• Title/Summary/Keyword: RANSAC algorithm

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A New Shape-Based Object Category Recognition Technique using Affine Category Shape Model (Affine Category Shape Model을 이용한 형태 기반 범주 물체 인식 기법)

  • Kim, Dong-Hwan;Choi, Yu-Kyung;Park, Sung-Kee
    • The Journal of Korea Robotics Society
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    • v.4 no.3
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    • pp.185-191
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    • 2009
  • This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

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An Implementation of the Real-time Image Stitching Algorithm Based on ROI (ROI 기반 실시간 이미지 정합 알고리즘 구현)

  • Kwak, Jae Chang
    • Journal of IKEEE
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    • v.19 no.4
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    • pp.460-464
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    • 2015
  • This paper proposes a panoramic image stitching that operates in real time at the embedded environment by applying ROI and PROSAC algorithm. The conventional panoramic image stitching applies SURF or SIFT algorithm which contains complicated operations and a lots of data, at the overall image to detect feature points. Also it applies RANSAC algorithm to remove outliers, so that an additional verification time is required due to its randomness. In this paper, unnecessary data are eliminated by setting ROI based on the characteristics of panorama images, and PROSAC algorithm is applied for removing outliers to reduce verification time. The proposed method was implemented on the ORDROID-XU board with ARM Cortex-A15. The result shows an improvement of about 54% in the processing time compared to the conventional method.

Filtering of Lidar Data using Labeling and RANSAC Algorithm (Labeling과 RANSAC알고리즘을 이용한 Lidar 데이터의 필터링)

  • Lee, Jeong-Ho;Kim, Yong-Il
    • Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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    • 2010.04a
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    • pp.267-270
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    • 2010
  • In filtering of urban lidar data, low outliers or opening underground areas may cause errors that some ground points are labelled as non-ground objects. To solve such a problem, this paper proposes an automated method which consists of RANSAC algorithm, one-dimensional labeling, and morphology filter. All processes are conducted along the lidar scan line profile for efficient computation. Lidar data over Dajeon, Korea is used and the final results are evaluated visually. It is shown that the proposed method is quite promising in urban dem generation.

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1-Point Ransac Based Robust Visual Odometry

  • Nguyen, Van Cuong;Heo, Moon Beom;Jee, Gyu-In
    • Journal of Positioning, Navigation, and Timing
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    • v.2 no.1
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    • pp.81-89
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    • 2013
  • Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

A Method for Effective Homography Estimation Applying a Depth Image-Based Filter (깊이 영상 기반 필터를 적용한 효과적인 호모그래피 추정 방법)

  • Joo, Yong-Joon;Hong, Myung-Duk;Yoon, Ui-Nyoung;Go, Seung-Hyun;Jo, Geun-Sik
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.2
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    • pp.61-66
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    • 2019
  • Augmented reality is a technology that makes a virtual object appear as if it exists in reality by composing a virtual object in real time with the image captured by the camera. In order to augment the virtual object on the object existing in reality, the homography of images utilized to estimate the position and orientation of the object. The homography can be estimated by applying the RANSAC algorithm to the feature points of the images. But the homography estimation method using the RANSAC algorithm has a problem that accurate homography can not be estimated when there are many feature points in the background. In this paper, we propose a method to filter feature points of a background when the object is near and the background is relatively far away. First, we classified the depth image into relatively near region and a distant region using the Otsu's method and improve homography estimation performance by filtering feature points on the relatively distant area. As a result of experiment, processing time is shortened 71.7% compared to a conventional homography estimation method, and the number of iterations of the RANSAC algorithm was reduced 69.4%, and Inlier rate was increased 16.9%.

