• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.31-38
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• 2016

In this paper, we propose multi-scale infrared target detection algorithm with varied filter size using integral image. Filter based target detection is widely used for small target detection, but it doesn't suit for large target detection depending on the filter size. When there are multi-scale targets on the sky background, detection filter with small filter size can not detect the whole shape of the large targe. In contrast, detection filter with large filter size doesn't suit for small target detection, but also it requires a large amount of processing time. The proposed algorithm integrates the filtering results of varied filter size for the detection of small and large targets. The proposed algorithm has good performance for both small and large target detection. Furthermore, the proposed algorithm requires a less processing time, since it use the integral image to make the mean images with different filter sizes for subtraction between the original image and the respective mean image. In addition, we propose the implementation of real-time embedded system using FPGA.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2016년도 추계학술발표대회
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• pp.50-53
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• 2016

Image processing techniques play an increasingly important role in many aspects of our daily life. For example, it has been shown to improve agricultural productivity in a number of ways such as plant pest detecting or fruit grading. However, massive quantities of images generated in real-time through multi-devices such as remote sensors during monitoring plant growth lead to the challenges of big data. Meanwhile, most current image processing systems are designed for small-scale and local computation, and they do not scale well to handle big data problems with their large requirements for computational resources and storage. In this paper, we have proposed an IPABigData (Image Processing Algorithm BigData) platform which provides algorithms to support large-scale image processing in agriculture based on Hadoop framework. Hadoop provides a parallel computation model MapReduce and Hadoop distributed file system (HDFS) module. It can also handle parallel pipelines, which are frequently used in image processing. In our experiment, we show that our platform outperforms traditional system in a scenario of image segmentation.

• 한국군사과학기술학회지
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• 제25권4호
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• pp.372-380
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• 2022

This study proposes a distributed parallel processing system, called the Fast Analysis System for remote sensing daTa(FAST), for large-scale satellite image processing and analysis. FAST is a system that designs jobs in vertices and sequences, and distributes and processes them simultaneously. FAST manages data based on the Hadoop Distributed File System, controls entire jobs based on Apache Spark, and performs tasks in parallel in multiple slave nodes based on a docker container design. FAST enables the high-performance processing of progressively accumulated large-volume satellite images. Because the unit task is performed based on Docker, it is possible to reuse existing source codes for designing and implementing unit tasks. Additionally, the system is robust against software/hardware faults. To prove the capability of the proposed system, we performed an experiment to generate the original satellite images as ortho-images, which is a pre-processing step for all image analyses. In the experiment, when FAST was configured with eight slave nodes, it was found that the processing of a satellite image took less than 30 sec. Through these results, we proved the suitability and practical applicability of the FAST design.

• 한국소음진동공학회논문집
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• 제15권3호
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• pp.329-333
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• 2005

This paper is concerned with the measurement of low-frequency vibrations of structures using the image processing method. To measure the vibrations visually, the measurement system consists of a camera, an image grabber board, and a computer. The specific target installed on the structure is used to calculate the vibration of structure. The captured image is then converted into a pixel-based data and then analyzed numerically. The limitation of the system depends on the image capturing speed and the size of image. In this paper, we propose the methodology for the vibration measurement using the image processing method. The method enables us to measure the displacement directly without any contact. The current resolution of the vibration measurement is limited to sub centimeter scale. However, the frequency bandwidth and resolution can be enhanced by a high-speed and high-resolution image processing system.

• 대한한의진단학회지
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• 제17권3호
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• pp.233-240
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• 2013

Objectives The objective and universal grading system for the facial nerve palsy is needed to the objectification of treatment in Oriental medicine. In this study, the facial nerve palsy grading was developed with combination of image processing technique and Nottingham scale. Methods The developed system is composed of measurement part, image processing part, facial nerve palsy evaluation part, and display part. With the video data recorded by webcam at measurement part, the positions of marker were measured at image processing part. In evaluation part, Nottingham scales were calculated in four different facial expressions with measured marker position. The video of facial movement, time history of marker position, and Nottingham scale were displayed in display part. Results & Conclusion The developed system was applied to a normal subject and a abnormal subject with facial nerve palsy. The left-right difference of Nottingham scores was large in the abnormal compared with the normal. In normal case, the change of the length between supraorbital point and infraorbital point was larger than that of the length between lateral canthus and angle of mouth. The abnormal case showed an opposite result. The developed system showed the possibilities of the objective and universal grading system for the facial nerve palsy.

