• Title/Summary/Keyword: 강건한 특징점

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Improved Parallel Thinning Algorithm for Fingerprint image Processing (지문영상 처리를 위한 개선된 병렬 세선화 알고리즘)

  • 권준식
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.3
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    • pp.73-81
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    • 2004
  • To extract the creditable features in fingerprint image, many people use the thinning algorithm that has a very important position in the preprocessing. In this paper, we propose the robust parallel thinning algorithm that can preserve the connectivity of the binarized fingerprint image, make the thinnest skeleton with 1-pixel width and get near to the medial axis extremely. The proposed thinning method repeats three sub-iterations. The first sub-iteration takes off only the outer boundary pixel by using the interior points. To extract the one side skeletons, the second sub-iteration finds the skeletons with 2-pixel width. The third sub-iteration prunes the needless pixels with 2-pixel width existing in the obtained skeletons and then the proposed thinning algorithm has the robustness against the rotation and noise and can make the balanced medial axis. To evaluate the performance of the proposed thinning algorithm we compare with and analyze the previous algorithms.

Improvement Method of Tracking Speed for Color Object using Kalman Filter and SURF (SURF(Speeded Up Robust Features)와 Kalman Filter를 이용한 컬러 객체 추적 속도 향상 방법)

  • Lee, Hee-Jae;Lee, Sang-Goog
    • Journal of Korea Multimedia Society
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    • v.15 no.3
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    • pp.336-344
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    • 2012
  • As an important part of the Computer Vision, the object recognition and tracking function has infinite possibilities range from motion recognition to aerospace applications. One of methods to improve accuracy of the object recognition, are uses colors which have robustness of orientation, scale and occlusion. Computational cost for extracting features can be reduced by using color. Also, for fast object recognition, predicting the location of the object recognition in a smaller area is more effective than lowering accuracy of the algorithm. In this paper, we propose a method that uses SURF descriptors which applied with color model for improving recognition accuracy and combines with Kalman filter which is Motion estimation algorithm for fast object tracking. As a result, the proposed method classified objects which have same patterns with different colors and showed fast tracking results by performing recognition in ROI which estimates future motion of an object.

Motion Recognitions Based on Local Basis Images Using Independent Component Analysis (독립성분분석을 이용한 국부기저영상 기반 동작인식)

  • Cho, Yong-Hyun
    • Journal of the Korean Institute of Intelligent Systems
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    • v.18 no.5
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    • pp.617-623
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    • 2008
  • This paper presents a human motion recognition method using both centroid shift and local basis images. The centroid shift based on 1st moment balance technique is applied to get the robust motion images against position or size changes, the extraction of local basis images based on independent component analysis(ICA) is also applied to find a set of statistically independent motion features, which is included in each motions. Especially, ICA of fixed-point(FP) algorithm based on Newton method is used for being quick to extract a local basis images of motions. The proposed method has been applied to the problem for recognizing the 160(1 person * 10 animals * 16 motions) sign language motion images of 240*215 pixels. The 3 distances such as city-block, Euclidean, negative angle are used as measures when match the probe images to the nearest gallery images. The experimental results show that the proposed method has a superior recognition performances(speed, rate) than the method using local eigen images and the method using local basis images without centroid shift respectively.

Estimating Price Elasticity of Residential Water Demand in Korea Using Panel Quatile Model (패널 분위수회귀 모형을 사용한 우리나라 지방 상수도 생활용수 수요의 가격탄력성 추정)

  • Kim, Hyung-Gun
    • Environmental and Resource Economics Review
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    • v.27 no.1
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    • pp.195-214
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    • 2018
  • This study estimates the price elasticity of residential water demand in Korea. For that, annual panel data from the year of 2010 to 2013 for 161 local water services is estimated by using panel quantile model. As a result, the price elasticities of residental water demand in Korea are estimated to be between -0.156 and -0.189 depending on its quantile. In addition, the study finds that the estimated elasticity of residential water demand by traditional conditional mean regression is relatively more influenced by high demand areas because the distribution of residental water demand in Korea is left-skewed.

