• Title/Summary/Keyword: Feature-based image matching

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High Speed Construction Method of Panoramic Images Using Scene Shot Guider (촬영 장면 가이더를 이용한 고속 파노라마 영상 생성 방법)

  • Kim, Tae-Woo;Yoo, Hyeon-Joong;Sohn, Kyu-Seek
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.8 no.6
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    • pp.1449-1457
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    • 2007
  • A panorama image is constructed by merging several overlapped images to a big one. There are two kinds of methods, feature based and direct method, in the construction. Feature based one has a merit of processing speed faster than direct one. But, it is difficult to be implemented under slower processing environments such as mobile device. This paper proposed high speed construction method of a panorama image. The algorithm extremely improved matching speed by reducing the number of matching parameters using scene shot guider, and additionally adapted local matching technique to reduce matching error caused by the fewer matching parameters. In the experiments, it was shown that the proposed method required about 0.078 second in processing time, about 17 times shorter than the feature based one, for 24-bit color images of $320{\times}240$ size.

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SHIP BLOCK ARRANGEMENT SYSTEM BASED ON IMAGE PROCESSING

  • Park, Jeong-Ho;Choi, Wan-Sik
    • Proceedings of the KSRS Conference
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    • 2008.10a
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    • pp.104-106
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    • 2008
  • This paper proposes an image based method for arranging ship blocks in a dockyard. The problem of appropriately arranging numerous blocks has to be carefully planned because it has close relation to the effectiveness of the whole working process. To implement the system, the block shape and feature points have to be obtained from block image. The block arrangement system can be implemented by the fusion of the block shape extraction and image matching technology.

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A Stereo Matching Algorithm with Image Fuzzification (이미지 퍼지화를 이용한 스테레오 정합 알고리즘)

  • Chung, Young-June;Jun, Hyo-Byung;Sim, Kwee-Bo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.8 no.6
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    • pp.85-90
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    • 1998
  • The most important step image processing is stereo matching process. That is finding pixels of 3 dimensional pair in the left and right image. There are two matching methods. One is an area based approach and the other is a feature based approach. An area based approach needs much calculation time. In the other hand, we have the advantage of calculation time in the feature based approach, but can not obtain matched data for all pixels in the image. In recent years, fuzzy image processing methods are developed to manage vagueness and noise in image and ambiguous, inconsistent knowledge in recognition step. In this paper, we propose a fuzzy stereo matching algorithm. This method converts brightness data of image to fuzzy membership value and processes an area based approach method for stereo matching algorithm. We experiment with some stereo images to validate effectiveness of this algorithm.

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Laser Image SLAM based on Image Matching for Navigation of a Mobile Robot (이동 로봇 주행을 위한 이미지 매칭에 기반한 레이저 영상 SLAM)

  • Choi, Yun Won;Kim, Kyung Dong;Choi, Jung Won;Lee, Suk Gyu
    • Journal of the Korean Society for Precision Engineering
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    • v.30 no.2
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    • pp.177-184
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    • 2013
  • This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

Octree model based fast three-dimensional object recognition (Octree 모델에 근거한 고속 3차원 물체 인식)

  • 이영재;박영태
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.34C no.9
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    • pp.84-101
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    • 1997
  • Inferring and recognizing 3D objects form a 2D occuluded image has been an important research area of computer vision. The octree model, a hierarchical volume description of 3D objects, may be utilized to generate projected images from arbitrary viewing directions, thereby providing an efficient means of the data base for 3D object recognition. We present a fast algorithm of finding the 4 pairs of feature points to estimate the viewing direction. The method is based on matching the object contour to the reference occuluded shapes of 49 viewing directions. The initially best matched viewing direction is calibrated by searching for the 4 pairs of feature points between the input image and the image projected along the estimated viewing direction. Then the input shape is recognized by matching to the projectd shape. The computational complexity of the proposed method is shown to be O(n$^{2}$) in the worst case, and that of the simple combinatorial method is O(m$^{4}$.n$^{4}$) where m and n denote the number of feature points of the 3D model object and the 2D object respectively.

