• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.142-150
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• 2018

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the edge orientation histogram and principle component analysis. By using the detected object region as a recognition input image, in this paper the object recognition method combined with principle component analysis and the multi-layer network which is one of the intelligent classification was suggested and its performance was evaluated. As a pre-processing algorithm of input object image, this method computes the eigenspace through principle component analysis and expresses the training images with it as a fundamental vector. Each image takes the set of weights for the fundamental vector as a feature vector and it reduces the dimension of image at the same time, and then the object recognition is performed by inputting the multi-layer neural network.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.223-228
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• 2009

We propose an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.901-908
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• 2013

The introduction of industrial intelligent robot using vision sensor has been interested in automated factory. 2D and 3D vision sensors have used to recognize object and to estimate object pose, which is for packaging parts onto a complete whole. But it is not trivial task due to illumination and various types of objects. Object image has distorted due to illumination that has caused low reliability in recognition. In this paper, recognition method of complex shape object has been proposed. An accurate object region has detected from combined binary image, which has achieved using DoG filter and local adaptive binarization. The object has recognized using neural network, which is trained with sub-divided object class according to object type and rotation angle. Predefined shape model of object and maximal slope have used to estimate the pose of object. The performance has evaluated on ETRI database and recognition rate of 96% has obtained.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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• pp.110-116
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• 2007

This paper presents a new technique to implement the real-time recognition for shapes and model number of parts based on an active vision approach. The main focus of this paper is to apply a technique of 3D object recognition for non-contacting inspection of the shape and the external form state of precision parts based on the pattern recognition. In the field of computer vision, there have been many kinds of object recognition approaches. And most of these approaches focus on a method of recognition using a given input image (passive vision). It is, however, hard to recognize an object from model objects that have similar aspects each other. Recently, it has been perceived that an active vision is one of hopeful approaches to realize a robust object recognition system. The performance is illustrated by experiment for several parts and models.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.348-350
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• 2021

The core technology of augmented reality is object recognition technology. Recently, due to the development of various artificial intelligence algorithms such as CNN, it has become possible to effectively distinguish specific objects from images. It is possible to realize more realistic and immersive augmented reality contents only when technology for recognizing objects quickly and accurately is secured. In this study, an object recognition model using SSD (single shot multibox detector) and an object recognition model using YOLO were compared and evaluated.

• International Journal of Advanced Culture Technology
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• v.4 no.4
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• pp.75-80
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• 2016

The process of extracting external boundary of an object is a very important process for recognizing an object in the image. The proposed extraction method consists of two processes: External Boundary Extraction and Thinning. In the first step, external boundary extraction process separates the region representing the object in the input image. Then, only the pixels adjacent to the background are selected among the pixels constituting the object to construct an outline of the object. The second step, thinning process, simplifies the outline of an object by eliminating unnecessary pixels by examining positions and interconnection relations between the pixels constituting the outline of the object obtained in the previous extraction process. As a result, the simplified external boundary of object results in a higher recognition rate in the next step, the object recognition process.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.219-222
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• 2009

Rapid object recognition is one of the main stream research themes focusing to reveal how human recognizes object and interacts with environment in natural world. This field of study is of consequence in that it is highly important in evolutionary perspective to quickly see the external objects and judge their characteristics to plan future reactions. In this study, we investigated how human detect natural scene objects and categorize them in a limited time frame. We applied Magnetoencepahlogram (MEG) while participants were performing detection (e.g. object vs. texture) or basic-level categorization (e.g. cars vs. dogs) tasks to track the dynamic interaction in human brain for rapid object recognition process. The results revealed that detection and categorization involves different temporal and functional connections that correlated for the successful recognition process as a whole. These results imply that dynamics in the brain are important for our interaction with environment. The implication from this study can be further extended to investigate the effect of subconscious emotional factors on the dynamics of brain interactions during the rapid recognition process.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.62-67
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• 2005

Object recognition in the field of robotics generally has depended on a computer vision system. Recently, RFID(Radio Frequency IDentification) technology has been suggested to support recognition and has been rapidly and widely applied. This paper introduces the more advanced RFID-based recognition. A novel tag named 3D tag, which facilitates the understanding of the object, was designed. The previous RFID-based system only detects the existence of the object, and therefore, the system should find the object and had to carry out a complex process such as pattern match to identify the object. 3D tag, however, not only detects the existence of the object as well as other tags, but also estimates the orientation and position of the object. These characteristics of 3D tag allows the robot to considerably reduce its dependence on other sensors required for object recognition the object. In this paper, we analyze the 3D tag's detection characteristic and the position and orientation estimation algorithm of the 3D tag-based RFID system.