Statistical Korean Spoken Language Understanding System for Dialog Processing (대화처리를 위한 통계기반 한국어 음성언어이해 시스템)

  • Roh, Yoon-Hyung;Yang, Seong-II;Kim, Young-Gil
    • Annual Conference on Human and Language Technology
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    • 2012.10a
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    • pp.215-218
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    • 2012
  • 본 논문에서는 한국어 대화 처리를 위한 통계기반 음성언어이해 시스템에 대해 기술한다. 음성언어이해시스템은 대화처리에서 음성 인식된 문장으로부터 사용자의 의도를 인식하여 의미표현으로 표현하는 기능을 담당한다. 한국어의 특성을 반영한 실용적인 음성언어이해 시스템을 위해서 강건성과 적용성, 확장성 등이 요구된다. 이를 위해 본 시스템은 음성언어의 특성상 구조분석을 하지 않고, 마이닝 기법을 이용하여 사용자 의도 표현을 생성하는 방식을 취하고 있다. 또한 한국어에서 나타나는 특징들에 대한 처리를 위해 자질 추가 및 점규화 처리 등을 수행하였다. 정보서비스용 대화처리 시스템을 대상으로 개발되고 있고, 차량 정보서비스용 학습 코퍼스를 대상으로 실험을 하여 문장단위 정확률로 약 89%의 성능을 보이고 있다.

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Shape Description and Recognition Using the Relative Distance-Curvature Feature Space (상대거리-곡률 특징 공간을 이용한 형태 기술 및 인식)

  • Kim Min-Ki
    • The KIPS Transactions:PartB
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    • v.12B no.5 s.101
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    • pp.527-534
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    • 2005
  • Rotation and scale variations make it difficult to solve the problem of shape description and recognition because these variations change the location of points composing the shape. However, some geometric Invariant points and the relations among them are not changed by these variations. Therefore, if points in image space depicted with the r-y coordinates system can be transformed into a new coordinates system that are invariant to rotation and scale, the problem of shape description and recognition becomes easier. This paper presents a shape description method via transformation from the image space into the invariant feature space having two axes: representing relative distance from a centroid and contour segment curvature(CSC). The relative distance describes how far a point departs from the centroid, and the CSC represents the degree of fluctuation in a contour segment. After transformation, mesh features were used to describe the shape mapped onto the feature space. Experimental results show that the proposed method is robust to rotation and scale variations.

Vision-based Navigation using Semantically Segmented Aerial Images (의미론적 분할된 항공 사진을 활용한 영상 기반 항법)

  • Hong, Kyungwoo;Kim, Sungjoong;Park, Junwoo;Bang, Hyochoong;Heo, Junhoe;Kim, Jin-Won;Pak, Chang-Ho;Seo, Songwon
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.48 no.10
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    • pp.783-789
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    • 2020
  • This paper proposes a new method for vision-based navigation using semantically segmented aerial images. Vision-based navigation can reinforce the vulnerability of the GPS/INS integrated navigation system. However, due to the visual and temporal difference between the aerial image and the database image, the existing image matching algorithms have difficulties being applied to aerial navigation problems. For this reason, this paper proposes a suitable matching method for the flight composed of navigational feature extraction through semantic segmentation followed by template matching. The proposed method shows excellent performance in simulation and even flight situations.

An Evaluation and Combination of Noise Reduction Filtering and Edge Detection Filtering for the Feature Element Selection in Stereo Matching (스테레오 정합 특징 요소 선택을 위한 잡음 감소 필터링과 에지 검출 필터링의 성능 평가와 결합)

  • Moon, Chang-Gi;Ye, Chul-Soo
    • Korean Journal of Remote Sensing
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    • v.23 no.4
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    • pp.273-285
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    • 2007
  • Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

2D Planar Object Tracking using Improved Chamfer Matching Likelihood (개선된 챔퍼매칭 우도기반 2차원 평면 객체 추적)

  • Oh, Chi-Min;Jeong, Mun-Ho;You, Bum-Jae;Lee, Chil-Woo
    • The KIPS Transactions:PartB
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    • v.17B no.1
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    • pp.37-46
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    • 2010
  • In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.

DB-Based Feature Matching and RANSAC-Based Multiplane Method for Obstacle Detection System in AR

  • Kim, Jong-Hyun
    • Journal of the Korea Society of Computer and Information
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    • v.27 no.7
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    • pp.49-55
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    • 2022
  • In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.