• 한국컴퓨터정보학회논문지
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• 제17권10호
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• pp.43-53
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• 2012

본 논문에서는 화면상 약속된 동작을 찾고 추적하는 알고리즘을 이용한 사용자 인터페이스를 제안한다. 현재 frame과 복수의 이전 frame간의 차영상을 이용하여 움직임 영역을 검출하고 약속된 제스처를 취하는 영역을 제어대상으로 인식한다. 이를 통하여 사용자가 장갑을 사용한다던지, 인종, 피부색등에 구애받지 않고 손동작 영역을 검출해 낼 수 있다. 또한 기존 색체 분포 추적 알고리즘을 개량하여 유사한 배경을 가로지르는 경우의 무게중심 위치의 정확성을 높였다. 그 결과 기존 피부색 인식 방법에 비해 약속된 손동작 인식률의 향상이 있었으며 기존 색체 추적 알고리즘에 비교하여 추적 인식률 향상을 확인할 수 있었다.

• 한국컴퓨터정보학회논문지
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• 제22권12호
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• pp.63-68
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• 2017

In this paper, we propose a bounding box prediction algorithm using multiple probability maps to improve object detection result of object detector. Although the performance of object detectors has been significantly improved, it is still not perfect due to technical problems and lack of learning data. Therefore, we use the result correction method to obtain more accurate object detection results. In the proposed algorithm, the preprocessed bounding box created as a result of object detection by the object detector is clustered in various form, and a conditional probability is given to each cluster to make multiple probability map. Finally, multiple probability map create new bounding box of object using morphological elements. Experiment results show that the newly predicted bounding box reduces the error in ground truth more than 45% on average compared to the previous bounding box.

• 한국정보통신학회논문지
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• 제25권8호
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• pp.1117-1123
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• 2021

객체 인식이란 하나의 특정 이미지를 입력했을 때, 주어진 이미지를 분석하여 특정한 객체(object)의 위치(location)와 종류(class)를 파악하는 것이다. 최근 객체 인식 기술이 적극적으로 접목되는 분야 중 하나는 자율주행 차량이라 할 수 있고, 본 논문에서는 자율주행 차량에서 영상 기반의 객체 인식 인공지능 기술에 대해 기술한다. 영상 기반 객체 검출 알고리즘은 최근 두 가지 방법(단일 단계 검출 방법 및 두 단계 검출 방법)으로 좁혀지고 있는데, 이를 중심으로 분석 정리하고자 한다. 두 가지 검출 방법의 장단점을 분석 제시하고, 단일 단계 검출 방법에 속하는 YOLO/SSD 알고리즘과 두 단계 검출 방법에 속하는 R-CNN/Faster R-CNN 알고리즘에 대해 분석 기술한다. 이를 통해 자율주행에 필요한 각 객체 인식 응용에 적합한 알고리즘이 선별적으로 선택되어 연구개발 되어질 수 있기를 기대한다.

• 디지털산업정보학회논문지
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• 제11권3호
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• pp.99-112
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• 2015

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

• 대한전자공학회논문지SP
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• 제46권6호
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• pp.88-94
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• 2009

본 논문에서는 보안 및 감시 시스템 분야에 적용할 수 있는 실시간 객체 탐지 및 추적 알고리듬을 제안한다. 구현된 시스템은 객체 탐지 단계, 객체 추적 단계로 구성되었다. 객체탐지에서는 정화한 객체의 움직임 검출을 위한 향상된 검출 방법인 적응배경 차분법과 적응적 블록 기반 모델을 제안한다. 객체추적에서는 칼만 필터에 기반한 다중 물체 추적 시스템을 설계하였다. 실험결과 이동객체의 움직임을 추정할 수 있었고, 추적 과정에서도 다수의 객체를 잃어버리지 않고 정상적으로 추적할 수 있었다. 또한 원거리 탐지 및 추적에서 향상된 결과를 얻을 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권1호
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• pp.395-405
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• 2012

A joint template matching algorithm is proposed in this paper to reduce the high rate of miss-detection and false-alarm caused by the traditional template matching algorithm during the process of multi-object detection. The proposed algorithm can reduce the influence on each object by matching all objects together according to the correlation information among different objects. Moreover, the rate of miss-detection and false-alarm in the process of single-template matching is also reduced based on the algorithm. In this paper, firstly, joint template is created from the information of relative positions among different objects. Then, matching criterion according to normalized cross correlation is generated for multi-object matching. Finally, the proposed algorithm is applied to the detection of watermarks in bill. The experiments show that the proposed algorithm has lower miss-detection and false-alarm rate comparing to the traditional NCC algorithm during the process of multi-object detection.

• 한국멀티미디어학회논문지
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• 제21권11호
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• pp.1305-1316
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• 2018

Graphical user interface(GUI) has a very important role to interact with software users. However, designing and coding of GUI are tedious and pain taking processes. In many studies, the researchers are trying to convert GUI elements or widgets to code or describe formally their structures by help of domain knowledge of stochastic methods. In this paper, we propose the GUI elements detection approach based on object detection strategy using deep neural networks(DNN). Object detection with DNN is the approach that integrates localization and classification techniques. From the experimental result, if we selected the appropriate object detection model, the results can be used for automatic code generation from the sketch or capture images. The successful GUI elements detection can describe the objects as hierarchical structures of elements and transform their information to appropriate code by object description translator that will be studied at future.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제12권2호
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• pp.91-98
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• 2023

인간-객체 상호작용 탐지는 객체 탐지와 상호작용 인식을 함께 풀어야하는 분야로 탐지 모델의 학습을 위해서 많은 데이터를 필요로 한다. 현재 공개된 데이터셋은 규모가 부족하여 데이터 증강 기법에 대한 요구가 커지고 있으나, 대부분의 연구에서 기존의 객체 탐지, 이미지 분할분야에서 활용하는 증강 기법을 활용하고 있는 실정이다. 이에 본 연구에서는 인간-객체 상호작용 탐지 분야에서 활용하는 데이터셋의 특성을 파악하고, 이를 통해 인간-객체 상호작용 탐지 모델 성능 향상에 효과적인 데이터 증강 기법을 제안한다. 본 연구에서 제안한 증강 기법에 대한 검증을 위하여 실험 환경을 구축하고, 기존의 학습 모델에 적용하여 증강 기법을 적용할 경우에 탐지 모델의 성능 향상이 가능함을 확인하였다.

• Journal of Animal Science and Technology
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• 제66권4호
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• pp.846-858
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• 2024

In a duck cage, ducks are placed in various states. In particular, if a duck is overturned and falls or dies, it will adversely affect the growing environment. In order to prevent the foregoing, it was necessary to continuously manage the cage for duck growth. This study proposes a method using an object detection algorithm to improve the foregoing. Object detection refers to the work to perform classification and localization of all objects present in the image when an input image is given. To use an object detection algorithm in a duck cage, data to be used for learning should be made and the data should be augmented to secure enough data to learn from. In addition, the time required for object detection and the accuracy of object detection are important. The study collected, processed, and augmented image data for a total of two years in 2021 and 2022 from the duck cage. Based on the objects that must be detected, the data collected as such were divided at a ratio of 9 : 1, and learning and verification were performed. The final results were visually confirmed using images different from the images used for learning. The proposed method is expected to be used for minimizing human resources in the growing process in duck cages and making the duck cages into smart farms.

• 로봇학회논문지
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• 제7권2호
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• pp.142-149
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• 2012

The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.