• Progress in Medical Physics
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• v.12 no.2
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• pp.177-184
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• 2001

The goal of radiation treatment is to deliver a prescribed radiation dose to the target volume accurately while minimizing dose to normal tissues. Due to inaccurate placement of field and shielding block and patient's movement, there could be displacement errors between the planed and treatment regions. In order to verify the location of radiation treatment, we in this study developed the registration algorithm of the x-ray simulator images and portal images and quantified the inaccuracy in terms of shift, scale and rotation. The algorithm for registration of pairs of radiation fields consists of the alignment of pairs of radiation images by points matching and field displacement analysis by field boundary matching. In the first step, paired surface landmarks are matched to calculate the transformation parameters (scale, rotation and shift) using the corresponding line pairs which are created by connecting two landmarks of each image. In the next step, portal field boundary is extracted and then the two field boundaries are matched by the $\rho$-$\theta$ technique. Applying the phantom portal images, detection errors were calculated to be less than 2mm in translation, 1$^{\circ}$ in rotation and 1% in scale. In conclusion, we quantitatively analyzed the displacement error of x-ray simulator images and portal images. The present results could contribute to the study of the radiation treatment verification.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1142-1149
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• 2004

Motion Estimation(ME) has been developed to reduce temporal redundancy in digital video signals and increase data compression ratio. ME is an Important part of video encoding systems, since it can significantly affect the output quality of encoded sequences. However, ME requires high computational complexity, it is difficult to apply to real time video transmission. for this reason, motion estimation algorithms with low computational complexity are viable solutions. In this paper, we present an efficient method with low computational complexity based on spatial and temporal correlations of motion vectors. The proposed method uses temporally and spatially correlated motion information, the motion vector of the block with the same coordinate in the reference frame and the motion vectors of neighboring blocks around the current block in the current frame, to decide the search pattern and the location of search starting point adaptively. Experiments show that the image quality improvement of the proposed method over MVFAST (Motion Vector Field Adaptive Search Technique) and PMVFAST (Predictive Motion Vector Field Adaptive Search Technique) is 0.01～0.3(dB) better and the speedup improvement is about 1.12～l.33 times faster which resulted from lower computational complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.4B
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• pp.771-782
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• 2000

In this paper, an H.263 video codec is implemented by adopting the concept of hardware and software co-design. Each module of the codec is investigated to find which approach between hardware and software is better to achieve real-time processing speed as well as flexibility. The hardware portion includes motion-related engines, such as motion estimation and compensation, and a memory control part. The remaining portion of theH.263 video codec is implemented in software using a RISC processor. This paper also introduces efficient design methods for hardware and software modules. In hardware, an area-efficient architecture for the motion estimator of a multi-resolution block matching algorithm using multiple candidates and spatial correlation in motion vector fields (MRMCS), is suggested to reduce the chip size. Software optimization techniques are also explored by using the statistics of transformed coefficients and the minimum sum of absolute difference (SAD)obtained from the motion estimator.

• Journal of Digital Contents Society
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• v.9 no.2
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• pp.305-315
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• 2008

Pre-proposed AHHS(Adaptive Hierarchical Hexagon Search) is a kind of the fast hierarchical block matching algorithm based on the AHS(Adaptive Hexagon Search). It is characterized as keeping the merits of the AHS capable of fast estimating motion vectors and also adaptively reducing the local minima often occurred in the video sequences with higher spatio-temporal motion activity. The objective of this paper is to propose the method effectively extending the horizontal biased pattern and the vertical biased pattern of the AHHS to improve its predictive image quality. In the paper, based on computer simulation results for multiple video sequences with different motion characteristics, the performance of the proposed method was analysed and assessed in terms of the predictive image quality and the computational time. The simulation results indicated that the proposed method was both suitable for (quasi-) stationary and large motion searches. While the proposed method increased the computational load on the process extending the hexagon search patterns, it could improve the predictive image quality so as to cancel out the increase of the computational load.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.851-859
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• 2011

