• Title/Summary/Keyword: Geometric Visual Feature

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A Camera Pose Estimation Method for Rectangle Feature based Visual SLAM (사각형 특징 기반 Visual SLAM을 위한 자세 추정 방법)

  • Lee, Jae-Min;Kim, Gon-Woo
    • The Journal of Korea Robotics Society
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    • v.11 no.1
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    • pp.33-40
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    • 2016
  • In this paper, we propose a method for estimating the pose of the camera using a rectangle feature utilized for the visual SLAM. A warped rectangle feature as a quadrilateral in the image by the perspective transformation is reconstructed by the Coupled Line Camera algorithm. In order to fully reconstruct a rectangle in the real world coordinate, the distance between the features and the camera is needed. The distance in the real world coordinate can be measured by using a stereo camera. Using properties of the line camera, the physical size of the rectangle feature can be induced from the distance. The correspondence between the quadrilateral in the image and the rectangle in the real world coordinate can restore the relative pose between the camera and the feature through obtaining the homography. In order to evaluate the performance, we analyzed the result of proposed method with its reference pose in Gazebo robot simulator.

A New Feature-Based Visual SLAM Using Multi-Channel Dynamic Object Estimation (다중 채널 동적 객체 정보 추정을 통한 특징점 기반 Visual SLAM)

  • Geunhyeong Park;HyungGi Jo
    • IEMEK Journal of Embedded Systems and Applications
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    • v.19 no.1
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    • pp.65-71
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    • 2024
  • An indirect visual SLAM takes raw image data and exploits geometric information such as key-points and line edges. Due to various environmental changes, SLAM performance may decrease. The main problem is caused by dynamic objects especially in highly crowded environments. In this paper, we propose a robust feature-based visual SLAM, building on ORB-SLAM, via multi-channel dynamic objects estimation. An optical flow and deep learning-based object detection algorithm each estimate different types of dynamic object information. Proposed method incorporates two dynamic object information and creates multi-channel dynamic masks. In this method, information on actually moving dynamic objects and potential dynamic objects can be obtained. Finally, dynamic objects included in the masks are removed in feature extraction part. As a results, proposed method can obtain more precise camera poses. The superiority of our ORB-SLAM was verified to compared with conventional ORB-SLAM by the experiment using KITTI odometry dataset.

Image Watermarking Scheme Based on Scale-Invariant Feature Transform

  • Lyu, Wan-Li;Chang, Chin-Chen;Nguyen, Thai-Son;Lin, Chia-Chen
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.10
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    • pp.3591-3606
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    • 2014
  • In this paper, a robust watermarking scheme is proposed that uses the scale-invariant feature transform (SIFT) algorithm in the discrete wavelet transform (DWT) domain. First, the SIFT feature areas are extracted from the original image. Then, one level DWT is applied on the selected SIFT feature areas. The watermark is embedded by modifying the fractional portion of the horizontal or vertical, high-frequency DWT coefficients. In the watermark extracting phase, the embedded watermark can be directly extracted from the watermarked image without requiring the original cover image. The experimental results showed that the proposed scheme obtains the robustness to both signal processing and geometric attacks. Also, the proposed scheme is superior to some previous schemes in terms of watermark robustness and the visual quality of the watermarked image.

Methods of Making Samples for a Visual Experiment with Feature Lines of Outer Automotive Panels (자동차 외판 특징선의 시각적 분석을 위한 시편 제작방법)

  • Han, Juho;Chung, Yunchan
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.24 no.4
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    • pp.455-462
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    • 2015
  • A feature line is a visually noticeable creased line on outer automotive panels. Feature lines play an important role in creating a good impression of a car. Even though the manufacturing quality of feature lines is important, it is difficult to achieve the designed shape owing to the springback of sheet metal. The current study presents five methods of making samples that will be used in a visual experiment to discover a quality control quantitative manufacturing allowance for feature lines. Measurement and inspection methods for the samples are also presented. The results show that plunge machining is the most accurate way to make the desired shape, and that wrapping the machined surface with sheet film is an appropriate way to emulate the roughness and visual texture of the painted outer panels of a car.

