• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.465-470
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• 2002

This paper presents a new model based method for tracking moving object from a moving camera. In the proposed method, binary model is derived from detected object regions and Hausdorff distance between the model and edge image is used as its similarity measure to overcome the target's shape changes. Also, a novel search algorithm and some optimization methods are proposed to enable realtime processing. The experimental results on our test sequences demonstrate the high efficiency and accuracy of our approach.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.3
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• pp.79-87
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• 2000

In this paper, we propose a model based moving object tracking algorithm In dynamic scenes To adapt shape change of the moving object, the Hausdorff distance is applied as the measurement of similarity between model and image To reduce processing time, 2D logarithmic search method is applied for locate the position of moving object Experiments on a running vehicle and motorcycle, the result showed that the mean square error of real position and tracking result is 1150 and 1845; matching times are reduced average 1125times and 523 times than existing algorithm for vehicle image and motorcycle image, respectively It showed that the proposed algorithm could track the moving object accurately.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.17-20
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• 2000

This paper describes a shape-based object recognition algorithm using multiple distance images. For the images containing dense edge points and noise, previous Hausdorff distance (HD) measures yield a high ms error for object position and many false matchings for recognition. Extended version of HD measure considering edge position and orientation simultaneously is proposed for accurate matching. Multiple distance images are used to calculate proposed matching measure efficiently. Results are presented for visual images and infrared images.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.76-84
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• 1999

This paper presents an integrated method for aircraft position estimation using sequential aerial images. The proposed integrated system for position estimation is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values decreases reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in large position error. Therefore absolute position estimation is required to compensate for the position error generated in relative position estimation. Absolute position estimation algorithms by image matching or digital elevation model (DEM) matching are presented. In image matching, a robust oriented Hausdorff measure (ROHM) is employed whereas in DEM matching an algorithm using multiple image pairs is used. Computer simulation with four real aerial image sequences shows the effectiveness of the proposed integrated position estimation algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.45-52
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• 2004

According tothe development of digital media technologies various algorithms for video sequence matching have been proposed to match the video sequences efficiently. A large number of video sequence matching methods have focused on frame-wise query, whereas a relatively few algorithms have been presented for video sequence matching or video shot matching. In this paper, we propose an efficientalgorithm to index the video sequences and to retrieve the sequences for video sequence query. To improve the accuracy and performance of video sequence matching, we employ the Cauchy function as a similarity measure between histograms of consecutive frames, which yields a high performance compared with conventional measures. The key frames extracted from segmented video shots can be used not only for video shot clustering but also for video sequence matching or browsing, where the key frame is defined by the frame that is significantly different from the previous fames. Several key frame extraction algorithms have been proposed, in which similar methods used for shot boundary detection were employed with proper similarity measures. In this paper, we propose the efficient algorithm to extract key frames using the cumulative Cauchy function measure and. compare its performance with that of conventional algorithms. Video sequence matching can be performed by evaluating the similarity between data sets of key frames. To improve the matching efficiency with the set of extracted key frames we employ the Cauchy function and the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed method yields the high matching performance and accuracy with a low computational load compared with conventional algorithms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.535-540
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• 2006

This paper presented an effective strategy to extract the buildings and to reconstruct 3-D buildings using high-resolution multispectral stereo satellite images. Proposed scheme contained three major steps: building enhancement and segmentation using both BDT (Background Discriminant Transformation) and ISODATA algorithm, conjugate building identification using the object matching with Hausdorff distance and color indexing, and 3-D building reconstruction using photogrammetric techniques. IKONOS multispectral stereo images were used to evaluate the scheme. As a result, the BDT technique was verified as an effective tool for enhancing building areas since BDT suppressed the dominance of background to enhance the building as a non-background. In building recognition, color information itself was not enough to identify the conjugate building pairs since most buildings are composed of similar materials such as concrete. When both Hausdorff distance for edge information and color indexing for color information were combined, most segmented buildings in the stereo images were correctly identified. Finally, 3-D building models were successfully generated using the space intersection by the forward RFM (Rational Function Model).

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.223-226
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• 2003

When we detect side-view face in color image, we decide a candidate face region using skin-tone color, and confirm to the face by template matching. Cang Wei use a left and a right template of face, calculate to similarity value by hausdorff method, and decide the final side-view face. It has a characteristic that side-view face is wide spreading neck region. To get exactly result, face region is separated vertically by 3 pixel unit, and matched template. In this paper, we assume that a side-view face is a right side-view or a left side-view face. We separate a half of the candidate face region vertically, and regard a left side as left candidate face, a right side as right candidate face by template matching. This method detect faster than Gang Wei method.

• Journal of the Korea society of information convergence
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• v.7 no.2
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• pp.59-65
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• 2014

Grassfire spot matching method is based on similarity comparison of topological patterns for neighbor spots. This is a method where spot matching is performed as if fire spreads all around on grass. Spot matching starts from a seed spot pair confirmed as a matched pair of spots and spot matching spreads to the direction where the best matching result is produced. In this paper, it is a bit complicated way of grassfire method where multi-seed matched spot pair are manually selected and spot matching is performed from each multi-seed matched spot pair. The proposed method shows better performance in detection rate and accuracy than that of the previous method.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.50-53
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• 2000

It is effectively removed noise in the image using FCNN(Fuzzy Cellular Neural Network) applying fuzzy theory to CNN(Cellular Neural Network) structure and HD(Hausdorff Distance) commonly used measures for object matching. HD calculates the distance between two point set of pixels in two-dimensional binary images without establishing correspondence. Also, this method is proposed in order to improve the operation speed. In this paper, $\alpha$-LTSHD(Least Trimmed Square HD) operator applying $\alpha$-Trimmed to LTSHD, one field of HD, is applied to FCNN structure, and it is proposed as the modified method in order to remove noise in the image. Also, it is made a comparison with the other filters by using MSE and SNR after removing noise using the FCNNS which are applied $\alpha$-LTSHD operator through the computer simulation. In a result, FCNN performance which is applied the proposed $\alpha$-LTSHD demonstrated the superiority to the other filters in the noise removal.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.700-705
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• 2008

In this paper, we propose object classification algorithm for real-time surveillance system. We have approached this problem using silhouette-based template matching. The silhouette of the object is extracted, and then it is compared with representative template models. Template models are previously stored in the database. Our algorithm is similar to previous pixel-based template matching scheme like Hausdorff Distance, but we use 1D image array rather than 2D regions inspired by Hilbert Path. Transformation of images could reduce computational burden to compute similarity between the detected image and the template images. Experimental results show robustness and real-time performance in object classification, even in low resolution images.