• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.149-152
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• 2012

In the roaming case, the cost of using the visited network is larger than that of home network. So, if a mobile terminal is connected to the visited network, even though it actually came back to the home network, the user may unduly pay for communication. Such a problem frequently occurs when many networks are overlapped in the same region, as shown in the case of Poland. In this paper, we propose a network search algorithm to support the fast comeback to home network in the roaming environment. In the proposed scheme, which is based on the 3GPP specification, the mobile terminal tries to search the home network by using a database of network information, as fast as possible. For performance evaluation, we construct a virtual testbed with real terminal and network equipment to emulate the service providers in Poland. From the experimental results, we can see that the proposed scheme can reduce the time of comeback to the home network by 3~60 minutes, compared the existing 3GPP scheme.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.402-410
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• 2013

In this paper, we propose a new fast block matching algorithm for block matching using the temporal and spatially correlation of the video sequence and local statistics of neighboring motion vectors. Since the temporal correlation of the video sequence between the motion vector of current block and the motion vector of previous block. The proposed algorithm determines the location of a better starting point for the search of an exact motion vector using the point of the smallest SAD(sum of absolute difference) value by the predicted motion vectors of neighboring blocks around the same block of the previous frame and the current frame and the predictor candidate point on each division region by binary-tree structure. Experimental results show that the proposed algorithm has the capability to dramatically reduce the search points and computing cost for motion estimation, comparing to fast FS(full search) motion estimation and other fast motion estimation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.2
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• pp.21-27
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• 2000

In Fast motion estimaion algorithms, they reduce the computational complexity using the assumption that the matching error increases monotonically as the search moves away from the global minimum error In this paper, we first investigate the statistical characteristics of motion vector that the motion vector mostly occures on the side of small MAE (mean absolute error) between the reference search points when the MAE difference of them is large Therefore, we propose a fast motion estimation algorithm using this property and can reduce the number of search points The computer simulation result shows that the proposed method reduces computational complexity compared with conventional fast algorithms.

• Journal of Digital Convergence
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• v.10 no.7
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• pp.123-128
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• 2012

In this paper, we propose a landmark-detection system of object for more accurate object recognition. The landmark-detection system of object becomes divided into a learning stage and a detection stage. A learning stage is created an interest-region model to set up a search region of each landmark as pre-information necessary for a detection stage and is created a detector by each landmark to detect a landmark in a search region. A detection stage sets up a search region of each landmark in an input image with an interest-region model created in the learning stage. The proposed system uses Fast Fourier Transform to detect landmark, because the landmark-detection is fast. In addition, the system fails to track objects less likely. After we developed the proposed method was applied to environment video. As a result, the system that you want to track objects moving at an irregular rate, even if it was found that stable tracking. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.7-10
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• 2000

Fast block motion estimation technique is proposed to reduce the computational complexity in video coding. In the conventional methods the size of search region is fixed. For small motion regions like background the small size of sea of search region is enough to find a block motion. But for active motion regions the large size of search region is preferred to figure out the accurate motion vector. Therefore, it is reasonable that a block motion is estimated in the variable search region (both the size and the position of it). That is to say, the search region varies according to the predicted motion characteristics of a block. The block motion in video frames has temporal continuity and then the search region of a current block is predicted using the block motion of previous blocks. The computational complexity of the proposed technique is significantly reduced with a good picture quality compared to the conventional methods.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.311-312
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• 2007

This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. Finally we conducted an experiment for the object tracking system based on a pan/tilt structure.

• 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 Digital Contents Society
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• v.8 no.4
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• pp.441-449
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• 2007

In video coding, motion estimation is a process to estimate the pixel of the current frame from the reference frame, which affects directly the predictive quality and the encoding time. This paper is related to AHHS(Adaptive Hierarchical Hexagon Search) using spatio-temporal motion activity for fast motion estimation. The proposed method defines the spatio-temporal motion activity of the current macroblock using the motion vectors of its spatio-temporally adjacent macroblocks, and then conventional AHS(Adaptive Hexagon Search) is performed if the spatio-temporal motion activity is lower, otherwise, hierarchical hexagon search is performed on a multi-layered hierarchical space constructed by multiple sub-images with low frequency in wavelet transform. 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 quality and the computational time. Experimental results indicate that the proposed method is both suitable for (quasi-) stationary and large motion searches. The proposed method could keep the merit of the adaptive hexagon search capable of fast estimating motion vectors and also adaptively reduce the local minima occurred in the video sequences with higher spatio-temporal motion activity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1527-1533
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• 2009

In this paper, we design directional search pattern using motion direction biased characteristics of MVP distribution, and proposes a direction applied cross diamond search method that adaptively change search pattern according to moving direction of search point. Proposed method predict motion vectors from neighbor macro blocks, and define initial motion direction by using predicted motion vectors. It improve search efficiency by using alternately proposed search pattern according to motion direction of BMP in search process. The simulation results show that proposed method is able to fast motion estimation compared with conventional cross diamond search, according as it reduce computational complexity that is required of motion estimation with $0.43%{\sim}1.76%$.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.2
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• pp.117-128
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• 2001

In order to achieve optimal retrieval, i.e., to find the best match to a query according to a certain similarity measure, the exhaustive search should be performed literally for all the images in a database. However, the straightforward exhaustive search algorithm is computationally expensive in large image databases. To reduce its heavy computational cost, this paper presents a fast exhaustive multi-resolution search algorithm based on image database clustering. Firstly, the proposed algorithm partitions the whole image data set into a pre-defined number of clusters having similar feature contents. Next, for a given query, it checks the lower bound of distances in each cluster, eliminating disqualified clusters. Then, it only examines the candidates in the remaining clusters. To alleviate unnecessary feature matching operations in the search procedure, the distance inequality property is employed based on a multi-resolution data structure. The proposed algorithm realizes a fast exhaustive multi-resolution search for either the best match or multiple best matches to the query. Using luminance histograms as a feature, we prove that the proposed algorithm guarantees optimal retrieval with high searching speed.