• 산업경영시스템학회지
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• 제46권1호
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• pp.76-83
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• 2023

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

• Genomics & Informatics
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• 제17권2호
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• pp.19.1-19.10
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• 2019

In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.

• Genomics & Informatics
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• 제18권2호
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• pp.19.1-19.6
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• 2020

In semantic annotation, semantic concepts are linked to natural language. Semantic annotation helps in boosting the ability to search and access resources and can be used in information retrieval systems to augment the queries from the user. In the research described in this paper, we aimed to identify ontological concepts in scientific text contained in spreadsheets. We developed a tool that can handle various types of spreadsheets. Furthermore, we used the NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionality to cover spreadsheet data. Table2Annotation has strengths in certain criteria such as speed, error handling, and complex concept matching.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.85-95
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• 2023

In the process of creating drawings based on Building Information Modeling (BIM), automatically generated annotations can cause interference issues depending on the drawing type. This study aims to develop an algorithm for repositioning annotations using genetic algorithms to minimize such interferences. To achieve this, the Application Programming Interface (API) of BIM software was used to analyze data extractable from BIM drawing files. The process involved defining drawing data related to annotation repositioning, preprocessing this data, and deriving optimal placement coordinates for the annotations. Furthermore, applying the developed algorithm to the preliminary design drawings of small and medium-sized neighborhood facilities resulted in approximately a 95.37% decrease in annotation interference, indicating that the proposed algorithm can significantly enhance productivity in BIM-based drawing tasks.

• 방송공학회논문지
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• 제25권3호
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• pp.386-398
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• 2020

본 논문에서는 물체인식 딥러닝 모델을 구성하는데 필요한 데이터 레이블링 과정을 하나의 프로그램에서 사용할 수 있는 Annotation 툴을 개발했다. 프로그램의 인터페이스는 파이썬의 기본 GUI 라이브러리를 활용하였으며, 실시간으로 데이터 수집이 가능한 크롤러 기능을 구성하였다. 기존의 물체인식 딥러닝 모델인 Retinanet을 활용하여, 자동으로 Annotation 정보를 제공하는 기능을 구현했다. 또한, 다양한 물체인식 네트워크의 레이블링 형식에 맞추어 학습할 수 있도록 Pascal-VOC, YOLO, Retinanet 등 제각기 다른 학습 데이터 레이블링 형식을 저장하도록 했다. 제안하는 방식을 통해 국산 차량 이미지 데이터셋을 구축했으며, 기존의 물체인식 딥러닝 네트워크인 Retinanet과 YOLO 등에 학습하고, 정확도를 측정했다. 차량이 진입하는 영상에서 실시간으로 차량의 모델을 구별하는 정확성은 약 94%의 정확도를 기록했다.

• 스마트미디어저널
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• 제11권4호
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• pp.38-45
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• 2022

Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.488-492
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• 2006

일반적으로 종이 문서 환경에서 어노테이션은 중요한 부분에 밑줄을 긋거나, 별표와 같은 심볼을 그리거나 내용의 추가적인 설명을 위해 원본 문서에 코멘트 등을 추가하는 것을 의미한다. 책이나 문서의 중요한 부분에 입력된 부가정보를 어노테이션(Annotation)이라 한다. 본 연구에서는 기존의 전자문서 환경위주의 어노테이션 기술을 3차원 가상환경에 적용한 탐색항해 기법을 연구한다.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2010년도 춘계학술대회
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• pp.589-590
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• 2010

All these images consist a considerable database. What's more, the semantic meanings of images are well presented by the surrounding text and links. But only a small minority of these images have precise assigned keyphrases, and manually assigning keyphrases to existing images is very laborious. Therefore it is highly desirable to automate the keyphrases extraction process. In this paper, we first introduce WWW image annotation methods, based on low level features, page tags, overall word frequency and local word frequency. Then we put forward our method of multi-cues integration image annotation. Also, show multi-cue image annotation method is more superior than other method through an experiment.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.222-224
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• 2003

Many users want to add some their own information to data which was on the web and computer without actually needing to touch data. In remote sensing, the result data for image classification consist of image and text file in general. To overcome these inconvenience problems, we suggest the annotation method using XML language. We give the efficient annotation method which can be applied to web and viewing of image classification. We can apply the annotation for web and image classification with image and text file. The need for thesaurus construction is the lack of information for remote sensing and GIS on search engine like Empas, Naver and Google. In search engine, we can’t search the information for word which has many different names simultaneously. We select the remote sensing data from different sources and make the relation between many terms. For this process, we analyze the meaning for different terms which has similar meaning.

• 한국멀티미디어학회논문지
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• 제12권10호
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• pp.1466-1477
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• 2009

3D 가상공간은 도시 및 건축 설계 분야에 매우 적합한 활용 기술로 인식되어 다양한 연구가 시도 되고 있다. 그러나 도시 및 건축 설계의 과정이 다양한 관계자들의 협업에 의해 이루어지는 반면, 이를 지원하는 연구는 아직 미흡한 상황이다. 3D 가상공간이 협업을 지원하기 위해서는 무엇보다 가상공간 내에 의사소통을 지원해야 하며, 이를 위해서는 빠르고 직접적인 의사 표현 기술을 필요로 한다. 이러한 의사 표현 방법으로는 Sketch 기법이 효과적이며, Sketch의 부 정확한 선은 협업과정의 아이디어 단계에서 사고의 폭을 넓혀줄 수 있다는 점에서도 매우 효과적인 표현 방식이다. 그러나 이러한 전통적인 Sketch 기법만으로는 사실상 3D 가상공간에서 협업을 지원하기 어렵다. Sketch 기법을 협업에 적용한 기존의 연구들이 있지만 주로 3D 가상공간에 Sketch Annotation을 입력하는 것에만 중점을 두고, 입력된 Sketch가 담고 있는 내용을 잘 전달할 수 있는 지에 대해선 고려하지 못하였다. 따라서 평면이 아닌 객체에는 Sketch Annotation 입력이 불가능하거나, 입력되더라도 그 내용전달이 어려운 경우가 발생한다. 이 논문에서는 3차원 공간에서도 효과적으로 의사를 전달 할 수 있도록, 어떠한 형태의 면에 대해서도 Sketch Annotation의 내용이 정확히 전달될 수 있는 Sketch-Box를 이용한 Annotation기법을 연구한다.