second, the SURF (Speed-Up Robust Features) image matching between the smartphone image and reference dataset is done and the wrong matching points are eliminated. Third, the geometric transformation is performed using the matching points with 2D affine transformation. Finally, the smartphone location and attitude estimation are done by PnP (Perspective-n-Point) algorithm. The location of smartphone GNSS observation is improved from the original 10.204m to a mean error of 3.575m. The attitude estimation is lower than 25 degrees from the 92.4% of the adjsuted images with an average of 5.1973 degrees.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.302-305
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• 2008

For an complete image composition to be stitched on several mosaic images, tracing displacement of direction and distance between successive images are important parameters. The input image is modeled by using a general second order two-dimensional Taylor-series and then converting it to a $3{\times}3$ correlation block and storing the data. A moving factor and coordinate is calculated by comparing the continuous correlation blocks. The experimentation result has a success rate of 85% for moving path tracing as continuous images are moved to 10% of image central position.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.141-144
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• 2002

In this paper, we propose a new object tracking algorithm using deformed template and Level-Set theory, which is robust against background variation, object flexibility and occlusion. The proposed tracking algorithm consists of two steps. The first step is an estimation of object shape and location, on the assumption that the transformation of object can be approximately modeled by the affine transform. The second step is a refinement of the object shape to fit into the real object accurately, by using the potential energy map and the modified Level Set speed function. Experimental results show that the proposed algorithm can track non-rigid objects with large variation in the backgrounds.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.195-198
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• 2000

Fractal image compression can reduce the size of image data by contractive mapping of original image. The mapping is affine transformation to find the block(called range block) which is the most similar to the original image. Fractal is very efficient way to reduce the data size. However, it has high distortion rate and requires long encoding time. In this paper, we present the simulation result of fractal and VQ hybrid systems which use different clustering algorithms, normal and improved competitive learning SOFM. The simulation results showed that the VQ hybrid fractal using improved competitive learning SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.81-84
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• 2000

Fractal image compression can reduce the size of image data by contractive mapping of original image. The mapping is affine transformation to find the block(called range block) which is the most similar to the original image. Fractal is very efficient way to reduce the data size. However, it has high distortion rate and requires long encoding time. In this paper, we present the simulation result of fractal and VQ hybrid systems which use different clustering algorithms, normal and improved competitive learning SOFM. The simulation results showed that the VQ hybrid fractal using improved competitive learning SOFM has better distortion rate than the VQ hybrid fractal using normal SOFM.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.271-275
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• 2003

In this paper, we propose a method that corrects the geometric distortion of mouth in an image. the method is composed of two steps - detecting key points and correcting geometric distortion. First, key points of lips in source and destination images are found by using lips detection algorithm. Then, the two images are mapped by using affine transformation and information found in first step. In experiment result for various mouths with different geometric distortion, we found that the proposed method have satisfactory efficiency.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.945-946
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• 2008

Feature matching is often used for image alignment. It, however, isconsidered as motion estimation problem in case of video. In that case we need only a motion vector in an image. Then we can compute the distance between two images although the images are far away each other. So we propose affine transformation from camera motion for spatial positioning of frames and aligning those frames. The data from this method can be useful for calculating the distance, stabilizing video, photographing panorama and so on.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1027-1031
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• 1990

In this paper, we propose a model-based method of estimating the velocity of a moving object from a series of images. The proposed method utilizes Kalman filtering technique. Assuming that the motion is described by an affine transformation, we construct a discrete-time state variable model of the motion based on the dynamic motion imagery modeling technique proposed by Schalkoff. Using this state variable model, we derive a Kalman filter algorithm. Some simulation results are presented to show that the proposed Kalman filter algorithm is superior to a simple least square method without a model.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.621-624
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• 1999

This paper describes a distance estimation method of object's motion in soccer image sequence by tracking field features. And we quantitatively evaluate the estimation accuracy We suppose that the input image sequence is taken with a camera on static axis and includes only zooming and panning transformation between frames. Adaptive template matching is adopted for non-rigid object tracking. For background compensation, feature templates selected from reference frame image are matched in following frames and the matched feature point pairs are used in computing Affine motion parameters. A perspective displacement field model is used for estimating the real distance between two position on Input Image. To quantitatively evaluate the accuracy of the estimation, we synthesized a 3 dimensional virtual stadium with graphic tools and experimented on the synthesized 2 dimensional image sequences. The experiment shows that the average of the error between the actual moving distance and the estimated distance is 1.84%.