• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.632-635
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• 2003

This paper describes a system fur tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.87-92
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• 2003

This paper describes a system for tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.330-332
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• 2012

본 논문에서는 임의의 정렬되지 않은 평면 점집합(Plane Point Set)에서 정렬을 고려한 개선된 Convex Hull 알고리즘을 제안한다. 이 알고리즘은 Convex Hull의 극점(Extreme Point) 특성을 이용하여 처리 데이터를 한정하기 때문에 계산복잡도를 낮춘다. 각 단계마다 볼록 정점(Convex Vertex)만을 판별하는 조건을 이용하여 한 번의 스캔으로 온전한 Convex Set이 구한다. 알고리즘 초기에 점집합의 정렬이 필요한데, 이때 걸리는 시간이 알고리즘 전체 동작시간의 대부분을 차지하는 만큼, 특성에 맞는 방법을 사용하여 빠르게 정렬하였다. 일반적인 상황을 가정하고 점집합을 랜덤하게 구성하여 실험하였으며 기존의 알고리즘에 비해 약 두 배의 속도 향상이 있음을 확인하였다.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.157-165
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• 2010

This paper presents a novel real-time rendering algorithm based on spherical coordinate system of the object using convex hull. While OpenGL rendering pipeline touches all vertices of an object, the proposed method takes account the only visible vertices by examining the visible triangles of the object. In order to determine the visible areas of the object in its spherical coordinate representation, the proposed method uses 3D geometric relation of 6 plane equations of the camera frustum and the bounding sphere of the object. In addition, we compute the convex hull of the object and its maximum side factors for hidden surface removal. Simulation results showed that the quality of result image is almost same compared to original image and rendering performance is greatly improved.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.623-625
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• 2021

Rubber o-rings are produced by conventional injection molding methods. In this case, products that are not normally molded are determined to be defective. However, if images acquired during image-based reading are read as original, there is a problem of poor accuracy. We have thus learned from convolutional neural networks using adaptive binarization and Convex Hull algorithms by extracting only rubber oring parts from the original images through pre-processing. During the test process, it was confirmed that the defect detection performance of the learning method applied pre-processing was better than the standard suggested.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.7
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• pp.103-110
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• 2008

This paper presents a new approach of recognizing a 3D object using a single camera, based on the extended convex hull of its silhouette. It aims at minimizing the DB size and simplifying the processes for matching and feature extraction. For this purpose, two concepts are introduced: extended convex hull and measurable region. Extended convex hull consists of convex curved edges as well as convex polygons. Measurable region is the cluster of the viewing vectors of a camera represented as the points on the orientation sphere from which a specific set of surfaces can be measured. A measurable region is represented by the extended convex hull of the silhouette which can be obtained by viewing the object from the center of the measurable region. Each silhouette is represented by a relation graph where a node describes an edge using its type, length, reality, and components. Experimental results are included to show that the proposed algorithm works efficiently even when the objects are overlapped and partially occluded. The time complexity for searching the object model in the database is O(N) where N is the number of silhouette models.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.3
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• pp.305-312
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• 2012

This paper aims at improving the accuracy and computational efficiency in reconstructing building boundaries from airborne Lidar points. We proposed an adaptive convex hull algorithm, which is a modified version of local convex hull algorithm in three ways. The candidate points for boundary are first selected to improve efficiency depending on their local density. Second, a searching-space is adjusted adaptively, based on raw data structure, to extract boundary points more robustly. Third, distance between two points and their IDs are utilized in detecting the seed points of inner boundary to distinguish between inner yards and inner holes due to errors or occlusions. The practicability of the approach were evaluated on two urban areas where various buildings exist. The proposed method showed less shape-dissimilarity(8.5%) and proved to be two times more efficient than the other method.

• Journal of Institute of Convergence Technology
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• v.10 no.1
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• pp.7-12
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• 2020

We describe a efficient surface reconstruction method that reconstructs a 3D manifold polygonal mesh approximately passing through a set of 3D oriented points. Our algorithm includes 3D convex hull, octree data structure, signed distance function (SDF), and marching cubes. The 3D convex hull provides us with a fast computation of SDF, octree structure allows us to compute a minimal distance for SDF, and marching cubes lead to iso-surface generation with SDF. Our approach gives us flexibility in the choice of the resolution of the reconstructed surface, and it also enables to use on low-level PCs with minimal peak memory usage. Experimenting with publicly available scan data shows that we can reconstruct a polygonal mesh from point cloud of sizes varying from 10,000 ~ 1,000,000 in about 1~60 seconds.

• Honam Mathematical Journal
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• v.36 no.4
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• pp.787-794
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• 2014

We prove three convex hull theorems on triangles and circles. Given a triangle ${\triangle}$ and a point p, let ${\triangle}^{\prime}$ be the triangle each of whose vertices is the intersection of the orthogonal line from p to an extended edge of ${\triangle}$. Let ${\triangle}^{{\prime}{\prime}}$ be the triangle whose vertices are the centers of three circles, each passing through p and two other vertices of ${\triangle}$. The first theorem characterizes when $p{\in}{\triangle}$ via a distance duality. The triangle algorithm in [1] utilizes a general version of this theorem to solve the convex hull membership problem in any dimension. The second theorem proves $p{\in}{\triangle}$ if and only if $p{\in}{\triangle}^{\prime}$. These are used to prove the third: Suppose p be does not lie on any extended edge of ${\triangle}$. Then $p{\in}{\triangle}$ if and only if $p{\in}{\triangle}^{{\prime{\prime}}$.

• Journal of the Korea Society of Computer and Information
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• v.16 no.3
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• pp.99-107
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• 2011

The feature extraction of asterias amurensis by using patterns is difficult to extract all the concave and convex features of asterias amurensis nor classify concave and convex. Concave and convex as important structural features of asterias amurensis are the features which should be found and the classification of concave and convex is also necessary for the recognition of asterias amurensis later. Accordingly, this study suggests the technique to extract the features of concave and convex, the main features of asterias amurensis. This technique classifies the concave and convex features by using the multi-directional linear scanning and form the candidate groups of the concave and convex feature points and decide the feature points of the candidate groups and apply convex hull algorithm to the extracted feature points. The suggested technique efficiently extracts the concave and convex features, the main features of asterias amurensis by dividing them. Accordingly, it is expected to contribute to the studies on the recognition of asterias amurensis in the future.