• Title/Summary/Keyword: speeded-up robust feature (SURF)

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The Target Detection and Classification Method Using SURF Feature Points and Image Displacement in Infrared Images (적외선 영상에서 변위추정 및 SURF 특징을 이용한 표적 탐지 분류 기법)

  • Kim, Jae-Hyup;Choi, Bong-Joon;Chun, Seung-Woo;Lee, Jong-Min;Moon, Young-Shik
    • Journal of the Korea Society of Computer and Information
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    • v.19 no.11
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    • pp.43-52
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    • 2014
  • In this paper, we propose the target detection method using image displacement, and classification method using SURF(Speeded Up Robust Features) feature points and BAS(Beam Angle Statistics) in infrared images. The SURF method that is a typical correspondence matching method in the area of image processing has been widely used, because it is significantly faster than the SIFT(Scale Invariant Feature Transform) method, and produces a similar performance. In addition, in most SURF based object recognition method, it consists of feature point extraction and matching process. In proposed method, it detects the target area using the displacement, and target classification is performed by using the geometry of SURF feature points. The proposed method was applied to the unmanned target detection/recognition system. The experimental results in virtual images and real images, we have approximately 73~85% of the classification performance.

Localization of Mobile Robot Using SURF and Particle Filter (SURF와 Particle filter를 이용한 이동 로봇의 위치 추정)

  • Mun, Hyun-Su;Joo, Young-Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.4
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    • pp.586-591
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    • 2010
  • In this paper, we propose the localization method of mobile robot using SURF(Speeded-Up Robust Features) and Particle filter. The proposed method is as follows: First, we seek the Landmark from the obtained image using SURF in order to find the first rigorous position of mobile robot. Second, we obtain the distance from obstacles using ultrasonic sensors in order to create the relative position of mobile robot. And then, we estimate the localization of mobile robot using Particle filter about movement of mobile robot. Finally, we show the feasibility of the proposed method through some experiments.

GPU based Fast Recognition of Artificial Landmark for Mobile Robot (주행로봇을 위한 GPU 기반의 고속 인공표식 인식)

  • Kwon, Oh-Sung;Kim, Young-Kyun;Cho, Young-Wan;Seo, Ki-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.5
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    • pp.688-693
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    • 2010
  • Vision based object recognition in mobile robots has many issues for image analysis problems with neighboring elements in dynamic environments. SURF(Speeded Up Robust Features) is the local feature extraction method of the image and its performance is constant even if disturbances, such as lighting, scale change and rotation, exist. However, it has a difficulty of real-time processing caused by representation of high dimensional vectors. To solve th problem, execution of SURF in GPU(Graphics Processing Unit) is proposed and implemented using CUDA of NVIDIA. Comparisons of recognition rates and processing time for SURF between CPU and GPU by variation of robot velocity and image sizes is experimented.

A Feature-Based Robust Watermarking Scheme Using Circular Invariant Regions

  • Doyoddorj, Munkhbaatar;Rhee, Kyung-Hyung
    • Journal of Korea Multimedia Society
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    • v.16 no.5
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    • pp.591-600
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    • 2013
  • This paper addresses a feature-based robust watermarking scheme for digital images using a local invariant features of SURF (Speeded-Up Robust Feature) descriptor. In general, the feature invariance is exploited to achieve robustness in watermarking schemes, but the leakage of information about hidden watermarks from publicly known locations and sizes of features are not considered carefully in security perspective. We propose embedding and detection methods where the watermark is bound with circular areas and inserted into extracted circular feature regions. These methods enhance the robustness since the circular watermark is inserted into the selected non-overlapping feature regions instead of entire image contents. The evaluation results for repeatability measures of SURF descriptor and robustness measures present the proposed scheme can tolerate various attacks, including signal processing and geometric distortions.

Objects Recognition and Intelligent Walking for Quadruped Robots based on Genetic Programming (4족 보행로봇의 물체 인식 및 GP 기반 지능적 보행)

  • Kim, Young-Kyun;Hyun, Soo-Hwan;Jang, Jae-Young;Seo, Ki-Sung
    • Journal of the Korean Institute of Intelligent Systems
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    • v.20 no.5
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    • pp.603-609
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    • 2010
  • This paper introduces an objects recognition algorithm based on SURF(Speeded Up Robust Features) and GP(Genetic Programming) based gaits generation. Combining both methods, a recognition based intelligent walking for quadruped robots is proposed. The gait of quadruped robots is generated by means of symbolic regression for each joint trajectories using GP. A position and size of target object are recognized by SURF which enables high speed feature extraction, and then the distance to the object is calculated. Experiments for objects recognition and autonomous walking for quadruped robots are executed for ODE based Webots simulation and real robot.