Development of the Computer Vision based Continuous 3-D Feature Extraction System via Laser Structured Lighting (레이저 구조광을 이용한 3차원 컴퓨터 시각 형상정보 연속 측정 시스템 개발)

  • Im, D. H.;Hwang, H.
    • Journal of Biosystems Engineering
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    • v.24 no.2
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    • pp.159-166
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    • 1999
  • A system to extract continuously the real 3-D geometric fearture information from 2-D image of an object, which is fed randomly via conveyor has been developed. Two sets of structured laser lightings were utilized. And the laser structured light projection image was acquired using the camera from the signal of the photo-sensor mounted on the conveyor. Camera coordinate calibration matrix was obtained, which transforms 2-D image coordinate information into 3-D world space coordinate using known 6 points. The maximum error after calibration showed 1.5 mm within the height range of 103mm. The correlation equation between the shift amount of the laser light and the height was generated. Height information estimated after correlation showed the maximum error of 0.4mm within the height range of 103mm. An interactive 3-D geometric feature extracting software was developed using Microsoft Visual C++ 4.0 under Windows system environment. Extracted 3-D geometric feature information was reconstructed into 3-D surface using MATLAB.

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An Image Quality Evaluation Model for Optical Strip Signal-to-Noise Ratio in the Target Area of High Temperature Forgings

  • Ma, Hongtao;Zhao, Yuyang;Feng, Yiran;Lee, Eung-Joo;Tao, Xueheng
    • Journal of Multimedia Information System
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    • v.8 no.2
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    • pp.93-100
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    • 2021
  • Under the time-varying temperature, the high-temperature radiation of forgings and the change of reflection characteristics of oxide skin on the surface of forgings lead to the difficulty of obtaining images to truly reflect the geometric characteristics of forgings. It is urgent to study the clear and reliable acquisition method of hot forging feature image under time-varying temperature to meet the requirements of visual measurement of hot geometric parameters of forgings. Based on this, this chapter first puts forward the quality evaluation method of forging feature image, which provides guarantee for the accurate evaluation of feature image quality. Furthermore, the factors that affect the image quality, such as the radiation characteristics of forgings and the photographic characteristics of cameras, are analyzed, and the imaging spectrum which can effectively suppress the radiation intensity of forgings is determined. Finally, aiming at the problem that the quality of image acquisition is difficult to guarantee due to the drastic change of radiation intensity of forgings under time-varying temperature, an image acquisition method based on minimum signal-to-noise ratio (SNR) based laser light intensity adaptation is proposed, which significantly improves the definition of feature light strips in forging images at high temperature, and finally realizes the clear acquisition of feature images of large-scale hot forging under time-varying temperature.

Automatic Extraction and Measurement of Visual Features of Mushroom (Lentinus edodes L.) (표고 외관 특징점의 자동 추출 및 측정)

  • Hwang, Heon;Lee, Yong-Guk
    • Journal of Bio-Environment Control
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    • v.1 no.1
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    • pp.37-51
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    • 1992
  • Quantizing and extracting visual features of mushroom(Lentinus edodes L.) are crucial to the sorting and grading automation, the growth state measurement, and the dried performance indexing. A computer image processing system was utilized for the extraction and measurement of visual features of front and back sides of the mushroom. The image processing system is composed of the IBM PC compatible 386DK, ITEX PCVISION Plus frame grabber, B/W CCD camera, VGA color graphic monitor, and image output RGB monitor. In this paper, an automatic thresholding algorithm was developed to yield the segmented binary image representing skin states of the front and back sides. An eight directional Freeman's chain coding was modified to solve the edge disconnectivity by gradually expanding the mask size of 3$\times$3 to 9$\times$9. A real scaled geometric quantity of the object was directly extracted from the 8-directional chain element. The external shape of the mushroom was analyzed and converted to the quantitative feature patterns. Efficient algorithms for the extraction of the selected feature patterns and the recognition of the front and back side were developed. The developed algorithms were coded in a menu driven way using MS_C language Ver.6.0, PC VISION PLUS library fuctions, and VGA graphic functions.

