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나인섭(InSeop Na),정창부(ChangBu Jeong),김형득(HyungDeuk Kim),김수형(SooHyung Kim) 한국멀티미디어학회 2007 한국멀티미디어학회 학술발표논문집 Vol.2007 No.1
최근 HCI(Human Computer Interaction)에 대한 관심이 커짐에 따라 키보드나 마우스와 같이 기존의 사용자인터페이스에 비해 보다 자연스럽고 편리한 사용자 인터페이스에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 YUV색상모델, 히스토그램 스트레칭을 이용한 이치화, 모폴로지 연산에 의한 손영역 추출과 원형도의 분산값을 이용한 손동작 구간 판별, 객체의 중심점을 이용한 손동작 경로법을 이용하여 손동작을 인식하고, 이를 웹 캠을 사용 동영상 플레이어 제어에 응용하였다.
DNA Fingerprint 영상의 이진화 및 인식에 관한 연구
나인섭(InSeop Na),김지수(JiSoo Kim),한태호(Taeho Han),김수형(SooHyung Kim) 한국멀티미디어학회 2007 한국멀티미디어학회 학술발표논문집 Vol.2007 No.1
RAPD, RFLP, AFLP, SSR 및 CAPs의 DNA fingerprint 영상에서 유전자들의 정보를 검출하는 것은 그 자체로도 상당한 가치가 있을 뿐만 아니라, 이 데이터를 분석하고 가공하면 생명공학에서 필요한 더 많은 생물학적인 정보를 얻어 낼 수 있다. 본 논문에서는 기존에 직접적인 실험을 통해 수작업으로 정보를 얻던 방법을 컴퓨터 영상처리 기술을 이용하여 DNA 마커정보를 획득하고자 했을 때, 전기영동의 특성상 발생하는 레인과 마커들의 왜곡현상을 정의하고, 해결책으로 Hough변환을 이용한 기울기 추정 및 교정방법과, 스미어(Smear)현상에 무관한 DNA 마커의 인식을 위해 레인(lane)별 이진화 방법을 제안하고 있다.
Lab Color Space based Rice Yield Prediction using Low Altitude UAV Field Image
( Md Nasim Reza ),( Inseop Na ),( Sunwook Baek ),( In Lee ),( Kyeonghwan Lee ) 한국농업기계학회 2017 한국농업기계학회 학술발표논문집 Vol.22 No.1
Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed L*a*b* color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to L*a*b* color space. All color information contain in both a* and b* layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.
Text Line Segmentation using AHTC and Watershed Algorithm for Handwritten Document Images
KangHan Oh,SooHyung Kim,InSeop Na,GwangBok Kim 한국콘텐츠학회(IJOC) 2014 International Journal of Contents Vol.10 No.3
Text line segmentation is a critical task in handwritten document recognition. In this paper, we propose a novel text-line segmentation method using baseline estimation and watershed. The baseline-detection algorithm estimates the baseline using Adaptive Head-Tail Connection(AHTC) on the document. Then, the watershed method segments the line region using the baseline-detection result. Finally, the text lines are separated by watershed result and a post-processing algorithm defines the lines more correctly. The scheme successfully segments text lines with 97% accuracy from the handwritten document images in the ICDAR database.
Local Similarity based Document Layout Analysis using Improved ARLSA
Gwangbok Kim,SooHyung Kim,InSeop Na 한국콘텐츠학회(IJOC) 2015 International Journal of Contents Vol.11 No.2
In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.
Text Line Segmentation using AHTC and Watershed Algorithm for Handwritten Document Images
Oh, KangHan,Kim, SooHyung,Na, InSeop,Kim, GwangBok The Korea Contents Association 2014 International Journal of Contents Vol.10 No.3
Text line segmentation is a critical task in handwritten document recognition. In this paper, we propose a novel text-line-segmentation method using baseline estimation and watershed. The baseline-detection algorithm estimates the baseline using Adaptive Head-Tail Connection (AHTC) on the document. Then, the watershed method segments the line region using the baseline-detection result. Finally, the text lines are separated by watershed result and a post-processing algorithm defines the lines more correctly. The scheme successfully segments text lines with 97% accuracy from the handwritten document images in the ICDAR database.
The Analysis of Rice Transplant Characteristics by using Low Altitude UAV Images
( Md Nasim Reza ),( Inseop Na ),( Sunwook Baek ),( Kyeonghwan Lee ) 한국농업기계학회 2016 한국농업기계학회 학술발표논문집 Vol.21 No.2
Manual field based monitoring is labor intensive, expansive and time consuming. To overcome this situation automatic monitoring of rice seedlings lane and growth can be done. So, we proposed an image processing technique to detect and count the rice plant lane using low altitude Unmanned Aerial vehicle (UAV) images. The main objective of the study is to make an image processing technique based on horizontal and vertical projection of low altitude RGB images obtained from UAV for automatic rice plant lane detection. The algorithm was developed as follows: the initial RGB images, convert the images to gray or binary, noise filter, vertical and horizontal projection, detection of plant lane and calculate the length width, removal of false lane, final result. An adaptive median filter was used to remove the noise and image projection method was applied to optimize the plant lane. The result of the image projection was used to detect the plant lanes in the field. The accuracy of the result was compared with the ground truth. Our method showed that it is efficient for detecting and counting the rice plant lanes. The proposed method have shown that it is able to detect and count lanes without any mechanical interferences and it may be used as an automated tool for different crops.
Bottle Label Segmentation Based on Multiple Gradient Information
Yanjuan Chen,Sangcheol Park,Inseop Na,Soohyung Kim,Myungeun Lee 한국콘텐츠학회(IJOC) 2011 International Journal of Contents Vol.7 No.4
In this paper, we propose a method to segment the bottle label in images taken by mobile phones using multi-gradient approaches. In order to segment the label region of interest-object, the saliency map method and Hough Transformation method are first applied to the original images to obtain the candidate region. The saliency map is used to detect the most salient area based on three kinds of features (color, orientation and illumination features). The Hough Transformation is a technique to isolated features of a particular shape within an image. Therefore, we utilize it to find the left and right border of the bottle. Next, we segment the label based on the gradient information obtained from the structure tensor method and edge method. The experimental results have shown that the proposed method is able to accurately segment the labels as the first step of product label recognition system.