• Title/Summary/Keyword: object feature set

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A Fast and Efficient Haar-Like Feature Selection Algorithm for Object Detection (객체검출을 위한 빠르고 효율적인 Haar-Like 피쳐 선택 알고리즘)

  • Chung, Byung Woo;Park, Ki-Yeong;Hwang, Sun-Young
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38A no.6
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    • pp.486-491
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    • 2013
  • This paper proposes a fast and efficient Haar-like feature selection algorithm for training classifier used in object detection. Many features selected by Haar-like feature selection algorithm and existing AdaBoost algorithm are either similar in shape or overlapping due to considering only feature's error rate. The proposed algorithm calculates similarity of features by their shape and distance between features. Fast and efficient feature selection is made possible by removing selected features and features with high similarity from feature set. FERET face database is used to compare performance of classifiers trained by previous algorithm and proposed algorithm. Experimental results show improved performance comparing classifier trained by proposed method to classifier trained by previous method. When classifier is trained to show same performance, proposed method shows 20% reduction of features used in classification.

Strategical matching algorithm for 3-D object recoginition (3차원 물체 인식을 위한 전략적 매칭 알고리듬)

  • 이상근;이선호;송호근;최종수
    • Journal of the Korean Institute of Telematics and Electronics C
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    • v.35C no.1
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    • pp.55-63
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    • 1998
  • This paper presents a new maching algorithm by Hopfield Neural Network for 3-D object recognition. In the proposed method, a model object is represented by a set of polygons in a single coordinate. And each polygon is described by a set of features; feature attributes. In case of 3-D object recognition, the scale and poses of the object are important factors. So we propose a strategy for 3-D object recognition independently to its scale and poses. In this strategy, the respective features of the input or the model objects are changed to the startegical constants when they are compared with one another. Finally, we show that the proposed method has a robustness through the results of experiments which included the classification of the input objects and the matching sequence to its 3-D rotation and scale.

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Open set Object Detection combining Multi-branch Tree and ASSL (다중 분기 트리와 ASSL을 결합한 오픈 셋 물체 검출)

  • Shin, Dong-Kyun;Ahmed, Minhaz Uddin;Kim, JinWoo;Rhee, Phill-Kyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.5
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    • pp.171-177
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    • 2018
  • Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

Development of Unique Naming Algorithm for 3D Straight Bridge Model Using Object Identification (3차원 직선교 모델 객체의 인식을 통한 고유 명칭부여 알고리즘 개발)

  • Park, Junwon;Park, Sang Il;Kim, Bong-Geun;Yoon, Young-Cheol;Lee, Sang-Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.27 no.6
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    • pp.557-564
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    • 2014
  • In this study, we present an algorithm that conducts an unique naming process for the bridge object through the solid object identification focused on 3D straight bridge model. For the recognition of 3D objects, the numerical algorithm utilizes centroid point, and solid object on the local coordination system. It classifies the object feature set by classifying the objects and members based on the bridge direction. By doing so, unique names, which contain the information about span, members and order of the object, were determined and the suitability of this naming algorithm was examined through a truss bridge model and a bridge model with different coordinate systems. Also, the naming process based on the object feature set was carried out for the real 3D bridge model and then was applied to the module on local server and mobile device for real bridge inspection work. From the comparison of the developed naming algorithm based on object identification and the conventional one based on field inspection, it was shown that the conventional field inspection work can be effectively improved.