Robust Estimation of Camera Motion using Fuzzy Classification Method (퍼지 분류기법을 이용한 강건한 카메라 동작 추정)

  • Lee, Joong-Jae;Kim, Gye-Young;Choi, Hyung-Il
    • The KIPS Transactions:PartB
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    • v.13B no.7 s.110
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    • pp.671-678
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    • 2006
  • In this paper, we propose a method for robustly estimating camera motion using fuzzy classification from the correspondences between two images. We use a RANSAC(Random Sample Consensus) algorithm to obtain accurate camera motion estimates in the presence of outliers. The drawback of RANSAC is that its performance depends on a prior knowledge of the outlier ratio. To resolve this problem the proposed method classifies samples into three classes(good sample set, bad sample set and vague sample set) using fuzzy classification. It then improves classification accuracy omitting outliers by iteratively sampling in only good sample set. The experimental results show that the proposed approach is very effective for computing a homography.

Development of a Microscopic Gap Measuring Algorithm with a Fuzzy-RANSAC (퍼지란삭을 이용한 미소 거리 측정 알고리즘 개발)

  • Kim, Jae-Hoon;Park, Seung-Kyu;Yoon, Tae-Sung
    • Proceedings of the KIEE Conference
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    • 2008.07a
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    • pp.1545-1546
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    • 2008
  • In this study, an image processing method with FRANSAC(Fuzzy RANSAC) is presented and discussed for the development of a microscopic gap measuring algorithm. Many problems in edge detection processing are mainly occurred by the illumination system. A serious problem is that the edge set of gap could include the error elements that have relatively larger error than normal. This problem leads to a incorrect measurement of gap. We present a gap measuring algorithm using FRANSAC[1] that is a representative robust estimation algorithm. FRANSAC is peformed by first categorizing all data into good sample set, bad sample set and vague sample set using a fuzzy classification and then sampling in only good sample set. Experimental results show that the presented gap measuring algorithm gives a higher accurate value of gap especially for the more noisy image data.

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LiDAR based Real-time Ground Segmentation Algorithm for Autonomous Driving (자율주행을 위한 라이다 기반의 실시간 그라운드 세그멘테이션 알고리즘)

  • Lee, Ayoung;Yi, Kyongsu
    • Journal of Auto-vehicle Safety Association
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    • v.14 no.2
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    • pp.51-56
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    • 2022
  • This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

A Method for Improving Object Recognition Using Pattern Recognition Filtering (패턴인식 필터링을 적용한 물체인식 성능 향상 기법)

  • Park, JinLyul;Lee, SeungGi
    • Journal of the Institute of Electronics and Information Engineers
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    • v.53 no.6
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    • pp.122-129
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    • 2016
  • There have been a lot of researches on object recognition in computer vision. The SURF(Speeded Up Robust Features) algorithm based on feature detection is faster and more accurate than others. However, this algorithm has a shortcoming of making an error due to feature point mismatching when extracting feature points. In order to increase a success rate of object recognition, we have created an object recognition system based on SURF and RANSAC(Random Sample Consensus) algorithm and proposed the pattern recognition filtering. We have also presented experiment results relating to enhanced the success rate of object recognition.

Stable and Precise Multi-Lane Detection Algorithm Using Lidar in Challenging Highway Scenario (어려운 고속도로 환경에서 Lidar를 이용한 안정적이고 정확한 다중 차선 인식 알고리즘)

  • Lee, Hanseul;Seo, Seung-Woo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.52 no.12
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    • pp.158-164
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    • 2015
  • Lane detection is one of the key parts among autonomous vehicle technologies because lane keeping and path planning are based on lane detection. Camera is used for lane detection but there are severe limitations such as narrow field of view and effect of illumination. On the other hands, Lidar sensor has the merits of having large field of view and being little influenced by illumination because it uses intensity information. Existing researches that use methods such as Hough transform, histogram hardly handle multiple lanes in the co-occuring situation of lanes and road marking. In this paper, we propose a method based on RANSAC and regularization which provides a stable and precise detection result in the co-occuring situation of lanes and road marking in highway scenarios. This is performed by precise lane point extraction using circular model RANSAC and regularization aided least square fitting. Through quantitative evaluation, we verify that the proposed algorithm is capable of multi lane detection with high accuracy in real-time on our own acquired road data.