• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.7
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• pp.956-962
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• 2018

As the study of the object feature detection from image, we explain methods to identify the species of the tree in forest using the picture taken from dron. Generally there are three kinds of methods, which are GLCM (Gray Level Co-occurrence Matrix) and Gabor filters, in order to extract the object features. We proposed the object extraction method using the statistical properties of trees in this research because of the similarity of the leaves. After we extract the sample images from the original images, we detect the objects using cross correlation techniques between the original image and sample images. Through this experiment, we realized the mean value and standard deviation of the sample images is very important factor to identify the object. The analysis of the color component of the RGB model and HSV model is also used to identify the object.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2017.11a
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• pp.327-328
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• 2017

본 논문에서는 사회재난 안전사고 중 수상 안전사고를 예방 및 사고 발생시 즉각 대응을 위한 센서 융복합 상황인지 기술을 개발하였다. 실제 현장에서의 위험상황을 전문가 컨설팅을 통하여 정의하였으며 이를 영상 분석을 이용한 객체의 검출 및 객체의 추적을 통한 위험상황 검출을 개발하였다. 기존 패턴인식 기술에 비하여 우수한 성능을 보이는 인공지능 기반 딥러닝 기술을 적용하였으며 딥러닝 기술을 적용하기 위하여는 많은 수의 데이터베이스 확보가 필수적이고 이를 위하여 기존 데이터베이스의 확보 및 현장에서의 실제 데이터베이스 구축을 위한 작업을 통하여 충분한 데이터베이스를 확보하였다. 객체 검출은 최적의 속도를 확보하기 위하여 SSD 구조를 이용하였으며 객체 추적을 위해서는 Re-identification 기법을 적용하여 Tied convolution 구조를 이용하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.5
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• pp.472-477
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• 2016

The most important and critical to the performance of video surveillance systems is to be detected exactly how much. In order to accurately track the object must be able to accurately separate the background and object. However, the system itself rather than the human vision exactly distinguish the object and the background, to assess the situation, it is not easy. If we can accurately detect the background and the object, to be able to accurately track an object, it is possible to increase the reliability of the system, have a significant impact on the success of the entire production system. In this paper, we propose a way to distinguish more precisely the background and the object being to determine the background environment changes more accurately.

• Journal of Digital Contents Society
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• v.8 no.3
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• pp.329-339
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• 2007

In this paper we present a facial region detection algorithm for real-time image with complex background and various illumination using spatial and temporal methods. For Detecting Human region It used summation of Edge-Difference Image between continuous image sequences. Then, Detected facial candidate region is vertically divided two objected. Non facial region is reduced using Analysis of Major Color Component. Non facial region has not available Major Color Component. And then, Background is reduced using boundary information. Finally, The Facial region is detected through horizontal, vertical projection of Images. The experiments show that the proposed algorithm can detect robustly facial region with complex background various illumination images.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.839-842
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• 2009

It is used to differential image for moving object detection in general. But it is difficult to detect the accurate detection which uses differential image between frames. In this paper, the proposed method overcome the noise that is generated by camera, grabber card, or weather condition. It extract to moving big object such as human or vehicle. The proposed method process morphological filtering and binary for the image with noise, reduce error. We are expect to apply to a real-time moving object detection system at fog condition, pass the limit of the object detection method using the differential image.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.531-534
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• 2012

본 논문에서는 Active Contour 기반의 영역 분할에서 이미지의 초점값을 이용하여 분할된 영역 사이의 경계를 생성하여 기존의 Active Contour에서 발생할 수 있는 중첩 객체의 동일 객체 인식을 방지하는 기법을 제안한다. Active Contour는 영상에서 객체의 윤곽을 검출하여 윤곽을 기준으로 영상을 분할하지만 중첩되거나 근접한 객체에서의 분할이 정확하게 이루어지지 않아 동일 객체로 인식하는 단점이 있다. 이러한 객체에서의 분할을 위해 영상의 초점값을 이용하여 영상 내에 존재하는 객체의 유사 경계 영역을 생성하고 Active Contour의 결과에 적용하여 경계를 생성한 뒤 초점값 적용으로 인해 생성될 수 있는 홀 영역을 hole filling 과정을 수행하여 보완함으로써 보다 정확한 객체를 추출하였다.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.239-244
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• 2006

In this paper, we present a snake-based scheme for contour tracking of objects in stereo image sequences. We address the problem by managing the insertion of new points and deletion of unnecessary points to better describe and track the object's boundary. In particular, our method uses more points in highly curved parts of the contour, and fewer points in less curved parts. The proposed algorithm can successfully define the contour of the object, and can track the contour in complex images. Furthermore, we tested our algorithm in the presence of partial object occlusion. Performance of the proposed algorithm has been verified by simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.683-685
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• 2018

본 논문은 도로에서의 객체탐지를 위한 딥러닝(deep learning) 데이터셋을 자동으로 생성, 분류하는 시스템을 제안한다. 시스템의 작동 과정은 크게 두 가지이다. 먼저 딥러닝을 활용하여 촬영된 영상에 존재하는 객체를 검출한다. 이때, 실시간으로 하는 방법과 레코딩된 영상을 다루는 방법 두 가지가 있다. 다음으로 검출된 객체 중 예측 값(scroe)가 임계치 이상인 객체의 위치와 종류를 파일로 저장한다. 이 시스템은 차량 전방 카메라 위치에 장착된 웹캠을 이용해 영상을 취득하고 임베디드 보드인 TX2 board를 이용해 데이터 셋을 생성한다. 매트랩의 image labeler app과 비교를 통해 보다 적은 시간비용으로 데이터셋을 생성해 냄을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.851-859
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• 2011

In this paper, we propose the method that automatically extracts the silhouette and the joints of consecutive input image, and track joints to trace object for interaction between human and computer. Also the proposed method presents the action of human being to map human body using joints. To implement the algorithm, we model human body using 14 joints to refer to body size. The proposed method converts RGB color image acquired through a single camera to hue, saturation, value images and extracts body's silhouette using the difference between the background and input. Then we automatically extracts joints using the corner points of the extracted silhouette and the data of body's model. The motion of object is tracted by applying block-matching method to areas around joints among all image and the human's motion is mapped using positions of joints. The proposed method is applied to the test videos and the result shows that the proposed method automatically extracts joints and effectively maps human body by the detected joints. Also the human's action is aptly expressed to reflect locations of the joints

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.