• Title/Summary/Keyword: 물체 인식 향상

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Probabilistic Graph Based Object Category Recognition Using the Context of Object-Action Interaction (물체-행동 컨텍스트를 이용하는 확률 그래프 기반 물체 범주 인식)

  • Yoon, Sung-baek;Bae, Se-ho;Park, Han-je;Yi, June-ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.40 no.11
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    • pp.2284-2290
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    • 2015
  • The use of human actions as context for object class recognition is quite effective in enhancing the recognition performance despite the large variation in the appearance of objects. We propose an efficient method that integrates human action information into object class recognition using a Bayesian appraoch based on a simple probabilistic graph model. The experiment shows that by using human actions ac context information we can improve the performance of the object calss recognition from 8% to 28%.

Object Relationship Modeling based on Bayesian Network Integration for Improving Object Detection Performance of Service Robots (서비스 로봇의 물체 탐색 성능 향상을 위한 베이지안 네트워크 결합 기반 물체 관계 모델링)

  • Song Youn-Suk;Cho Sung-Bae
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.7
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    • pp.817-822
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    • 2005
  • Recently tile study that exploits visual information for tile services of robot in indoor environments is active. Conventional image processing approaches are based on the pre-defined geometric models, so their performances are likely to decrease when they are applied to the uncertain and dynamic environments. For this, diverse researches to manage the uncertainty based on the knowledge for improving image recognition performance have been doing. In this paper we propose a Bayesian network modeling method for predicting the existence of target objects when they are occluded by other ones for improving the object detection performance of the service robots. The proposed method makes object relationship, so that it allows to predict the target object through observed ones. For this, we define the design method for small size Bayesian networks (primitive Bayesian netqork), and allow to integrate them following to the situations. The experiments are performed for verifying the performance of constructed model, and they shows $82.8\%$ of accuracy in 5 places.

Improvement of Region Growing Algorithm (영역 확장 알고리듬의 개선)

  • Kim, Kyong-Wook;Lee, Hyo-Jong;Song, Myung-Sung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2002.04a
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    • pp.745-748
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    • 2002
  • 다양한 형태, 독특한 질감, 불균일한 조명과 같은 야외 영상의 특징 때문에, 윤곽선 탐지 방법은 단일 물체나 단순한 형태의 물체 등을 인식하는데 있어서 적합하지 않다. 따라서 본 논문에서는 주변에 있는 물체들과 명확히 구분하여 객체의 인식에 향상을 가져 올 수 있는 영역 확장 법을 실험하였다.

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Improvement and Implementation of Region Growing Algorithm (영역확장 알고리듬 개선 및 구현)

  • Lee, Hyo-Jong;Song, Myung-Sung
    • Proceedings of the Korea Information Processing Society Conference
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    • 2001.10a
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    • pp.715-718
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    • 2001
  • 자연영상 같은 야외영상의 특징으로는 산재하는 대상, 다양한 형태, 독특한 질감, 불균일한 조명 상태 등을 들 수 있는데, 이러한 특징으로 인하여 단일 물체나 단순한 형태의 물체 등을 인식하는 윤곽선 탐지 방법 등의 사용은 적합하지 않다. 따라서 본 논문에서는 다양한 질감이나, 주변에 있는 물체들과 명확히 구분하여 객체의 인식에 향상을 가져 올 수 있는 영역 확장 법을 실험하였다.

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Performance Improvement of Pedestrian Detection using a GM-PHD Filter (GM-PHD 필터를 이용한 보행자 탐지 성능 향상 방법)

  • Lee, Yeon-Jun;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.150-157
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    • 2015
  • Pedestrian detection has largely been researched as one of the important technologies for autonomous driving vehicle and preventing accidents. There are two categories for pedestrian detection, camera-based and LIDAR-based. LIDAR-based methods have the advantage of the wide angle of view and insensitivity of illuminance change while camera-based methods have not. However, there are several problems with 3D LIDAR, such as insufficient resolution to detect distant pedestrians and decrease in detection rate in a complex situation due to segmentation error and occlusion. In this paper, two methods using GM-PHD filter are proposed to improve the poor rates of pedestrian detection algorithms based on 3D LIDAR. First one improves detection performance and resolution of object by automatic accumulation of points in previous frames onto current objects. Second one additionally enhances the detection results by applying the GM-PHD filter which is modified in order to handle the poor situation to classified multi target. A quantitative evaluation with autonomously acquired road environment data shows the proposed methods highly increase the performance of existing pedestrian detection algorithms.

Real-time Moving Object Recognition and Tracking Using The Wavelet-based Neural Network and Invariant Moments (웨이블릿 기반의 신경망과 불변 모멘트를 이용한 실시간 이동물체 인식 및 추적 방법)

  • Kim, Jong-Bae
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.45 no.4
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    • pp.10-21
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    • 2008
  • The present paper propose a real-time moving object recognition and tracking method using the wavelet-based neural network and invariant moments. Candidate moving region detection phase which is the first step of the proposed method detects the candidate regions where a pixel value changes occur due to object movement based on the difference image analysis between continued two image frames. The object recognition phase which is second step of proposed method recognizes the vehicle regions from the detected candidate regions using wavelet neurual-network. From object tracking Phase which is third step the recognized vehicle regions tracks using matching methods of wavelet invariant moments bases to recognized object. To detect a moving object from image sequence the candidate regions detection phase uses an adaptive thresholding method between previous image and current image as result it was robust surroundings environmental change and moving object detections were possible. And by using wavelet features to recognize and tracking of vehicle, the proposed method decrease calculation time and not only it will be able to minimize the effect in compliance with noise of road image, vehicle recognition accuracy became improved. The result which it experiments from the image which it acquires from the general road image sequence and vehicle detection rate is 92.8%, the computing time per frame is 0.24 seconds. The proposed method can be efficiently apply to a real-time intelligence road traffic surveillance system.