Modified Speeded Up Robust Features(SURF) for Performance Enhancement of Mobile Visual Search System (모바일 시각 검색 시스템의 성능 향상을 위하여 개선된 Speeded Up Robust Features(SURF) 알고리듬)

  • Seo, Jung-Jin;Yoona, Kyoung-Ro
    • Journal of Broadcast Engineering
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    • v.17 no.2
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    • pp.388-399
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    • 2012
  • In the paper, we propose enhanced feature extraction and matching methods for a mobile environment based on modified SURF. We propose three methods to reduce the computational complexity in a mobile environment. The first is to reduce the dimensions of the SURF descriptor. We compare the performance of existing 64-dimensional SURF with several other dimensional SURFs. The second is to improve the performance using the sign of the trace of the Hessian matrix. In other words, feature points are considered as matched if they have the same sign for the trace of the Hessian matrix, otherwise considered not matched. The last one is to find the best distance-ratio which is used to determine the matching points. We find the best distance-ratio through experiments, and it gives the relatively high accuracy. Finally, existing system which is based on normal SURF method is compared with our proposed system which is based on these three proposed methods. We present that our proposed system shows reduced response time while preserving reasonably good matching accuracy.

View invariant image matching using SURF (SURF(speed up robust feature)를 이용한 시점변화에 강인한 영상 매칭)

  • Son, Jong-In;Kang, Minsung;Sohn, Kwanghoon
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.222-225
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    • 2011
  • 영상 매칭은 컴퓨터 비전에서 중요한 기초 기술 중에 하나이다. 하지만 스케일, 회전, 조명, 시점변화에 강인한 대응점을 찾는 것은 쉬운 작업이 아니다. 이러한 문제점을 보안하기 위해서 스케일 불변 특징 변환(Scale Invariant Feature Transform) 고속의 강인한 특징 추출(Speeded up robust features) 알고리즘등에 제안되었지만, 시점 변화에 있어서 취약한 문제점을 나타냈다. 본 논문에서는 이런 문제점을 해결하기 위해서 시점 변화에 강인한 알고리즘을 제안하였다. 시점 변화에 강인한 영상매칭을 위해서 원본 영상과 질의 영상간 유사도 높은 특징점들의 호모그래피 변환을 이용해서 질의 영상을 원본 영상과 유사하게 보정한 뒤에 매칭을 통해서 시점 변화에 강인한 알고리즘을 구현하였다. 시점이 변화된 여러 영상을 통해서 기존 SIFT,SURF와 성능과 수행 시간을 비교 함으로서, 본 논문에서 제안한 알고리즘의 우수성을 입증 하였다.

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Panoramic Image Stitching using SURF

  • You, Meng;Lim, Jong-Seok;Kim, Wook-Hyun
    • Journal of the Institute of Convergence Signal Processing
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    • v.12 no.1
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    • pp.26-32
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    • 2011
  • This paper proposes a new method to process panoramic image stitching using SURF(Speeded Up Robust Features). Panoramic image stitching is considered a problem of the correspondence matching. In computer vision, it is difficult to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. However, SURF algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform). In this work, we also describe an efficient approach to decreasing computation time through the homography estimation using RANSAC(random sample consensus). RANSAC is a robust estimation procedure that uses a minimal set of randomly sampled correspondences to estimate image transformation parameters. Experimental results show that our method is robust to rotation, zoom, Gaussian noise and illumination change of the input images and computation time is greatly reduced.

Object Detection and Classification Using Extended Descriptors for Video Surveillance Applications (비디오 감시 응용에서 확장된 기술자를 이용한 물체 검출과 분류)

  • Islam, Mohammad Khairul;Jahan, Farah;Min, Jae-Hong;Baek, Joong-Hwan
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.48 no.4
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    • pp.12-20
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    • 2011
  • In this paper, we propose an efficient object detection and classification algorithm for video surveillance applications. Previous researches mainly concentrated either on object detection or classification using particular type of feature e.g., Scale Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) etc. In this paper we propose an algorithm that mutually performs object detection and classification. We combinedly use heterogeneous types of features such as texture and color distribution from local patches to increase object detection and classification rates. We perform object detection using spatial clustering on interest points, and use Bag of Words model and Naive Bayes classifier respectively for image representation and classification. Experimental results show that our combined feature is better than the individual local descriptor in object classification rate.

Error Correction Scheme in Location-based AR System Using Smartphone (스마트폰을 이용한 위치정보기반 AR 시스템에서의 부정합 현상 최소화를 위한 기법)

  • Lee, Ju-Yong;Kwon, Jun-Sik
    • Journal of Digital Contents Society
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    • v.16 no.2
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    • pp.179-187
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    • 2015
  • Spread of smartphone creates various contents. Among many contents, AR application using Location Based Service(LBS) is needed widely. In this paper, we propose error correction algorithm for location-based Augmented Reality(AR) system using computer vision technology in android environment. This method that detects the early features with SURF(Speeded Up Robust Features) algorithm to minimize the mismatch and to reduce the operations, and tracks the detected, and applies it in mobile environment. We use the GPS data to retrieve the location information, and use the gyro sensor and G-sensor to get the pose estimation and direction information. However, the cumulative errors of location information cause the mismatch that and an object is not fixed, and we can not accept it the complete AR technology. Because AR needs many operations, implementation in mobile environment has many difficulties. The proposed approach minimizes the performance degradation in mobile environments, and are relatively simple to implement, and a variety of existing systems can be useful in a mobile environment.