• 전기전자학회논문지
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• 제27권1호
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• pp.59-64
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• 2023

딥러닝은 다양한 컴퓨터 비전 문제를 해결할 수 있지만, 대량의 데이터셋이 필요하다. 본 논문에서는 대형 이미지 데이터셋을 구축하기 위해 이미지 이진화 기반 데이터 증강 기법을 제안한다. 이미지 이진화를 사용하여 특성을 추출하고 추출된 나머지 픽셀을 랜덤하게 배치하여 새로운 이미지를 생성한다. 생성된 이미지는 원본 이미지와 유사한 품질을 보여주며, 딥러닝 모델에서도 뛰어난 성능을 보였다.

• Journal of Information Processing Systems
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• 제13권5호
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• pp.1126-1134
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• 2017

A variety of medical service applications in the field of the Internet of Things (IoT) are being studied. Segmentation is important to identify meaningful regions in images and is also required in 3D images. Previous methods have been based on gray value and shape. The Visible Korean dataset consists of serially sectioned high-resolution color images. Unlike computed tomography or magnetic resonance images, automatic segmentation of color images is difficult because detecting an object's boundaries in colored images is very difficult compared to grayscale images. Therefore, skilled anatomists usually segment color images manually or semi-automatically. We present an out-of-core 3D segmentation method for large-scale image datasets. Our method can segment significant regions in the coronal and sagittal planes, as well as the axial plane, to produce a 3D image. Our system verifies the result interactively with a multi-planar reconstruction view and a 3D view. Our system can be used to train unskilled anatomists and medical students. It is also possible for a skilled anatomist to segment an image remotely since it is difficult to transfer such large amounts of data.

• 방송공학회논문지
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• 제23권5호
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• pp.606-613
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• 2018

본 연구는 대규모 영상처리를 위한 메모리 확장을 위한 외장 메모리 확장장치 구현에 관련된 내용으로, 이는 영상처리를 위한 그래픽 워크스테이션에 장착되는 PCI(Peripheral Component Interconnect) Express Gen3 x8 인터페이스를 가지는 외장 메모리 어댑터 카드와 외장 DDR(Dual Data Rate) 메모리로 구성된 외장 메모리 보드로 구성되며, 메모리 어댑터 카드와 외장 메모리 보드간의 연결은 광 인터페이스를 통하여 이루어진다. 외장 메모리 억세스를 위해서는 Programmable I/O 방식과 DMA(Direct Memory Access) 방식을 모두 사용할 수 있도록 하여 영상 데이터의 효율적 송수신이 이루어지도록 하였다. 본 연구 결과의 구현은 Altera Stratix V FPGA(Field Programmable Gate Array)와 40G 광 트랜시버가 장착된 보드를 사용하였으며, 1.6GB/s의 대역폭 성능을 보여주고 있다. 이는 4K UHD(Ultra High Definition) 영상 한 채널을 담당할 수 있는 규모이다. 향후 본 연구를 계속 진행하여 3GB/s 이상 대역폭을 보이는 연구결과를 보일 예정이다.

• 재활복지공학회논문지
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• 제9권2호
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• pp.129-135
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• 2015

본 연구에서는 3차원 영상처리와 노팅험 스케일을 이용하여 안면마비 평가 시스템을 개발하였다. 시스템은 측정부, 영상처리부, 연산부, 그리고 안면마비 평가 및 출력부로 구성되어 있다. 두 개의 웹캠을 사용하여 안면부의 8곳에 부착된 마커의 3차원 위치를 계산하였으며, 이를 이용하여 노팅험 스케일을 계산하고 화면에 보여준다. 피험자의 자세변화와 측정방식이 노팅험 스케일에 미치는 영향을 조사하였다. 측정방식은 2차원과 3차원을 비교하였으며, 피험자자세는 정면응시와 $11^{\circ}$ 측면응시를 비교하였다. 측면응시한 피험자를 2차원 방식으로 측정한 경우의 오차가 가장 컸다. 3차원 측정방식이 피험자의 자세변화에 따른 오차에 가장 덜 민감하였다.