• Title/Summary/Keyword: 3D object recognition

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Object Recognition using 3D Depth Measurement System. (3차원 거리 측정 장치를 이용한 물체 인식)

  • Gim, Seong-Chan;Ko, Su-Hong;Kim, Hyong-Suk
    • Proceedings of the IEEK Conference
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    • 2006.06a
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    • pp.941-942
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    • 2006
  • A depth measurement system to recognize 3D shape of objects using single camera, line laser and a rotating mirror has been investigated. The camera and the light source are fixed, facing the rotating mirror. The laser light is reflected by the mirror and projected to the scene objects whose locations are to be determined. The camera detects the laser light location on object surfaces through the same mirror. The scan over the area to be measured is done by mirror rotation. The Segmentation process of object recognition is performed using the depth data of restored 3D data. The Object recognition domain can be reduced by separating area of interest objects from complex background.

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3-D Underwater Object Recognition Using Ultrasonic Sensor Fabricated with 1-3 type Piezoelectric Composites and Invariant moment (1-3형 복합압전체 초음파센서와 불변모멘트를 이용한 3차원 수중 물체인식)

  • Cho, Hyun-Chul
    • Proceedings of the KIEE Conference
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    • 2000.07d
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    • pp.2330-2332
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    • 2000
  • In this study, 3-D underwater object recognition using ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites and invariant moment vector and SOFM(Self Organizing Feature Map) neural networks are presented. The recognition rates for the training data and the testing data were 99% and 93%, respectively.

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Design of the 3D Object Recognition System with Hierarchical Feature Learning (계층적 특징 학습을 이용한 3차원 물체 인식 시스템의 설계)

  • Kim, Joohee;Kim, Dongha;Kim, Incheol
    • KIPS Transactions on Software and Data Engineering
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    • v.5 no.1
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    • pp.13-20
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    • 2016
  • In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

Segmentation and Classification of Range Data Using Phase Information of Gabor Fiter (Gabor 필터의 위상 정보를 이용한 거리 영상의 분할 및 분류)

  • 현기호;이광호;황병곤;조석제;하영호
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.27 no.8
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    • pp.1275-1283
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    • 1990
  • Perception of surfaces from range images plays a key role in 3-D object recognition. Recognition of 3-D objects from range images is performed by matching the perceived surface descriptions with stored object models. The first step of the 3-d object recognition from range images is image segmentation. In this paper, an approach for segmenting 3-D range images into symbolic surface descriptions using spatial Gabor filter is proposed. Since the phase of data has a lot of important information, the phase information with magnitude information can effectively segment the range imagery into regions satisfying a common homogeneity criterion. The phase and magnitude of Gabor filter can represent a unique featur vector at a point of range data. As a result, range images are trnasformed into feature vectors in 3-parameter representation. The methods not only to extract meaningful features but also to classify a patch information from range images is presented.

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ASM Algorithm Applid to Image Object spFACS Study on Face Recognition (영상객체 spFACS ASM 알고리즘을 적용한 얼굴인식에 관한 연구)

  • Choi, Byungkwan
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.12 no.4
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    • pp.1-12
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    • 2016
  • Digital imaging technology has developed into a state-of-the-art IT convergence, composite industry beyond the limits of the multimedia industry, especially in the field of smart object recognition, face - Application developed various techniques have been actively studied in conjunction with the phone. Recently, face recognition technology through the object recognition technology and evolved into intelligent video detection recognition technology, image recognition technology object detection recognition process applies to skills through is applied to the IP camera, the image object recognition technology with face recognition and active research have. In this paper, we first propose the necessary technical elements of the human factor technology trends and look at the human object recognition based spFACS (Smile Progress Facial Action Coding System) for detecting smiles study plan of the image recognition technology recognizes objects. Study scheme 1). ASM algorithm. By suggesting ways to effectively evaluate psychological research skills through the image object 2). By applying the result via the face recognition object to the tooth area it is detected in accordance with the recognized facial expression recognition of a person demonstrated the effect of extracting the feature points.

