• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1678-1680
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• 2002

Stereo matching technique is used in many practical fields like satellite image analysis and computer vision. In this paper, we suggest a method to extract a target object image from a complicated background. For example, human face image can be extracted from random background. This method can be applied to computer vision such as security system, dressing simulation by use of extracted human face, 3D modeling, and security system. Many researches about stereo matching have been performed. Conventional approaches can be categorized into area-based and feature-based method. In this paper, we start from area-based method and apply area tracking using scanning window. Coarse depth information is used for area merging process using area searching data. Finally, we produce a target object image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1083-1097
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• 2018

This paper presents a novel range-free localization algorithm based on quantum particle swarm optimization. The proposed algorithm is capable of estimating the distance between two non-neighboring sensors for multi-hop heterogeneous wireless sensor networks where all nodes' communication ranges are different. Firstly, we construct a new cumulative distribution function of expected hop progress for sensor nodes with different transmission capability. Then, the distance between any two nodes can be computed accurately and effectively by deriving the mathematical expectation of cumulative distribution function. Finally, quantum particle swarm optimization algorithm is used to improve the positioning accuracy. Simulation results show that the proposed algorithm is superior in the localization accuracy and efficiency when used in random and uniform placement of nodes for heterogeneous wireless sensor networks.

• 제어로봇시스템학회논문지
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• 제22권1호
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• pp.8-16
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• 2016

In the field of computer vision and robotics, bin picking is an important application area in which object pose estimation is necessary. Different approaches, such as 2D feature tracking and 3D surface reconstruction, have been introduced to estimate the object pose accurately. We propose a new approach where we can use both 2D image features and 3D surface information to identify the target object and estimate its pose accurately. First, we introduce a label detection technique using Maximally Stable Extremal Regions (MSERs) where the label detection results are used to identify the target objects separately. Then, the 2D image features on the detected label areas are utilized to generate 3D surface information. Finally, we calculate the 3D position and the orientation of the target objects using the information of the 3D surface.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.647-650
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• 1988

This paper proposes and sketches out a new parallel architecture of transputer-based pyramidal parallel array computer (TPPAC) used to process computationally intensive problems for geometric processing applications such as computer vision, image processing etc. It explores how efficiently the pyramid computer architecture is designed using transputer chips, and poses a new interconnection scheme for TPPAC without using additional transputers.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.89-94
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• 2022

The Saudi Vision 2030 defined the directions of the national economy and market towards diversifying sources of income, and developing energy to become less dependent on oil. The study sought through a theoretical review to identify the reality of the energy sector and the areas of investment available in the field of renewable energy. Findings showed that investment in the renewable energy sector is a promising source according to solar, wind, hydrogen, geothermal energy and burning waste than landfill to extract biogas for less emission. The renewable energy sector faces challenges related to technology, production cost, price, quantity of production and consumption, and markets. The study revealed some recommendations providing and suggested electronic marketing system to provide investors and consumers with energy available from renewable sources.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2010년도 한국컴퓨터종합학술대회논문집 Vol.37 No.1(C)
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• pp.442-446
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• 2010

본 논문에서는 거리변환 기반의 정밀한 fiducial 마크 정렬 알고리즘을 제안한다. 거리변환은 물체의 중심에 가중치를 가지는 특성이 있는데 이는 AOI 공정에서 에칭, 이동과 같은 다양한 요소들로부터 획득되는 타겟영상에 대하여 강인하게 물체의 중심으로 매칭할 수 있도록 한다. 제안한 방법은 우선 입력 타겟영상에 대하여 이진화를 진행하고, 다음 모델과 타겟영상에 대하여 거리변환을 이용하여 거리특징을 추출하고, 추출된 모델과 타겟영상에 대한 거리특징을 NCC(Normalized Cross Correlation)를 이용하여 매칭한 후, 매칭 스코어에 대하여 Sub-pixel 분석을 진행하여 sub-pixel 레벨의 정확도를 가지도록 한다. 실험결과로부터 제안한 거리특징을 이용한 매칭 알고리즘이 기존의 픽셀 밝기 값을 이용한 매칭보다 강인하고 정확하게 매칭됨을 확인할 수 있었다.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 2000년도 THE THIRD INTERNATIONAL CONFERENCE ON AGRICULTURAL MACHINERY ENGINEERING. V.II
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• pp.300-306
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• 2000