In this paper, we propose the method that automatically extracts the silhouette and the joints of consecutive input image, and track joints to trace object for interaction between human and computer. Also the proposed method presents the action of human being to map human body using joints. To implement the algorithm, we model human body using 14 joints to refer to body size. The proposed method converts RGB color image acquired through a single camera to hue, saturation, value images and extracts body's silhouette using the difference between the background and input. Then we automatically extracts joints using the corner points of the extracted silhouette and the data of body's model. The motion of object is tracted by applying block-matching method to areas around joints among all image and the human's motion is mapped using positions of joints. The proposed method is applied to the test videos and the result shows that the proposed method automatically extracts joints and effectively maps human body by the detected joints. Also the human's action is aptly expressed to reflect locations of the joints

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.288-296
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• 2009

In POCS (projection onto convex sets)-based reconstruction of HR (high resolution) image, the quality of reconstructed image is gradually improved through iterative motion estimation and image restoration. The amount of computation, however, increases because of the repeated inter-frame motion estimation. In this paper, an HR reconstruction algorithm is proposed where modified ARPS (adaptive rood pattern search) and POCS are simultaneously performed. In the modified ARPS, the motion estimates obtained from phase correlation or from the previous steps in POCS restoration are utilized as the initial reference in the motion estimation. Moreover, estimated motion is regularized with reference to the neighboring blocks' motion to enhance the reliability. Computer simulation results show that, when compared to conventional methods which are composed of full search block matching and POCS restoration, the proposed method is about 30 times faster and yet produces HR images of almost equal or better quality.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.278-286
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• 2010

Motion Compensated Frame Interpolation(MCFI) has been used to reduce motion jerkiness for dynamic scenes and motion blurriness for LCD-panel display as post processing for large screen and full HD(high definition) display. Conventionally, block matching algorithms (BMA) are widely used to do motion estimation for simplicity of implementation. However, there are still several drawbacks. So in this paper, we propose a novel shape-based ME algorithm to increase accuracy and reduce ME computational cost. To increase ME accuracy, we do motion estimation based on shape of moving objects. And only moving areas are included for motion estimation to reduce computational cost. The results show that the computational cost is 25 % lower than full search BMA, while the performance is similar or is better, especially in the fast moving region.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.29-40
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• 2007

The purpose of this research is to propose a method for scoring the quality of a fingerprint image using the local information derived from the fingerprint image. In previous works for the quality measuring, most of the quality scores are related to the performance of a matching algorithm, and this makes the quality result more subjective. The quality score of a fingerprint image proposed in this work is sensor-independent, source-independent and matcher-independent one, and this concept of fingerprint sample quality results in effective improvement of the system performance. In this research, a new definition of fingerprint image quality and a new method for measuring the quality are proposed. For the experiments, several sub-databases from FVCs are used and the proposed method showed reasonable results for the test database. The proposed method can be used in various systems for the numerous purposes since the quality scores generated by the proposed method are based on the idea that the quality of fingerprint should be sensor-independent, source-independent and matcher-independent.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.1-10
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• 2007

Most of natural images have a special property, what is called self-similarity, which is the basis of fractal image coding. Even though an image has local stationarity in several homogeneous regions, it is generally non-stationarysignal, especially in edge region. This is the main reason that poor results are induced in linear techniques. In order to overcome the difficulty we propose a non-linear technique using self-similarity in the image. In our work, an image is classified into stationary and non-stationary region with respect to sample variance. In case of stationary region, do-noising is performed as simply averaging of its neighborhoods. However, if the region is non-stationary region, stationalization is conducted as make a set of center pixels by similarity matching with respect to bMSE(block Mean Square Error). And then do-nosing is performed by Gaussian weighted averaging of center pixels of similar blocks, because the set of center pixels of similar blocks can be regarded as nearly stationary. The true image value is estimated by weighted average of the elements of the set. The experimental results show that our method has better performance and smaller variance than other methods as estimator.