Vanishing point-based 3D object detection method for improving traffic object recognition accuracy

  • Jeong-In, Park
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.1
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    • pp.93-101
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    • 2023
  • In this paper, we propose a method of creating a 3D bounding box for an object using a vanishing point to increase the accuracy of object recognition in an image when recognizing an traffic object using a video camera. Recently, when vehicles captured by a traffic video camera is to be detected using artificial intelligence, this 3D bounding box generation algorithm is applied. The vertical vanishing point (VP1) and horizontal vanishing point (VP2) are derived by analyzing the camera installation angle and the direction of the image captured by the camera, and based on this, the moving object in the video subject to analysis is specified. If this algorithm is applied, it is easy to detect object information such as the location, type, and size of the detected object, and when applied to a moving type such as a car, it is tracked to determine the location, coordinates, movement speed, and direction of each object by tracking it. Able to know. As a result of application to actual roads, tracking improved by 10%, in particular, the recognition rate and tracking of shaded areas (extremely small vehicle parts hidden by large cars) improved by 100%, and traffic data analysis accuracy was improved.

The Object 3D Pose Recognition Using Stereo Camera (스테레오 카메라를 이용한 물체의 3D 포즈 인식)

  • Yoo, Sung-Hoon;Kang, Hyo-Seok;Cho, Young-Wan;Kim, Eun-Tai;Park, Mig-Non
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.1123-1124
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    • 2008
  • In this paper, we develop a program that recognition of the object 3D pose using stereo camera. In order to detect the object, this paper is applied to canny edge detection algorithm and also used stereo camera to get the 3D point about the object and applied to recognize the pose of the object using iterative closest point(ICP) algorithm.

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Cooperative recognition using multi-view images

  • Kojoh, Toshiyuki;Nagata, Tadashi;Zha, Hong-Bin
    • 제어로봇시스템학회:학술대회논문집
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    • 1993.10b
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    • pp.70-75
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    • 1993
  • We represent a method of 3-D object recognition using multi images in this paper. The recognition process is executed as follows. Object models as prior knowledgement are generated and stored on a computer. To extract features of a recognized object, three CCD cameras are set at vertices of a regular triangle and take images of an object to be recognized. By comparing extracted features with generated models, the object is recognized. In general, it is difficult to recognize 3-D objects because there are the following problems such as how to make the correspondence to both stereo images, generate and store an object model according to a recognition process, and effectively collate information gotten from input images. We resolve these problems using the method that the collation on the basis of features independent on the viewpoint, the generation of object models as enumerating some candidate models in an early recognition level, the execution a tight cooperative process among results gained by analyzing each image. We have made experiments based on real images in which polyhedral objects are used as objects to be recognized. Some of results reveal the usefulness of the proposed method.

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The 3-D Underwater Object Recognition Using Neural Networks and Ultrasonic Sensor Fabricated with 1-3 Type Piezoelectric Composites (1-3형 압전복합체로 제작한 초음파센서와 신경회로망을 이용한 3차원 수중 물체인식)

  • 조현철;이기성
    • The Transactions of the Korean Institute of Electrical Engineers C
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    • v.50 no.7
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    • pp.324-325
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    • 2001
  • In this study, the characteristics of ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites are investigated. The 3-D Underwater object recognition using the self-made ultrasonic sensor and SOFM neural network is presented. The ultrasonic sensor is satisfied with the required condition of commercial ultrasonic sensor in underwater. The 3-D underwater object recognition for the training data and the testing data are 100[100%], respectively. The experimental results have shown that the ultrasonic sensor fabricated with PZT-Polymer 1-3 type composites can be applied for sonar system.

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A New Matching Strategy for SNI-based 3-D Object Recognition (면 법선 영상 기반형 3차원 물체인식에서의 새로운 매칭 기법)

  • 박종훈;최종수
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.30B no.7
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    • pp.59-69
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    • 1993
  • In this paper, a new matching strategy for 3-D object recognition, based on the Surface Normal Images (SNIs), is proposed. The matching strategy using the similarity decision function [9,10] lost the efficiency and the reliability of matching, because all features of models within model base must be compared with the scene object features, and the weights of the attributes of features is given by heuristic manner. However, the proposed matching strategy can solve these problems by using a new approach. In the approach, by searching the model base, a model object whose features are fully matched with the features of sceme object is selected. In this paper, the model base is constructed for the total 26 objects, and systhetic and real range images are used in the test of the system operation. Experimental result is performed to show the possibility that this strategy can be effectively used for the SNI based recognition.

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