• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.2
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• pp.53-61
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• 2011

This research presents an adaptive fast motion estimation (ME) computation on the stage of uneven multi-hexagon grid search (UMHGS) algorithm included in an unsymmetrical-cross multi-hexagon-grid search (UMHexagonS) in H.264 standard. The proposed adaptive method is based on statistical analysis and previously obtained motion vectors to reduce the computational complexity of ME. For this purpose, the algorithm is decomposed into three processes: skipping, terminating, and reducing search areas. Skipping and terminating are determined by the statistical analysis of the collected minimum SAD (sum of absolute difference) and the search area is constrained by the slope of previously obtained motion vectors. Simulation results show that 13%-23% of ME time can be reduced compared with UMHexagonS, while still maintaining a reasonable PSNR (peak signal-to-noise ratio) and average bitrates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.242-259
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• 2015

Three-dimensional video coding is one of the main challenges restricting the widespread applications of 3D video and free viewpoint video. In this paper, a novel fractal coding algorithm with motion-vector-field-based motion estimation for depth map sequence is proposed. We firstly add pre-search restriction to rule the improper domain blocks out of the matching search process so that the number of blocks involved in the search process can be restricted to a smaller size. Some improvements for motion estimation including initial search point prediction, threshold transition condition and early termination condition are made based on the feature of fractal coding. The motion-vector-field-based adaptive hexagon search algorithm on the basis of center-biased distribution characteristics of depth motion vector is proposed to accelerate the search. Experimental results show that the proposed algorithm can reach optimum levels of quality and save the coding time. The PSNR of synthesized view is increased by 0.56 dB with 36.97% bit rate decrease on average compared with H.264 Full Search. And the depth encoding time is saved by up to 66.47%. Moreover, the proposed fractal depth map sequence codec outperforms the recent alternative codecs by improving the H.264/AVC, especially in much bitrate saving and encoding time reduction.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.570-576
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• 2007

This paper presents a hexagon-based Q-leaning algorithm to find a hidden targer object with multiple robots. An experimental environment was designed with five small mobile robots, obstacles, and a target object. Robots went in search of a target object while navigating in a hallway where obstacles were strategically placed. This experiment employed two control algorithms: an area-based action making (ABAM) process to determine the next action of the robots and hexagon-based Q-learning to enhance the area-based action making process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.3
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• pp.264-269
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• 2006

This paper presents the strategy for searching a hidden object in an unknown area for using by multiple distributed autonomous robotic systems (DARS). To search the target in Markovian space, DARS should recognize th ε ir surrounding at where they are located and generate some rules to act upon by themselves. First of all, DARS obtain 6-distances from itself to environment by infrared sensor which are hexagonally allocated around itself. Second, it calculates 6-areas with those distances then take an action, i.e., turn and move toward where the widest space will be guaranteed. After the action is taken, the value of Q will be updated by relative formula at the state. We set up an experimental environment with five small mobile robots, obstacles, and a target object, and tried to research for a target object while navigating in a un known hallway where some obstacles were placed. In the end of this paper, we present the results of three algorithms - a random search, an area-based action making process to determine the next action of the robot and hexagon-based Q-learning to enhance the area-based action making process.

• ETRI Journal
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• v.34 no.3
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• pp.369-378
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• 2012

In this paper, we propose new adaptive search range motion estimation methods where the search ranges are constrained by the probabilities of motion vector differences and a search point sampling technique is applied to the constrained search ranges. Our new methods are based on our previous work, in which the search ranges were analytically determined by the probabilities. Since the proposed adaptive search range motion estimation methods effectively restrict the search ranges instead of search point sampling patterns, they provide a very flexible and hardware-friendly approach in motion estimation. The proposed methods were evaluated and tested with JM16.2 of the H.264/AVC video coding standard. Experiment results exhibit that with negligible degradation in PSNR, the proposed methods considerably reduce the computational complexity in comparison with the conventional methods. In particular, the combined method provides performance similar to that of the hybrid unsymmetrical-cross multi-hexagon-grid search method and outstanding merits in hardware implementation.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.119-125
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• 2008

This paper presents the polygon-based Q-leaning and Cascade Support Vector Machine algorithm for object search with multiple robots. We organized an experimental environment with ten mobile robots, twenty five obstacles, and an object, and then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and Cascade SVM to enhance the fusion model with DBAM and ABAM process.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1044-1047
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• 2005

In this paper, we present the strategy of object search for distributed autonomous robotic systems (DARS). The DARS are the systems that consist of multiple autonomous robotic agents to whom required functions are distributed. For instance, the agents should recognize their surrounding at where they are located and generate some rules to act upon by themselves. In this paper, we introduce the strategy for multiple DARS robots to search a hidden object at the unknown area. First, we present an area-based action making process to determine the direction change of the robots during their maneuvers. Second, we also present Q learning adaptation to enhance the area-based action making process. Third, we introduce the coordinate system to represent a robot's current location. In the end of this paper, we show experimental results using hexagon-based Q learning to find the hidden object.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.291-296
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• 2008

This paper presents the dodecagon-based Q-leaning and SVM algorithm for object search with multiple robots. We organized an experimental environment with several mobile robots, obstacles, and an object. Then we sent the robots to a hallway, where some obstacles were tying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process to determine the next action of the robots, and hexagon-based Q-learning and dodecagon-based Q-learning and SVM to enhance the fusion model with Distance-based action making(DBAM) and Area-based action making(ABAM) process.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.49-60
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• 2010

H.264/AVC offers a better encoding efficiency than conventional video standards by adopting many new encoding techniques. However, the advanced coding techniques also add to the overall complexity for H.264/AVC encoder. Accordingly, it is necessary to perform optimization to alleviate the level of complexity for the video encoder. The amount of computation for motion estimation is of particular importance. In this paper, we propose a diamond web-grid search algorithm combined with efficient stationary block skip method which employs full diamond and dodecagon search patterns, and the variable thresholds are used for performing an effective skip of stationary blocks. The experimental results indicate that the proposed technique reduces the computations of the unsymmetrical-cross multi-hexagon-grid search algorithm by up to 12% while maintaining a similar PSNR performance.