• Journal of Platform Technology
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• v.11 no.3
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• pp.56-67
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• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.571-585
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• 2021

Applications for object detection are expanding as it is automated through artificial intelligence-based processing, such as deep learning, on a large volume of images and videos. High dependence on training data and a non-transparent way to find answers are the common characteristics of deep learning. Attacks on training data and training models have emerged, which are closely related to the nature of deep learning. Privacy, integrity, and robustness for the extracted information are important security issues because deep learning enables object recognition in images and videos. This paper summarizes the security issues that need to be addressed for future applications and analyzes the state-of-the-art security studies related to robustness, privacy, and integrity of object detection for images and videos.

• Journal of KIISE
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• v.44 no.3
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• pp.287-294
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• 2017

With the personal-media development that has emerged through various means such as UCC and SNS, many media studies have been completed for the purposes of analysis and recognition, thereby improving the object-recognition level. The focus of these studies is a classification of media that is based on a recognition of the corresponding objects, rather than the use of the title, tag, and scripter information. The media-classification task, however, is intensive in terms of the consumption of time and energy because human experts need to model the underlying media ontology. This paper therefore proposes an automated approach for the modeling of the media-classification ontology schema; here, the OWL-DL Axiom that is based on the frequency of the recognized media-based objects is considered, and the automation of the ontology modeling is described. The authors conducted media-classification experiments across 15 YouTube-video categories, and the media-classification accuracy was measured through the application of the automated ontology-modeling approach. The promising experiment results show that 1500 actions were successfully classified from 15 media events with an 86 % accuracy.

• Journal of Drive and Control
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• v.18 no.4
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• pp.9-18
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• 2021

This study proposes a method for creating learning datasets to recognize obstacles using deep learning algorithms in automated construction machinery or an autonomous vehicle. Recently, many researchers and engineers have developed various recognition algorithms based on deep learning following an increase in computing power. In particular, the image classification technology and image segmentation technology represent deep learning recognition algorithms. They are used to identify obstacles that interfere with the driving situation of an autonomous vehicle. Therefore, various organizations and companies have started distributing open datasets, but there is a remote possibility that they will perfectly match the user's desired environment. In this study, we created an interface of the virtual simulator such that users can easily create their desired training dataset. In addition, the customized dataset was further advanced by using the RDBMS system, and the recognition rate was improved.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.986-1005
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• 2020

In this paper, we propose AnoVid, an automated video annotation tool based on deep neural networks, that automatically generates various meta data for each scene or shot in a long drama video containing rich elements. To this end, a novel meta data schema for drama video is designed. Based on this schema, the AnoVid video annotation tool has a total of six deep neural network models for object detection, place recognition, time zone recognition, person recognition, activity detection, and description generation. Using these models, the AnoVid can generate rich video annotation data. In addition, AnoVid provides not only the ability to automatically generate a JSON-type video annotation data file, but also provides various visualization facilities to check the video content analysis results. Through experiments using a real drama video, "Misaeing", we show the practical effectiveness and performance of the proposed video annotation tool, AnoVid.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.13-20
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• 2024

This paper introduces a method for generating model images that can identify specific cylindrical medicine containers in videos and investigates data collection techniques. Previous research had separated object detection from specific object recognition, making it challenging to apply automated image stitching. A significant issue was that the coordinate-based object detection method included extraneous information from outside the object area during the image stitching process. To overcome these challenges, this study applies the newly released YOLOv8 (You Only Look Once) segmentation technique to vertically rotating pill bottles video and employs the ORB (Oriented FAST and Rotated BRIEF) feature matching algorithm to automate model image generation. The research findings demonstrate that applying segmentation techniques improves recognition accuracy when identifying specific pill bottles. The model images created with the feature matching algorithm could accurately identify the specific pill bottles.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.670-674
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• 1996

In automated search of a design database to support mechanical design, it is necessaryto recognize a shape pattern which represents a design object. This paper introduces the concept of a surface relation graph (SRG) for recognizing shape patterns from a 3D boundary representation scheme of a solid model(a B-rep model). In SRG, the nodes and arcs correspond to the faces and edges shared by two adjacent faces, respectively. An attribute assigned to an arc is given by an integer which discriminates the relationship between two adjacent faces. The + sign of the integer represents the geometric convexity of the solid, and the -sign the concivity at the shared edge. The input shape is recognized by comparison with the predefined features which are subgraphs of the SRG. A hierarchyof the database for upporting the design is presented. A search for the design database is also discussed. The usefulness of this method is illustrated by some application results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.176-185
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• 1996

Transformin the huge size of drawings into a suitable format for CAD system and recognizng the contents of drawings are the major concerans in the automated analysis of engineering drawings. This paper proposes some methods for text/graphics separation, symbol extraction, vectorization and symbol recognition with the object of applying them to electronic cirucit drawings. We use MBR (Minimum bounding rectangle) and size of isolated region on the drawings for separating text and graphic regions. Characteristics parameters such as the number of pixels, the length of circular constant and the degree of round shape are used for extracting loop symbols and geometric structures for non-loop symbols. To recognize symbols, nearest netighbor between FD (foruier descriptor) of extractd symbols and these of classification reference symbols is used. Experimental results show that the proposed method can generate compact vector representation of extracted symbols and perform the scale change and rotation of extracted symbol using symbol vectorization. Also we achieve an efficient searching of circuit drawings.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.4
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• pp.420-425
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• 2009

In this paper, image detection and recognition algorithms are studied with respect to embedded carrier system. There are many suggested techniques to detect and recognize objects. But they have the propensity to need much calculation for high hit rate. Advanced and modified method needs to study for embedded systems that low power consumption and real time response are requested. The proposed methods were implemented using Intel(R) Open Source Computer Vision Library provided by Intel Corporation. And they run and tested on embedded system using a ARM920T processor by cross-compiling. They showed 1.6sec response time and 95% hit rate and supported the automated moving carrier system smoothly.