• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.147-152
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• 2007

Face detection is a crucial part of the face recognition system. It determines the performance of the whole recognition system. Hausdorff distance metric has been used in face detection and recognition with good results. It defines the distance metric based only on the geometric similarity between two sets or points. However, not only the geometry but also the local patterns around the points are available in most cases. In this paper a new Hausdorff distance measure is proposed that makes hybrid use of the similarity of the geometry and the local patterns around the points. Several experiments shows that the new method outperforms the conventional method.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.147-150
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• 2000

Hausdorff distance(HD) commonly used measures for object matching, and calculates the distance between two point set of pixels in two-dimentional binary images without establishing correspondence. And it is realized as the image filter applying the fuzzy. In this paper, the fuzzy hardware realizes in order to construct the image filter applying HD, also, propose as the method for the noise removal using it in the image. MIN-MAX circuit designs the circuit using MAX-PLUS, and the fuzzy HD hardware results are obtained to the simulation. And then, the previous computer simulation is confirmed to the result by using MATLAB.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.70-80
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• 1997

This paper proposes a hierarchical Hausdorff distance (HD) matching algorithm baased on coarse-to-fine approach. It reduces the computational complexity greatly by using the pyramidal structures consisting of distance transform (DT) and edge pyramids. Also, inthe proposed hierarchical HD matching, a thresholding method is presented to find an optimal matching position with small error, in which the threshold values are determined by using the property between adjacent level of a DT map pyramid. By computer simulation, the performance of the conventional and proposed hierarchical HD matching algorithms is compared in therms of the matching position for binary images containing uniform noise.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.65-72
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• 2000

In this paper we propose the new binary image matching algorithm called the Fuzzy logic induced Hausdorff Distance(FHD) for finding the maximally matched image with the query image. The membership histogram is obtained by normalizing the cardinality of the subset with the corresponding radius after obtaining the distribution of the minimum distance computed by the Hausdroff distance between two binary images. in the proposed algorithm, The fuzzy influence method Center of Gravity(COG) is applied to calculate the best matching candidate in the membership function described above. The proposed algorithm shows the excellent results for the face image recognition when the noise is added to the query image as well as for the character recognition.

• Honam Mathematical Journal
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• v.38 no.4
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• pp.833-847
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• 2016

We are interested in the problem of finding the Hausdorff distance between two objects in ${\mathbb{R}}^2$, or in ${\mathbb{R}}^3$. In this paper, we develop an algorithm for computing the Hausdorff distance between two ellipses in ${\mathbb{R}}^3$. Our algorithm is mainly based on computing the distance between a point $u{\in}{\mathbb{R}}^3$ and a standard ellipse $E_s$, equipped with a pruning technique. This algorithm requires O(log M) operations, compared with O(M) operations for a direct method, to achieve a comparable accuracy. We give an example,and observe that the computational cost needed by our algorithm is only O(log M).

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.71-76
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• 2007

Object detection process which makes decision on the existence of meaningful objects in a given image is a crucial part of image recognition in computer vision system. Hausdorff distance metric has been used in object detection and shows good results in applications such as face recognition. It defines the dissimilarity between two sets of points and is used to find the object that is most similar to the given model. This paper proposes a Hausdorff distance based detection method that uses directional information of points to improve detection accuracy when the sets of points are derived from edge extraction as is in usual cases. In this method, elastic energy needed to make two directional points coincident is used as a measure of similarity.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.707-714
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• 2002

This paper presents a real-time Hausdorff matching algorithm for tracking of moving object acquired from an active camera. The proposed method uses the edge image of object as its model and uses Hausdorff distance as the cost function to identify hypothesis with the model. To enable real-time processing, a high speed approach to calculate Hausdorff distance and half cross matching method to improve performance of existing search methods are also presented. the experimental results demonstrate that the proposed method can accurately track moving object in real-time.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.474-483
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• 2004

In this paper, we present a new Practical implementation of a person verification system using the profile of 3-dimensional(3D) face images based on weighted Hausdorff distance(WHD) used depth information. The approach works on finding the nose tip have protrusion shape on the face using iterative selection method to use a fiducial feint and extract the profile image from vertical 3D data for the nose tip. Hausdorff distance(HD) is one of usually used measures for object matching. This works analyze the conventional HD and WHD, which the weighted factor is depth information. The Ll measure for comparing two feature vectors were used, because it is simple and robust. In the experimental results, the WHD method achieves recognition rate of 94.3% when the ranked threshold is 5.

• Journal of the Korea Computer Graphics Society
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• v.17 no.3
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• pp.33-42
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• 2011

We present a new technique for removing interior knots of parametric B-spline curves. An initial curve is constructed by continuous $L_{\infty}$ approximation proposed by Eck and Hadenfeld. We employ Hausdorff distance to measure the shape difference between the original curve and the initial one. The final curve is obtained by minimizing their Hausdorff distance. We demonstrate the effectiveness of our technique with experimental results on various types of planar and spatial curves.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.381-384
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• 2000

본 논문은 움직임이나 장면에 제약되지 않는 카메라로부터 획득한 여러장의 디지털 영상을 한장의 통힙된 영상으로 자동적으로 모자이킹 하는 방법에 대해 기술한다. 우선 영상을 세그멘테이션과 레이블링으로 영상의 영역을 분할하고, 특징 점을 찾는다. 같은 영역으로 분할된 곳의 특징점을 각각의 그룹으로 하고 영역안의 특징점을 hausdorff distance를 이용영상을 비교하여 대응점을 구한 후 비선형 이승오차 최적화 알고리즘을 이용해 최적의 변환 행렬로 하나의 영상을 자동적으로 구성하는 과정에 대해서 기술한다.