• Journal of the Korea Computer Industry Society
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• v.2 no.11
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• pp.1465-1476
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• 2001

For the applications like video indexing, text understanding, and automatic captions localization system, real-time localization of captions is an essential task. This paper presents a algorithm for localization of captions in MPEG compression images based on I frame. In this algorithm, caption text regions are segmented from background images using their distinguishing texture characteristics and chrominance information. Unlike previously published algorithms which fully decompress the video sequence before extracting the text regions, this algorithm locates candidate caption text region directly in the DCT compressed domain.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.920-923
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of the simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two parallel walls are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.247-248
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• 2006

In this paper, we propose a new sound localization algorithm for an active perception system. In an active perception system, an acquired sound is mixed with the sound of motors. So a sound localization algorithm for an active perception system requires a robustness for the noise and a computational efficiency. The proposed localization algorithm can achieve robustness and efficiency to use only sub-band channels that are contained harmonic structure of the target speech.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7B
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• pp.595-605
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• 2012

DV-Hop is one of the well known range-free localization algorithms. The algorithm works well in case of isotropic network since the sensor and anchor nodes are placed in the entire area. However, it results in large errors in case of anisotropic networks where the hop count between nodes is not linearly proportional to the Euclidean distance between them. Hence, we proposed a novel range-free algorithm for anisotropic networks to improve the localization accuracy. In the paper, the Euclidean distance between anchor node and unknown node is estimated by the average hop distance calculated at each hop count with hop count and distance information between anchor nodes. By estimating the unknown location of nodes with the estimated distance estimated by the average hop distance calculated at each hop, the localization accuracy is improved. Simulation results show that the proposed algorithm has more accuracy than DV-Hop.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1182-1188
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• 2010

This paper proposes a precise localization algorithm for a quickly moving mobile robot. In order to localize a mobile robot with active beacon sensors, a relatively long time is needed, since the distance to the beacon is measured using the flight time of the ultrasonic signal. The measurement time does not cause a high error rate when the mobile robot moves slowly. However, with an increase of the mobile robot's speed, the localization error becomes too high to use for accurate mobile robot navigation. Therefore, in this research into high speed mobile robot operations, instead of using two active beacons for localization an active beacon and dual compass are utilized to localize the mobile robot. This new approach resolves the high localization error caused by the speed of the mobile robot. The performance of the precise localization algorithm was verified by comparing it to the conventional method through real-world experiments.

• Progress in Medical Physics
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• v.11 no.1
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• pp.39-47
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• 2000

In Stereotactic Radiosurgery (SRS), there are three imaging methods of target localization, such as digital subtraction Angiography (DSA), computed tomography (CT), magnetic resonance imaging (MRI). Especially, DSA and MR images have a distortion effect generated by each modality. In this research, image properties of DSA were studied. A first essential condition in SRS is an accurate information of target locations, since high dose used to treat a patient may give a complication on critical organ and normal tissue. Hut previous localization program did not consider distortion effect which was caused by image intensifier (II) of DSA. A neurosurgeon could not have an accurate information of target locations to operate a patient. In this research, through distortion correction, we tried to calculate accurate target locations. We made a grid phantom to correct distortion, and a target phantom to evaluate localization algorithm. The grid phantom was set on the front of II, and DSA images were obtained. Distortion correction methods consist of two parts: 1. Bilinear transform for geometrical correction and bilinear interpolation for gray level correction. 2. Automatic detection method for calculating locations of grid crosses, fiducial markers, and target balls. Distortion was corrected by applying bilinear transform and bilinear interpolation to anterior-posterior and left-right image, and locations of target and fiducial markers were calculated by the program developed in this study. Localization errors were estimated by comparing target locations calculated in DSA images with absolute locations of target phantom. In the result, the error in average with and without distortion correction is $\pm$0.34 mm and $\pm$0.41 mm respectively. In conclusion, it could be verified that our localization algorithm has an improved accuracy and acceptability to patient treatment.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.208-215
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• 2013

Early iterations of the existing Global Positioning System (GPS)-based or radio lateration technique-based vehicle localization algorithms suffer from flip ambiguities, forged relative location information and location information exchange overhead, which affect the subsequent iterations. This, in turn, results in an erroneous neighbor-vehicle map. This paper proposes an extended information overlap measure (EIOM) algorithm to reduce the flip error rates by exchanging the neighbor-vehicle presence features in binary information. This algorithm shifts and associates three pieces of information in the Moore neighborhood format: 1) feature information of the neighboring vehicles from a vision-based environment sensor system; 2) cardinal locations of the neighboring vehicles in its Moore neighborhood; and 3) identification information (MAC/IP addresses). Simulations were conducted for multi-lane highway scenarios to compare the proposed algorithm with the existing algorithm. The results showed that the flip error rates were reduced by up to 50%.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.115-117
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• 2009

This paper presents localization performance enhancement on GPS interfering spot for mobile robot. Localization system applied Extended Kalman filter algorithm that utilized Diffrential GPS and odometry, inertial sensors. In this paper, different noise covariance is applied to Extended Kalman Filter according to the GPS quality. Experiment results show that proposed localization system improve considerably localization performance of mobile robots.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.483-493
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• 2008

For effective wireless localization utilizing signal strength measurements based on IEEE 802.11 WLAN standard diversity of mobile hardware, characteristics of is one of the important problems to be considered for advanced location-based services. For improved accuracy regardless of a bias originating from the mobile hardware characteristics, this paper proposes a new localization algorithm, which is named as the hybrid Kriging algorithm. To evaluate the performance characteristics of the proposed algorithm, simulation and experiment results are illustrated. By the simulation and experiment result, the proposed algorithm is more accurate than the well-known location finger-print method given the same density of reference measurements.