• 한국멀티미디어학회논문지
• /
• 제21권10호
• /
• pp.1150-1161
• /
• 2018

Classification is widely used in medical images to categorize patients and non-patients. However, conventional classification requires a complex procedure, including some rigid steps such as pre-processing, segmentation, feature extraction, detection, and classification. In this paper, we propose a novel convolutional neural network (CNN), called LeukemiaNet, to specifically classify two different types of leukemia, including acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), and non-cancerous patients. To extend the limited dataset, a PCA color augmentation process is utilized before images are input into the LeukemiaNet. This augmentation method enhances the accuracy of our proposed CNN architecture from 96.9% to 97.2% for distinguishing ALL, AML, and normal cell images.

• 대한기계학회논문집A
• /
• 제21권10호
• /
• pp.1551-1560
• /
• 1997

This study is devoted to the detection of the 3-dimensional point obstacles on the plane by using accumulated scan line images. The proposed accumulating only one scan line allow to process image at real time. And the change of motion of the feature in image is small because of the short time between image frames, so it does not take much time to track features. To obtain recursive optimal obstacles position and robot motion along to the motion of camera, Kalman filter algorithm is used. After using Kalman filter in case of the fixed environment, 3-dimensional obstacles point map is obtained. The position and motion of moving obstacles can also be obtained by pre-segmentation. Finally, to solve the stereo ambiguity problem from multiple matches, the camera motion is actively used to discard mis-matched features. To get relative distance of obstacles from camera, parallel stereo camera setup is used. In order to evaluate the proposed algorithm, experiments are carried out by a small test vehicle.

• 한국멀티미디어학회논문지
• /
• 제8권9호
• /
• pp.1239-1247
• /
• 2005

볼륨 렌더링에서는 기본적으로 전이함수에 의해 객체를 구분하여 표시한다. 그러나, 경우에 따라서는 전이 함수만으로는 객체를 구분할 수 없는 경우가 있다. 이 때는 객체를 다른 방법으로 미리 분할한 후 분할 정보를 바탕으로 가시화를 수행한다. 본 논문은 분할된 볼륨의 가시화에서 객체당 투명도를 부여하는 방법을 제시한다. 반투명한 렌더링은 관찰의 대상이 되는 객체에 대한 맥락정보를 효과적으로 전달되기 위해 사용된다. 객체당 투명도는 전이함수의 조절에 의해 객체를 반투명하게 조절하기 어려운 경우에 매우 효과적인 가시화 도구로 사용될 수 있다. 객체당 투명도의 의미에 대한 몇가지 해석과, 그에 따른 알고리즘의 변화를 제시하였다. 또한, 제안된 방법을 일반 그래픽스 하드웨어를 사용하여 구현함으로써, 대화식 사용이 가능한 효율적 구현이 가능함을 보였다.

• 한국멀티미디어학회논문지
• /
• 제20권9호
• /
• pp.1491-1501
• /
• 2017

In this paper, we propose a method to measure respiration rate by dividing the respiration related region in depth image using level set method. In the conventional method, the respiration related region was separated using the pre-defined region designated by the user. We separate the respiration related region using level set method combining shape prior knowledge. Median filter and clipping are performed as a preprocessing method for noise reduction in the depth image. As a feasibility test, respiration activity was recorded using depth camera in various environments with arm movements or body movements during breathing. Respiration activity was also measured simultaneously using a chest belt to verify the accuracy of calculated respiration rate. Experimental results show that our proposed method shows good performance for respiration rate estimation in various situation compared with the conventional method.

• 한국위성정보통신학회논문지
• /
• 제11권2호
• /
• pp.14-17
• /
• 2016

본 논문은 시각 장애인들을 위해 영상처리 기반의 숫자-자동 점자 변환기의 설계 및 구현에 관한 내용을 기술한다. 영상처리 기반의 숫자-점자 변환 알고리즘은 카메라로 획득한 입력 영상을 이진 영상화 한 다음, 문자 영역을 팽창과 라벨링 연산을 수행하고 저장되어 있는 문자 패턴 영상과 상호 상관도를 계산하여 해당되는 점자로 변환한다. 컴퓨터 시뮬레이션을 통하여 제안한 알고리즘을 모의실험한 결과, A4 용지에 인쇄된 숫자(0-9)에 대하여 91.8% 변환 성공률을 보여 주었고, DSP 영상처리 보드에 구현한 시제품 시험을 통하여 90% 변환 성능을 확인함으로서 구현된 숫자-자동 점자 변환기의 실용화 가능성을 확인하였다.

