• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.473-475
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• 2022

In this paper, as children's smartphone penetration and usage time increase, the need for educational application development is increasing.Therefore, in this paper, we propose an idea for the development of an application service that is optimized for children and designed to be easily used by children by applying image recognition technology. Using image recognition technology, we propose a service that helps children easily take pictures of objects with their smartphone's camera and easily identify appropriate search results for them. Through this, even in an environment where it is difficult to receive direct guidance from a teacher due to online classes, children can easily study on their own initiative or find a subject they want to learn more about and learn.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1117-1123
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• 2021

Object recognition is to identify the location and class of a specific object by analyzing the given image when a specific image is input. One of the fields in which object recognition technology is actively applied in recent years is autonomous vehicles, and this paper describes the trend of image-based object recognition artificial intelligence technology in autonomous vehicles. The image-based object detection algorithm has recently been narrowed down to two methods (a single-step detection method and a two-step detection method), and we will analyze and organize them around this. The advantages and disadvantages of the two detection methods are analyzed and presented, and the YOLO/SSD algorithm belonging to the single-step detection method and the R-CNN/Faster R-CNN algorithm belonging to the two-step detection method are analyzed and described. This will allow the algorithms suitable for each object recognition application required for autonomous driving to be selectively selected and R&D.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1799-1802
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• 2003

This paper has been studied a Adaptive feature-factors based fingerprints recognition in many biometrics. we study preprocessing and matching method of fingerprints image in various circumstances by using optical fingerprint input device. The Fingerprint Recognition Technology had many development until now. But, There is yet many point which the accuracy improves with operation speed in the side. First of all we study fingerprint classification to reduce existing preprocessing step and then extract a Feature-factors with direction information in fingerprint image. Also in the paper, we consider minimization of noise for effective fingerprint recognition system.

• International Journal of Advanced Culture Technology
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• v.6 no.3
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• pp.173-177
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• 2018

Masks are generated by adding different fonts of learning data characters in pixel unit, and pixel values belonging to each of the masks are divided into 3 groups. Using the directional expansion operators, we expand the text area of the test data character into 4 diagonal directions in order to create the boundary areas to distinguish it from the background area. A mask with a minimum average discordance is selected as the final recognition result by calculating the degree of discordance between the expanded test data and the masks. Image comparison using directional expansion operations more accurately recognizes test data through 4 subdivided recognition processes. It is also possible to expand the ranges of 3 groups of pixel values of masks more evenly such that new fonts can easily be added to the given learning data.

• International Journal of Advanced Culture Technology
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• v.9 no.2
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• pp.100-105
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• 2021

Contactless service is rapidly emerging as a new growth strategy due to consumers who are reluctant to the face-to-face situation in the global pandemic of coronavirus disease 2019 (COVID-19), and various technologies are being developed to support the fast-growing contactless service market. In particular, the restaurant industry is one of the most desperate industrial fields requiring technologies for contactless service, and the representative technical case should be a kiosk, which has the advantage of reducing labor costs for the restaurant owners and provides psychological relaxation and satisfaction to the customer. In this paper, we propose a solution to the restaurant's store operation through the unmanned kiosk using a state-of-the-art artificial intelligence (AI) technology of image recognition. Especially, for the products that do not have barcodes in bakeries, fresh foods (fruits, vegetables, etc.), and autonomous restaurants on highways, which cause increased labor costs and many hassles, our proposed system should be very useful. The proposed system recognizes products without barcodes on the ground of image-based AI algorithm technology and makes automatic payments. To test the proposed system feasibility, we established an AI vision system using a commercial camera and conducted an image recognition test by training object detection AI models using donut images. The proposed system has a self-learning system with mismatched information in operation. The self-learning AI technology allows us to upgrade the recognition performance continuously. We proposed a fully automated payment system with AI vision technology and showed system feasibility by the performance test. The system realizes contactless service for self-checkout in the restaurant business area and improves the cost-saving in managing human resources.

• Journal of Convergence for Information Technology
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• v.7 no.2
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• pp.77-82
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• 2017

In this paper, a new security algorithm to recognize the type of the vehicle with the vehicle image as a input image is suggested. The vehicle recognition security algorithm is composed of five core parts, such as the input image, background removal, edge areas extraction, pre-processing(binarization), and the vehicle recognition. Therefore, the final recognition rate of the security algorithm for vehicle type recognition can be affected by the function and efficiency of each step. After inputting image into a gray scale image and removing backgrounds, the binarization is performed by extracting only the edge region. After the pre-treatment process for making outlines clear, the type of vehicles is categorized into large vehicles, passenger cars and motorcycles through the ratio of height and width of the vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2379-2384
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• 2005

We propose a novel method for object recognition using the smart tag system with a stereo vision on a pan-tilt mechanism. We developed a smart tag which included IRED device. The smart tag is attached onto the object. We also developed a stereo vision system which pans and tilts for the object image to be the centered on each whole image view. A Stereo vision system on the pan-tilt mechanism can map the position of IRED to the robot coordinate system by using pan-tilt angles. And then, to map the size and pose of the object for the robot to coordinate the system, we used a simple model-based vision algorithm. To increase the possibility of tag-based object recognition, we implemented our approach by using as easy and simple techniques as possible.

• Korean Journal of Artificial Intelligence
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• v.5 no.1
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• pp.10-17
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• 2017

This study is the first attempt to raise face recognition ability through machine learning algorithm and apply to CRM's information gathering, analysis and application. In other words, through face recognition of VIP customer in distribution field, we can proceed more prompt and subdivided customized services. The interest in machine learning, which is used to implement artificial intelligence, has increased, and it has become an age to automate it by using machine learning beyond the way that a person directly models an object recognition process. Among them, Deep Learning is evaluated as an advanced technology that shows amazing performance in various fields, and is applied to various fields of image recognition. Face recognition, which is widely used in real life, has been developed to recognize criminals' faces and catch criminals. In this study, two image analysis models, TF-SLIM and Inception-V3, which are likely to be used for criminal face recognition, were selected, analyzed, and implemented. As an evaluation criterion, the image recognition model was evaluated based on the accuracy of the face recognition program which is already being commercialized. In this experiment, it was evaluated that the recognition accuracy was good when the accuracy of the image classification was more than 90%. A limit of our study which is a way to raise face recognition is left as a further research subjects.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.341-348
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• 2020

The ability to extract useful information from an image, such as the human eye, is an interface technology essential for AI computer implementation. The building recognition technology has a lower recognition rate than other image recognition technologies due to the various building shapes, the ambient noise images according to the season, and the distortion by angle and distance. The computer vision based building recognition algorithms presented so far has limitations in discernment and expandability due to manual definition of building characteristics. This paper introduces the deep learning CNN (Convolutional Neural Network) model, and proposes new method to improve the recognition rate even by changes of building images caused by season, illumination, angle and perspective. This paper introduces the partial images that characterize the building, such as windows or wall images, and executes the training with whole building images. Experimental results show that the building recognition rate is improved by about 14% compared to the general CNN model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.441-443
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• 2018

This paper deals with the analysis of leg motion using RGB image. We used RGB image as gait analysis element by using BMC(Background Model Challenge) method and by using combining object recognition segmentation algorithm and attitude detection algorithm. It is considered that gait analysis incorporating image can be used as a parameter for classification of gait pattern recognition and abnormal gait.