Content-Based Image Retrieval System using Feature Extraction of Image Objects (영상 객체의 특징 추출을 이용한 내용 기반 영상 검색 시스템)

  • Jung Seh-Hwan;Seo Kwang-Kyu
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.27 no.3
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    • pp.59-65
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    • 2004
  • This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

High Efficient Viola-Jones Detection Framework for Real-Time Object Detection (실시간 물체 검출을 위한 고효율 Viola-Jones 검출 프레임워크)

  • Park, Byeong-Ju;Lee, Jae-Heung
    • Journal of IKEEE
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    • v.18 no.1
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    • pp.1-7
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    • 2014
  • In this paper, we suggest an improved Viola-Jones detection framework for the efficient feature selection and the fast rejection method of the sub-window. Our object detector has low computational complexity because it rejects sub-windows until specific threshold. Owing to using same framework, detection performance is same with the existing Viola-Jones detector. We measure the number of average feature calculation about MIT-CMU test set. As a result of the experiment, the number of average feature calculation is reduced to 45.5% and the detection speed is improved about 58.5% compared with the previous algorithm.

Object-based Image Retrieval for Color Query Image Detection (컬러 질의 영상 검출을 위한 객체 기반 영상 검색)

  • Baek, Young-Hyun;Moon, Sung-Ryong
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.45 no.3
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    • pp.97-102
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    • 2008
  • In this paper we propose an object-based image retrieval method using spatial color model and feature points registration method for an effective color query detection. The proposed method in other to overcome disadvantages of existing color histogram methods and then this method is use the HMMD model and rough set in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the database image and query image. Here, we select candidate regions in the similarity between the query image and database image. And we use SIFT registration methods in the selected region for object retrieving. The experimental results show that the proposed method is more satisfactory detection radio than conventional method.

Development of Robust Feature Detector Using Sonar Data (초음파 데이터를 이용한 강인한 형상 검출기 개발)

  • Lee, Se-Jin;Lim, Jong-Hwan;Cho, Dong-Woo
    • Journal of the Korean Society for Precision Engineering
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    • v.25 no.2
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    • pp.35-42
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    • 2008
  • This study introduces a robust feature detector for sonar data from a general fixed-type of sonar ring. The detector is composed of a data association filter and a feature extractor. The data association filter removes false returns provided frequently from sonar sensors, and classifies set of data from various objects and robot positions into a group in which all the data are from the same object. The feature extractor calculates the geometries of the feature for the group. We show the possibility of extracting circle feature as well as a line and a point features. The proposed method was applied to a real home environment with a real robot.

Real-time Multi-Objects Recognition and Tracking Scheme (실시간 다중 객체 인식 및 추적 기법)

  • Kim, Dae-Hoon;Rho, Seung-Min;Hwang, Een-Jun
    • Journal of Advanced Navigation Technology
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    • v.16 no.2
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    • pp.386-393
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    • 2012
  • In this paper, we propose an efficient multi-object recognition and tracking scheme based on interest points of objects and their feature descriptors. To do that, we first define a set of object types of interest and collect their sample images. For sample images, we detect interest points and construct their feature descriptors using SURF. Next, we perform a statistical analysis of the local features to select representative points among them. Intuitively, the representative points of an object are the interest points that best characterize the object. in addition, we make the movement vectors of the interest points based on matching between their SURF descriptors and track the object using these vectors. Since our scheme treats all the objects independently, it can recognize and track multiple objects simultaneously. Through the experiments, we show that our proposed scheme can achieve reasonable performance.

An Efficient Vision-based Object Detection and Tracking using Online Learning

  • Kim, Byung-Gyu;Hong, Gwang-Soo;Kim, Ji-Hae;Choi, Young-Ju
    • Journal of Multimedia Information System
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    • v.4 no.4
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    • pp.285-288
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    • 2017
  • In this paper, we propose a vision-based object detection and tracking system using online learning. The proposed system adopts a feature point-based method for tracking a series of inter-frame movement of a newly detected object, to estimate rapidly and toughness. At the same time, it trains the detector for the object being tracked online. Temporarily using the result of the failure detector to the object, it initializes the tracker back tracks to enable the robust tracking. In particular, it reduced the processing time by improving the method of updating the appearance models of the objects to increase the tracking performance of the system. Using a data set obtained in a variety of settings, we evaluate the performance of the proposed system in terms of processing time.