Viola-Jones Object Detection Method to Add a rectangular feature and a variance feature (사각특징 및 분산특징을 추가한 비올라존스 물체 검출 알고리즘)

  • Hong, Young-Min;Lee, In-Sung;Park, Jong-Soon;Jo, Yong-Sung;Kim, Chang-Bum
    • Proceedings of the KIEE Conference
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    • 2011.07a
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    • pp.1995-1996
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    • 2011
  • 영상 혹은 이미지 내에서 특정 물체를 검출하기 위한 일반적인 방법으로 비올라 존스(Viola-Jones) 알고리즘을 많이 사용하고 있으나 이 방법은 배경의 픽셀이 거의 동일한 값일 때에는 매우 높은 검출률을 보이는 반면 다양한 배경과 물체가 복합적으로 이루어져 있는 이미지의 경우에는 물체 검출 시 오인식 할 가능성이 매우 높다. 이러한 단점들은 얼굴 인식 시스템을 개발함에 있어 매우 불리하게 적용될 수 있는데, 본 연구는 얼굴인식 출입통제 시스템을 개발하는 과정에서 발생하는 이러한 문제점을 보완하기 위하여 비올라 존스 알고리즘에서 이용되는 하 라이크 피춰(Harr-like feature)에 새로운 형태의 사각 특징(Rectangular feature) 및 분산 특징(Varience feature) 추가하여 복잡한 배경에서도 물체 검출 성능을 향상시킬 수 있는 방법을 제안한다.

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Study on vision-based object recognition to improve performance of industrial manipulator (산업용 매니퓰레이터의 작업 성능 향상을 위한 영상 기반 물체 인식에 관한 연구)

  • Park, In-Cheol;Park, Jong-Ho;Ryu, Ji-Hyoung;Kim, Hyoung-Ju;Chong, Kil-To
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.18 no.4
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    • pp.358-365
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    • 2017
  • In this paper, we propose an object recognition method using image information to improve the efficiency of visual servoingfor industrial manipulators in industry. This is an image-processing method for real-time responses to an abnormal situation or to external environment change in a work object by utilizing camera-image information of an industrial manipulator. The object recognition method proposed in this paper uses the Otsu method, a thresholding technique based on separation of the V channel containing color information and the S channel, in which it is easy to separate the background from the HSV channel in order to improve the recognition rate of the existing Harris Corner algorithm. Through this study, when the work object is not placed in the correct position due to external factors or from being twisted,the position is calculated and provided to the industrial manipulator.

Model-Based Object Recognition using PCA & Improved k-Nearest Neighbor (PCA와 개선된 k-Nearest Neighbor를 이용한 모델 기반형 물체 인식)

  • Jung Byeong-Soo;Kim Byung-Gi
    • The KIPS Transactions:PartB
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    • v.13B no.1 s.104
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    • pp.53-62
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    • 2006
  • Object recognition techniques using principal component analysis are disposed to be decreased recognition rate when lighting change of image happens. The purpose of this thesis is to propose an object recognition technique using new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. And the object recognition algorithm proposed here represents more enhanced recognition rate using improved k-Nearest Neighbor. In this thesis, we proposed an object recognition algorithm which creates object space by pre-processing and being learned image using histogram equalization and median filter. By spreading histogram of test image using histogram equalization, the effect to change of illumination is reduced. This method is stronger to change of illumination than basic PCA method and normalization, and almost removes effect of illumination, therefore almost maintains constant good recognition rate. And, it compares ingredient projected test image into object space with distance of representative value and recognizes after representative value of each object in model image is made. Each model images is used in recognition unit about some continual input image using improved k-Nearest Neighbor in this thesis because existing method have many errors about distance calculation.

Improved recognition of 3D objects using nonlinear correlator based on direct pixel mapping in curving-effective integral imaging (커브형 집적 영상에서 DPM 기반의 비선형 상관기를 이용한 3D 물체 인식 향상)

  • Lee, Joon-Jae;Shin, Donghak;Lee, Byung-Gook
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.1
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    • pp.190-196
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    • 2013
  • Curved integral imaging is a simple method to display 3D images in space using lens array and provides wide viewing angle. In this paper, we propose a nonlinear 3D correlator based on the direct pixel-mapping (DPM) method in order to improve the recognition performance of 3D target object in curving-effective integral imaging. With this scheme, the elemental image array (EIA) originally picked up from a partially occluded 3-D target object can be converted into a resolution enhanced new EIA by using DPM method. Then, through nonlinear cross-correlations between the reconstructed reference and the target plane images, the improved pattern recognition can be performed from the correlation outputs. To show the feasibility of the proposed method, some preliminary experiments are carried out and results are presented by comparing the conventional method.