• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.15-28
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• 2019

Artificial intelligence is evolving rapidly in the ICT field, smart convergence media system and content industry through the fourth industrial revolution, and it is evolving very rapidly through Big Data. In this paper, we propose a face recognition method based on object recognition based on object recognition through artificial intelligence. In this method, Were experimented and studied through the object recognition technique of artificial intelligence. In the conventional 3D image field, general research on object recognition has been carried out variously, and researches have been conducted on the side effects of visual fatigue and dizziness through 3D image. However, in this study, we tried to solve the problem caused by the quantitative difference between object recognition and object recognition for human factor algorithm that measure visual fatigue through cognitive function, morphological analysis and object recognition. Especially, The new method of computer interaction is presented and the results are shown through experiments.

• 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 Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.99-112
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• 2015

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.374-385
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• 2020

In this paper, we propose a preprocessing technique to solve the problems of action recognition with Equirectangular Projection (ERP) video. The preprocessing technique proposed in this paper assumes the person object as the subject of action, that is, the Object of Interest (OOI), and the surrounding area of the OOI as the ROI. The preprocessing technique consists of three modules. I) Recognize person object in the image with object recognition model. II) Create a saliency map from the input image. III) Select subject of action using recognized person object and saliency map. The subject boundary box of the selected action is input to the action recognition model in order to improve the action recognition performance. When comparing the performance of the proposed preprocessing method to the action recognition model and the performance of the original ERP image input method, the performance is improved up to 99.6%, and the action is obtained when only the OOI is detected. It can also see the effects of related video summaries.

• 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.

• Convergence Security Journal
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• v.22 no.5
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• pp.3-9
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• 2022

Recently, in order to build a cyber physical system(CPS) that is a technology related to the 4th industry, the construction of the virtual control system for physical model and control circuit simulation is increasingly required in various industries. It takes a lot of time and money to convert documents that are not electronically documented through direct input. For this, it is very important to digitize a large number of drawings that have already been printed through object recognition using artificial intelligence. In this paper, in order to accurately recognize objects in drawings and to utilize them in various applications, a recognition technique using artificial intelligence by analyzing the characteristics of objects in drawing was proposed. In order to improve the performance of object recognition, each object was recognized and then an intermediate file storing the information was created. And the recognition rate of the next recognition target was improved by deleting the recognition result from the drawing. In addition, the recognition result was stored as a standardized format document so that it could be utilized in various fields of the control system. The excellent performance of the technique proposed in this paper was confirmed through the experiments.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.546-557
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• 2022

Recently, the demand to interpret image data with artificial intelligence in various fields is rapidly increasing. Object recognition and detection techniques using deep learning are mainly used, and video integration analysis to determine unstructured object recognition is a particularly important problem. In the case of natural disasters or social disasters, there is a limit to the object recognition structure alone because it has an unstructured shape. In this paper, we propose intelligent video integration analysis system that can recognize unstructured objects based on video turning point and object detection. We also introduce a method to apply and evaluate object recognition using virtual augmented images from 2D to 3D through GAN.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.10
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• pp.194-202
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• 1999

A method for pattern recognition based on wavelet transform is proposed in this paper. The boundary of the object to be recognized includes shape information for object of machine parts. The contour is first represented using a one-dimensional signal and normalized about translation, rotation and scale, then is used to build the wavelet transform representation of the object. Wavelets allow us to decompose a function into multi-resolution hierarchy of localized frequency bands. The recognition of 2-dimensional object based on the wavelet is described to analyze the shape of analysis technique; the discrete wavelet transform(DWT). The feature vectors obtained using wavelet analysis is classified using a multi-layer neural network. The results show that, compared with the use of fourier descriptors, recognition using wavelet is more stable and efficient representation. And particularly the performance for objects corrupted with noise is better than that of other method.