• 스마트미디어저널
• /
• 제7권2호
• /
• pp.23-33
• /
• 2018

A musical sheet is read by optical music recognition (OMR) systems that automatically recognize and reconstruct the read data to convert them into a machine-readable format such as XML so that the music can be played. This process, however, is very challenging due to the large variety of musical styles, symbol notation, and other distortions. In this paper, we present a model for the recognition of musical symbols through the use of a mobile application, whereby a camera is used to capture the input image; therefore, additional difficulties arise due to variations of the illumination and distortions. For our proposed model, we first generate a line adjacency graph (LAG) to remove the staff lines and to perform primitive detection. After symbol segmentation using the primitive information, we use a covariance-matching method to estimate the similarity between every symbol and pre-defined templates. This method generates the three hypotheses with the highest scores for likelihood measurement. We also add a global consistency (time measurements) to verify the three hypotheses in accordance with the structure of the musical sheets; one of the three hypotheses is chosen through a final decision. The results of the experiment show that our proposed method leads to promising results.

• 한국통신학회논문지
• /
• 제27권8A호
• /
• pp.796-804
• /
• 2002

본 논문에서는 방향성필터뱅크(Directional Filter Bank; DFB)에 기반하여 지문영상을 해석하여 처리하는 새로운 방법을 제안한다. 지문에서 융선의 방향성는 방향성지도를 구성하고, 이로부터 방향성 필터링을 통한 영상 향상을 행하는 등 지문 영상을 해석하는 전처리과정에서 매우 중요한 요소이다. DFB는 입력영상을 방향성대역영상으로 분해한 다음 이로부터 원영상을 완전하게 복원하는 필터이다. 본 논문에서는 DFB를 이용하여 지문영상을 여러 개의 방향성대역영상으로 분해하고, 이로부터 국부 방향성 에너지를 정의한 다음, 이를 기반으로 방향성지도의 제작, 영상분할, 특이점추출 및 영상개선의 처리과정들을 일관되게 수행하는 알고리듬을 제안한다.

• 대한원격탐사학회지
• /
• 제10권2호
• /
• pp.133-160
• /
• 1994

In this paper, we propose a comprehensive geometric correction algorithm of Along Track Scanning Radiometer(ATSR) images. The procedure consists of two cascaded modules; precorrection and fine co-location. The pre-correction algorithm is based on the navigation model which was derived in mathematical forms. This model was applied for correction raw(un-geolocated) ATSR images. The non-systematic geometric errors are also introduced as the limitation of the geometric correction by this analytical method. A fast and automatic algorithm is also presented in the paper for co-locating nadir and forward views of the ATSR images by using a binary cross-correlation matching technique. It removes small non-systematic errors which cannot be corrected by the analytic method. The proposed algorithm does not require any auxiliary informations, or a priori processing and avoiding the imperfect co-registratio problem observed with multiple channels. Coastlines in images are detected by a ragion segmentation and an automatic thresholding technique. The matching procedure is carried out with binaty coastline images (nadir and forward), and it gives comparable accuracy and faster processing than a patch based matching technique. This technique automatically reduces non-systematic errors between two views to .$\pm$ 1 pixel.

• International Journal of Internet, Broadcasting and Communication
• /
• 제15권1호
• /
• pp.85-96
• /
• 2023

The number of traffic accidents caused by wet or icy road surface conditions is on the rise every year. Car crashes in such bad road conditions can increase fatalities and serious injuries. Historical data (from the year 2016 to the year 2020) on weather-related traffic accidents show that the fatality rates are fairly high in Korea. This requires accurate prediction and identification of hazardous road conditions. In this study, a forecasting model is developed to predict the chances of traffic accidents that can occur on roads affected by weather and road surface conditions. Multiple deep learning algorithms taking into account AlexNet and 2D-CNN are employed. Data on orthophoto images, automatic weather systems, automated synoptic observing systems, and road surfaces are used for training and testing purposes. The orthophotos images are pre-processed before using them as input data for the modeling process. The procedure involves image segmentation techniques as well as the Z-Curve index. Results indicate that there is an acceptable performance of prediction such as 65% for dry, 46% for moist, and 33% for wet road conditions. The overall accuracy of the model is 53%. The findings of the study may contribute to developing comprehensive measures for enhancing road safety.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2021년도 춘계학술발표대회
• /
• pp.454-456
• /
• 2021

본 논문은 웹의 발달로 인하여 의료 서비스들이 기존의 Client-Server 방식의 제품에서 Web 방식의 제품으로 변경되고 있는 현대 흐름에서 인공지능 어플리케이션 또한 Web 으로 서비스 하기 위한 방법과 구현된 요로결석 AI 어플리케이션에 대해 기술한다. 이를 구현하기 위해 Python 기반의 Flask 라는 마이크로 웹 프레임워크를 사용하여 DICOM 핸들링, Pre-Processing, Mask 를 생성하고 Predict 결과를 Model Serving 을 통하여 Urinary Stone Segmentation Model 이 서비스되는 인공지능 웹 어플리케이션 동작 방식과 수행 결과를 보인다.