This paper introduces a new concept of automation for bio-production with tele-operative system. The proposed system showed practical and feasible way of automation for the volatile bio-production process. Based on the proposition, recognition of the job environment with object identification was performed using computer vision system. A man-machine interactive hybrid decision-making, which utilized a concept of tele-operation was proposed to overcome limitations of the capability of computer in image processing and feature extraction from the complex environment image. Identifying watermelons from the outdoor scene of the cultivation field was selected to realize the proposed concept. Identifying watermelon from the camera image of the outdoor cultivation field is very difficult because of the ambiguity among stems, leaves, shades, and especially fruits covered partly by leaves or stems. The analog signal of the outdoor image was captured and transmitted wireless to the host computer by R.F module. The localized window was formed from the outdoor image by pointing to the touch screen. And then a sequence of algorithms to identify the location and size of the watermelon was performed with the local window image. The effect of the light reflectance of fruits, stems, ground, and leaves were also investigated.

• 방송공학회논문지
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• 제26권6호
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• pp.738-747
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• 2021

오늘날 딥러닝 기술의 향상으로 영상 분류, 객체 탐지, 객체 분할, 객체 추적 등 컴퓨터 비전 분야 또한 큰 발전을 이루고 있다. 지능적 감시, 로봇, 사물 인터넷, 자율주행 자동차 등 딥러닝 기술이 결합된 다양한 응용 기술들은 실제 산업에 적용되고 있으며, 이에 따라 사람의 소비를 위한 영상 데이터 뿐만 아니라 머신 비전을 위한 영상 데이터의 효율적인 압축 방식에 대한 필요성이 대두되고 있다. 본 논문에서는 머신 비전을 위한 열 적외선 영상의 객체 기반 압축 기법을 제안한다. 효율적인 영상 압축과 신경망의 좋은 성능을 유지하기 위해 본 논문에서는 신경망의 객체 탐지 결과와 객체 크기에 따라 입력 영상을 객체 부분과 배경 부분으로 나누어 서로 다른 압축률로 부호화를 수행하는 방법을 제안한다. 제안하는 방법은 VVC로 영상 전체를 압축하는 방식보다 BD-rate 값이 최대 -19.83%로 압축 효율이 뛰어나다는 것을 확인할 수 있다.

• Journal of Animal Science and Technology
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• 제66권1호
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• pp.31-56
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• 2024

Pig farming, a vital industry, necessitates proactive measures for early disease detection and crush symptom monitoring to ensure optimum pig health and safety. This review explores advanced thermal sensing technologies and computer vision-based thermal imaging techniques employed for pig disease and piglet crush symptom monitoring on pig farms. Infrared thermography (IRT) is a non-invasive and efficient technology for measuring pig body temperature, providing advantages such as non-destructive, long-distance, and high-sensitivity measurements. Unlike traditional methods, IRT offers a quick and labor-saving approach to acquiring physiological data impacted by environmental temperature, crucial for understanding pig body physiology and metabolism. IRT aids in early disease detection, respiratory health monitoring, and evaluating vaccination effectiveness. Challenges include body surface emissivity variations affecting measurement accuracy. Thermal imaging and deep learning algorithms are used for pig behavior recognition, with the dorsal plane effective for stress detection. Remote health monitoring through thermal imaging, deep learning, and wearable devices facilitates non-invasive assessment of pig health, minimizing medication use. Integration of advanced sensors, thermal imaging, and deep learning shows potential for disease detection and improvement in pig farming, but challenges and ethical considerations must be addressed for successful implementation. This review summarizes the state-of-the-art technologies used in the pig farming industry, including computer vision algorithms such as object detection, image segmentation, and deep learning techniques. It also discusses the benefits and limitations of IRT technology, providing an overview of the current research field. This study provides valuable insights for researchers and farmers regarding IRT application in pig production, highlighting notable approaches and the latest research findings in this field.

• 인지과학
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• 제1권2호
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• pp.293-316
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• 1989