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Feature-based Disparity Correction for the Visual Discomfort Minimization of Stereoscopic Video Camera (입체영상의 시각 피로 최소화를 위한 특징기반 시차 보정)

  • Jung, Eun-Kyung;Kim, Chang-Il;Baek, Seung-Hae;Park, Soon-Yong
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.6
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    • pp.77-87
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    • 2011
  • In this paper, we propose a disparity correction technique to reduce the inherent visual discomfort while watching stereoscopic videos. The visual discomfort must be solved for commercial 3D display systems to provide natural stereoscopic videos to human eyes. The proposed disparity correction technique consists of horizontal and vertical disparity corrections. The horizontal disparity correction is implemented by controlling the depth budget of stereoscopic video using the geometric relations of a stereoscopic camera system. In addition, the vertical disparity correction is implemented by using a feature-based stereo matching algorithm. Conventional vertical disparity corrections have been done by only using camera calibration parameters, which still cause systematic errors in vertical disparities. In this paper, we minimize the vertical disparity as small as possible by using a feature-based correction algorithm. Through the comparisons of conventional feature-based correction algorithms, we analyze the performance of the proposed technique.

Kinematics and Control of a Visual Alignment System for Flat Panel Displays (평판 디스플레이 비전 정렬 시스템의 기구학 및 제어)

  • Kwon, Sang-Joo;Park, Chan-Sik;Lee, Sang-Moo
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.4
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    • pp.369-375
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    • 2008
  • The kinematics and control problem of a visual alignment system is investigated, which plays a crucial role in the fabrication process of flat panel displays. The first solution is the inverse kinematics of a 4PPR parallel alignment mechanism. It determines the driving distance of each joint to compensate the misalignment between mask and panel. Second, an efficient vision algorithm for fast alignment mark recognition is suggested, where by extracting essential feature points to represent the geometry of a mark, the geometric template matching enables much faster object recognition comparing with the general template matching. Finally, the overall visual alignment process including the kinematic solution, vision algorithm, and joint control is implemented and experimental results are given.

Geometric and Semantic Improvement for Unbiased Scene Graph Generation

  • Ruhui Zhang;Pengcheng Xu;Kang Kang;You Yang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.10
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    • pp.2643-2657
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    • 2023
  • Scene graphs are structured representations that can clearly convey objects and the relationships between them, but are often heavily biased due to the highly skewed, long-tailed relational labeling in the dataset. Indeed, the visual world itself and its descriptions are biased. Therefore, Unbiased Scene Graph Generation (USGG) prefers to train models to eliminate long-tail effects as much as possible, rather than altering the dataset directly. To this end, we propose Geometric and Semantic Improvement (GSI) for USGG to mitigate this issue. First, to fully exploit the feature information in the images, geometric dimension and semantic dimension enhancement modules are designed. The geometric module is designed from the perspective that the position information between neighboring object pairs will affect each other, which can improve the recall rate of the overall relationship in the dataset. The semantic module further processes the embedded word vector, which can enhance the acquisition of semantic information. Then, to improve the recall rate of the tail data, the Class Balanced Seesaw Loss (CBSLoss) is designed for the tail data. The recall rate of the prediction is improved by penalizing the body or tail relations that are judged incorrectly in the dataset. The experimental findings demonstrate that the GSI method performs better than mainstream models in terms of the mean Recall@K (mR@K) metric in three tasks. The long-tailed imbalance in the Visual Genome 150 (VG150) dataset is addressed better using the GSI method than by most of the existing methods.