• Title/Summary/Keyword: Corner detection method

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High-speed Object Detection in a Mobile Terminal Environment (휴대단말 고속 객체 검출)

  • Lee, Jae-Ho;Lee, Chul-Hee
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.05a
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    • pp.646-648
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    • 2012
  • In this paper, an image detection technique is proposed to extract image features in a mobile terminal environment. To detect objects, the HSI color model of the image is used. The object's corner points are detected using the Harris corner detection method. Finally we detect the object of interest using region growing The experiment results show that the proposed method improves detection performance and reduces the amount of computation.

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Head Detection based on Foreground Pixel Histogram Analysis (전경픽셀 히스토그램 분석 기반의 머리영역 검출 기법)

  • Choi, Yoo-Joo;Son, Hyang-Kyoung;Park, Jung-Min;Moon, Nam-Mee
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.11
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    • pp.179-186
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    • 2009
  • In this paper, we propose a head detection method based on vertical and horizontal pixel histogram analysis in order to overcome drawbacks of the previous head detection approach using Haar-like feature-based face detection. In the proposed method, we create the vertical and horizontal foreground pixel histogram images from the background subtraction image, which represent the number of foreground pixels in the same vertical or horizontal position. Then we extract feature points of a head region by applying Harris corner detection method to the foreground pixel histogram images and by analyzing corner points. The proposal method shows robust head detection results even in the face image covering forelock by hairs or the back view image in which the previous approaches cannot detect the head regions.

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Comer Detection in Gray Lavel Images for Wafer Die Position Recognition (웨이퍼 다이 위치 인식을 위한 명암 영상 코너점 검출)

  • 나재형;오해석
    • Journal of KIISE:Software and Applications
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    • v.31 no.6
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    • pp.792-798
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    • 2004
  • In this paper, we will introduce a new corner detector for the wafer die position recognition. The die position recognition procedure is necessary for WSCSP(Wafer Scale Chip Scale Packaging) technology, decide the accuracy of post-procedure. We present a hierarchical gray level corner detection method for the recognition of the die position from a wafer image. The new corner detector divides the corner region into many homocentric circles, and calculates the comer response and the angle of direction about each circle to get an accurate toner point. The new corner detector has a hierarchical structure so it can detect comer point more quickly than general gray level corner detector.

A Vision-Based Method to Find Fingertips in a Closed Hand

  • Chaudhary, Ankit;Vatwani, Kapil;Agrawal, Tushar;Raheja, J.L.
    • Journal of Information Processing Systems
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    • v.8 no.3
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    • pp.399-408
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    • 2012
  • Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

Regularization Parameter Determination for Optical Flow Estimation using L-curve (L-curve를 이용한 광학 흐름 추정을 위한 정규화 매개변수 결정)

  • Kim, Jong-Dae;Kim, Jong-Won
    • The KIPS Transactions:PartB
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    • v.14B no.4
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    • pp.241-248
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    • 2007
  • An L-curve corner detection method is proposed for the determination of the regularization parameter in optical flow estimation. The method locates the positive peak whose curvature difference from the just right-hand negative valley is the maximum in the curvature plot of the L-curve. while the existing curvature-method simply finds the maximum in the plot. Experimental results show that RMSE of the estimated optical flow is greater only by 0.02 pixels-per-frame than the least in the average sense. The proposed method is also compared with an existing curvature-method and the adaptive pruning method, resulting in the optical flow estimation closest to the least RMSE.

A Novel Corner Detector using a Non-cornerness Measure

  • Park, Seokmok;Cho, Woon;Paik, Joonki
    • IEIE Transactions on Smart Processing and Computing
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    • v.6 no.4
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    • pp.253-261
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    • 2017
  • In this paper, a corner detection method based on a new non-cornerness measure is presented. Rather than evaluating local gradients or surface curvatures, as done in previous approaches, a non-cornerness function is developed that can identify stable corners by testing an image region against a set of desirable corner criteria. The non-cornerness function is comprised of two steps: 1) eliminate any pixel located in a flat region and 2) remove any pixel that is positioned along an edge in any orientation. A pixel that passes the non-cornerness test is considered a reliable corner. The proposed method also adopts the idea of non-maximum suppression to remove multiple corners from the results of the non-cornerness function. The proposed method is compared with previous popular methods and is tested with an artificial test image covering several corner forms and three real-world images that are universally used by the community to evaluate the accuracy of corner detectors. The experimental results show that the proposed method outperforms previous corner detectors with respect to accuracy, and that it is suitable for real-time processing.

Application of Image Processing Techniques to GPR Data for the Reliability Improvement in Subsurface Void Analysis (지표레이더(GPR) 탐사자료를 이용한 지하공동 분석 시 신뢰도 향상을 위한 영상처리기법의 활용)

  • Kim, Bona;Seol, Soon Jee;Byun, Joongmoo
    • Geophysics and Geophysical Exploration
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    • v.20 no.2
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    • pp.61-71
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    • 2017
  • Recently, ground-penetrating radar (GPR) surveys have been actively carried out for precise subsurface void investigation because of the rapid increase of subsidence in urban areas. However, since the interpretation of GPR data was conducted based on the interpreter's subjective decision after applying only the basic data processing, it can result in reliability problems. In this research, to solve these problems, we analyzed the difference between the events generated from subsurface voids and those of strong diffraction sources such as the buried pipeline by applying the edge detection technique, which is one of image processing technologies. For the analysis, we applied the image processing technology to the GRP field data containing events generated from the cavity or buried pipeline. As a result, the main events by the subsurface void or diffraction source were effectively separated using the edge detection technique. In addition, since subsurface voids associated with the subsidence has a relatively wide scale, it is recorded as a gentle slope event unlike the event caused by the strong diffraction source recorded with a sharp slope. Therefore, the directional analysis of amplitude variation in the image enabled us to effectively separate the events by the subsurface void from those by the diffraction source. Interpretation based on these kinds of objective analysis can improve the reliability. Moreover, if suggested techniques are verified to various GPR field data sets, these approaches can contribute to semiautomatic interpretation of large amount of GPR data.