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A Survey and Comparison of 3D Registration of Brain Images Between Marker Based and Feature Based Method (마커 기반과 특징기반에 기초한 뇌 영상의 3차원 정합방법의 비교 . 고찰)

  • 조동욱;김태우;신승수;김지영;김동원;조태경
    • The Journal of the Korea Contents Association
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    • v.3 no.3
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    • pp.85-97
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    • 2003
  • Medical tomography images like CT, MRI, PET, SPECT, fMRI, ett have been widely used for diagnosis and treatment of a patient and for clinical study in hospital. In many cases, tomography images are scanned in several different modalities or with time intervals for a single subject for extracting complementary information and comparing one another. 3D image registration is mapping two sets of images for comparison onto common 3D coordinate space, and may be categorized to marker -based matching and feature-based matching. 3D registration of brain images has an important role for visual and quantitative analysis in localization of treatment area of a brain, brain functional research, brain mapping research, and so on. In this article, marker-based and feature-based matching methods which are often used are introduced.

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Integrated SIFT Algorithm with Feature Point Matching Filter for Relative Position Estimation (특징점 정합 필터 결합 SIFT를 이용한 상대 위치 추정)

  • Gwak, Min-Gyu;Sung, Sang-Kyung;Yun, Suk-Chang;Won, Dae-Hee;Lee, Young-Jae
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.37 no.8
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    • pp.759-766
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    • 2009
  • The purpose of this paper is an image processing algorithm development as a base research achieving performance enhancement of integrated navigation system. We used the SIFT (Scale Invariant Feature Transform) algorithm for image processing, and developed feature point matching filter for rejecting mismatched points. By applying the proposed algorithm, it is obtained better result than other methods of parameter tuning and KLT based feature point tracking. For further study, integration with INS and algorithm optimization for the real-time implementation are under investigation.

Post Processing to Reduce Wrong Matches in Stereo Matching

  • Park, Hee-Ju;Lee, Suk-Bae
    • Korean Journal of Geomatics
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    • v.1 no.1
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    • pp.43-49
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    • 2001
  • Although many kinds of stereo matching method have been developed in the field of computer vision and photogrammetry, wrong matches are not easy to avoid. This paper presents a new method to reduce wrong matches after matching, and experimental results are reported. The main idea is to analyze the histogram of the image attribute differences between each pair of image patches matched. Typical image attributes of image patch are the mean and the standard deviation of gray value for each image patch, but there could be other kinds of image attributes. Another idea is to check relative position among potential matches. This paper proposes to use Gaussian blunder filter to detect the suspicious pair of candidate match in relative position among neighboring candidate matches. If the suspicious candidate matches in image attribute difference or relative position are suppressed, then many wrong matches are removed, but minimizing the suppression of good matches. The proposed method is easy to implement, and also has potential to be applied as post processing after image matching for many kinds of matching methods such as area based matching, feature matching, relaxation matching, dynamic programming, and multi-channel image matching. Results show that the proposed method produces fewer wrong matches than before.

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Keyword Spotting on Hangul Document Images Using Image-to-Image Matching (영상 대 영상 매칭을 이용한 한글 문서 영상에서의 단어 검색)

  • Park Sang Cheol;Son Hwa Jeong;Kim Soo Hyung
    • The KIPS Transactions:PartB
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    • v.12B no.3 s.99
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    • pp.357-364
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    • 2005
  • In this paper, we propose an accurate and fast keyword spotting system for searching user-specified keyword in Hangul document images by using two-level image-to-image matching. The system is composed of character segmentation, creating a query image, feature extraction, and matching procedure. Two different feature vectors are used in the matching procedure. An experiment using 1600 Hangul word images from 8 document images, downloaded from the website of Korea Information Science Society, demonstrates that the proposed system is superior to conventional image-based document retrieval systems.

Another Approach to Stereo Matching - Fuzzification of Feature Values (또다른 접근방식에 의한 스테레오 정합 - 특정 값의 퍼지화)

  • 김동현;최우영;박래홍
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.28B no.11
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    • pp.925-933
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    • 1991
  • Conventional stereo matching techniques are based on the assumption that the features representing an object in left and right images have fixed attribute values. But, in fact, such features may take different values due to the practical stereo image formation and the image acquisition error, and thus the conventional techniques tend to result in the in the incorrect matching of features. In this paper, we propose a stereo matching mathod with a possibilistic view which copes with the possible variability of feature values. As a result, this method decreases the number of incorrect matching features when the values of corresponding features are somewhat large. The effectiveness of the proposed method is shown via computer simulation.

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