• 한국산학기술학회논문지
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• 제22권3호
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• pp.446-452
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• 2021

우리나라 생활수준의 향상과 더불어 식품소비의 양적인 요구가 충족되면서, 세분화된 식품의 기호 성향을 충족시킬 수 있는 닭고기 소비가 증가하고 있다. 2003년 3월 축산물 품질평가원에서 고시(농림부 고시 제2003-14호)한 닭 도체 품질판정세부기준은 닭 도체 부위별 이물질 부착, 피·멍의 크기 및 중량에 따라 품질 등급을 기준을 제시하였다. 그러나 현실적으로 검사관 개개인의 주관적인 평가 기준으로 적용된 고시로 수천 마리의 닭 도체 등급판정을 유지하기가 어려운 문제점을 가지고 있다. 본 논문에서는 닭 도체 품질 세부기준에 따라 닭 도체 부위 분할하기 위해 비접촉/비파괴방식인 컴퓨터 시각 기술 알고리즘을 제안한다. 제안된 알고리즘은 실시간으로 빠르게 움직이는 닭 도체를 부위 분할하기 위하여 조명 외란에 강인하도록 보정하는 과정과 닭 도체와 배경을 구분하기 위한 EM(Expectation maximization), Erosion 및 Labeling 알고리즘, 그리고 닭 도체의 기하학적 형태를 분석하여 부위별 특징점을 찾고 점들의 위치를 계산하여 부위를 분할 할 수 있는 알고리즘을 사용하였다. 총 78마리의 닭 도체 샘플에 대하여 제안한 영상처리 알고리즘을 적용한 결과 닭 도체 부위 분할 알고리즘이 효과적임을 알 수 있었다.

• 한국정밀공학회지
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• 제22권9호
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• pp.85-92
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• 2005

This paper describes the recognition method of small-obstacles using a camera for a mobile robot in indoor environment. The technique of image processing using a camera has been widely used for an automaton of industrial system, an inspection of inferior goods, a lookout of an invader, and a vision sensor of intelligent robot. Mobile robot could meet small-obstacles such as a small plastic bottle of about 0.5 l in quantity, a small box of $7{\times}7{\times}7cm^3$ in volume, and so on in its designated path, and could be disturbed by them in the locomotion of a mobile robot. So, it is necessary to research on the recognition of small-obstacles using a camera and program. In this paper, 2-D image processing algorism and method fur recognition of small-obstacles using a camera for a mobile robot in indoor environment was developed. The characteristic test of the developed program to confirm the recognition of small-obstacles was performed. It is shown that the developed program could judge the size and the position of small-obstacles accurately.

• Journal of Multimedia Information System
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• 제6권4호
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.

• Journal of Construction Engineering and Project Management
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• 제5권4호
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• pp.16-22
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. Several case studies were conducted to experimentally evaluate their impact on BIM-based energy analysis to calculate energy load. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• 응용통계연구
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• 제22권3호
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• pp.569-584
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• 2009

영상에서 에지검출은 영상처리시스템과 컴퓨터비전에서 매우 중요한 단계이다. 지금까지 형태학적 에지검출은 고정된 구조적 요소를 사용한 형태학적 연산 토대 하에서 수행되어왔다. 본 논문에서는 잡음영상에서 에지검출을 위해 영상의 다양한 형태에 맞춰 다이내믹하게 모양이 변하는 아메바라는 구조적 요소를 사용하고자 한다. 제안된 에지검출 방법의 성능을 시각적인 방법뿐만 아니라 객관적인 척도인 PFOM과 ROC 곡선을 사용하여 정성적, 정량적으로 모두 평가하였다. 영상 설험 결과 고정된 구조적 요소를 이용하는 기존의 방법보다 잡음에 덜 민감하였으며 미세한 에지까지도 검출하는 뛰어난 성능을 보여주었다.

• 국제초고층학회논문집
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• 제9권4호
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• pp.351-360
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• 2020

Monitoring civil structures periodically is necessary for ensuring the fitness of the structures. Cracks on inner and outer surfaces of the building plays a vital role in indicating the health of the building. Conventionally, human visual inspection techniques were carried up to human reachable altitudes. Monitoring of high rise infrastructures cannot be done using this primitive method. Also, there is a necessity for more accurate prediction of cracks on building surfaces for ensuring the health and safety of the building. The proposed research focused on developing an efficient crack classification model using Transfer Learning enabled EfficientNet (TL-EN) architecture. Though many other pre-trained models were available for crack classification, they rely on more number of training parameters for better accuracy. The TL-EN model attained an accuracy of 0.99 with less number of parameters on large dataset. A bench marked METU dataset with 40000 images were used to test and validate the proposed model. The surfaces of high rise buildings were investigated using vision enabled Unmanned Arial Vehicles (UAV). These UAV is fabricated with TL-EN model schema for capturing and analyzing the real time streaming video of building surfaces.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.213-220
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• 2023

Face recognition is a very important process in video monitoring and is a type of biometric technology. It is mainly used for identification and security purposes, such as ID cards, licenses, and passports. The recognition process has many variables and is complex, so development has been slow. In this paper, we proposed a face recognition method using CNN, which has been re-examined due to the recent development of computers and algorithms, and compared with the feature comparison method, which is an existing face recognition algorithm, to verify performance. The proposed face search method is divided into a face region extraction step and a learning step. For learning, face images were standardized to 50×50 pixels, and learning was conducted while minimizing unnecessary nodes. In this paper, convolution and polling-based techniques, which are one of the deep learning technologies, were used for learning, and 1,000 face images were randomly selected from among 7,000 images of Caltech, and as a result of inspection, the final recognition rate was 98%.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 춘계학술발표대회
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• pp.369-372
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• 2010

평판 디스플레이(flat panel display)의 크기가 커짐에 따라 다양한 기판을 이용한 제조 방법이 개발되고 있다. 디스플레이 제조 공정 중 기판의 결함을 찾아서 분류하는 검사 시스템은 최종 제품의 품질을 결정하는 매우 중요한 부분이다. 본 연구는 머신비전 기술을 이용하여 불투명하고 반사율이 높은 기판 표면의 결함을 찾아내고, 이 결함을 스크래치(scratch), 흑결함(dark defect), 백결함(white defect)으로 분류하는 장치를 구현하는데 목적이 있다. 이를 구현하기 위해 본 논문에서는 정밀 스테이지(stage)와 라인 카메라(line CCD camera)을 이용한 광학계를 활용하여 검사 시스템을 구현하였다. 구축된 시스템을 이용하여 취득한 이미지를 12 개의 영역으로 등분하여 각각의 국부 영역에 대한 문턱값 연산(thresholding)을 적용함으로써 조명의 불균일을 의한 검출 에러율을 획기적으로 낮추었다. 간단한 컴퓨터비전 알고리듬의 채용으로도 검사 시스템의 구현이 가능함을 보였다.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.532-536
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• 2015

This paper presents an automated computer vision-based system to update BIM data by leveraging multi-modal visual data collected from existing buildings under inspection. Currently, visual inspections are conducted for building envelopes or mechanical systems, and auditors analyze energy-related contextual information to examine if their performance is maintained as expected by the design. By translating 3D surface thermal profiles into energy performance metrics such as actual R-values at point-level and by mapping such properties to the associated BIM elements using XML Document Object Model (DOM), the proposed method shortens the energy performance modeling gap between the architectural information in the as-designed BIM and the as-is building condition, which improve the reliability of building energy analysis. The experimental results on existing buildings show that (1) the point-level thermography-based thermal resistance measurement can be automatically matched with the associated BIM elements; and (2) their corresponding thermal properties are automatically updated in gbXML schema. This paper provides practitioners with insight to uncover the fundamentals of how multi-modal visual data can be used to improve the accuracy of building energy modeling for retrofit analysis. Open research challenges and lessons learned from real-world case studies are discussed in detail.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권4호
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• pp.959-979
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• 2024

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADL-ARPDC approach classifies rice pests from rice plant images. Before processing, FFADL-ARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods.