• Journal of Space Technology and Applications
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• v.1 no.2
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• pp.268-281
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• 2021

Recently, the synthetic aperture radar (SAR) has been increasingly in demand due to its advantage of being able to observe desired points regardless of time and weather. To utilize SAR data, first of all, many pre-processing such as satellite orbit correction, radiometric calibration, multi-looking, and geocoding are required. For analysis of SAR imagery such as object detection, change detection, and DEM(Digital Elevation Model), additional processings are needed. These pre-processing and additional processes are very complex and require a lot of time and computational resources. In order to handle the SAR images easily, the institutions that use SAR images develop analysis tools and provide users. This paper introduces the function and characteristics of representative SAR imagery analysis tools.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.189-190
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• 2016

일반 영상의 영상확대를 위한 다양한 알고리즘이 존재한다. 하지만 적외선 열화상 영상의 경우 일반영상과 다른 특성을 가지고 있기 때문에 적외선 영상을 위한 영상 확대 알고리즘이 필요하다. 따라서 적외선 영상이 일반영상에 비해 디테일이 없다는 특성을 고려하여 복잡한 알고리즘을 적용시키기 보다는 ADRC 와 같은 단순한 분류 기법을 활용하여 LR-HR 패치를 분류하고 학습된 데이터를 이용하여 영상확대 알고리즘에 적용하였다. 알고리즘의 성능 향상을 위해 학습과정에 전처리 과정을 추가하여 합성과정에서 추가적인 연산량의 증가 없이 확대 영상의 선명도를 향상시키고자 하였다. 또한 확대된 적외선 영상이 동일 해상도의 가시광영상에 비해 선명도가 떨어진다는 점을 고려하여 확대된 적외선 영상에 가시광영상의 고주파 정보를 합성시켜 이전보다 영상의 선명도를 더 향상시키고자 하였다. 이와 같은 방법으로 영상 확대 알고리즘만 수행하였을 때 통상적인 영상확대 기법인 bi-cubic interpolation 기법보다 JNB 수치가 평균 0.0727 만큼 높은 결과를 확인할 수 있었고 가시광영상과 융합하였을 때 이전보다 평균 0.0742 만큼 더 선명해진 영상을 얻었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.75-81
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• 2012

Road identification is the important task for extracting a road region from the high-resolution satellite images, when the road candidates is extracted by the pre-processing tasks using a binarization, noise removal, and color processing. Therefore, we propose a noble approach for identifying a road using the orientation-selective spatial filters, which is motivated by a computational model of neuron cells found in the primary visual cortex. In our approach, after the neuron cell typed spatial filters is applied to the isolated connected-labeling road candidate regions, proposed method identifies the region of perceiving the strong orientation feature with the real road region. To evaluate the effectiveness of the proposed method, the accuracy&error ratio in the confusion matrix was measured from road candidates including road and non-road class. As a result, the proposed method shows the more than 92% accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.127-136
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• 2010

In multi-view video, illumination disharmony between neighboring views can occur on account of different location of each camera and imperfect camera calibration, and so on. Such discrepancy can be the cause of the performance decrease of multi-view video coding by mismatch of inter-view prediction which refer to the pictures obtained from the neighboring views at the same time. In this paper, we propose an efficient histogram-based prefiltering algorithm to compensate mismatches between the luminance and chrominance components in multi-view video for improving its coding efficiency. To compensate illumination variation efficiently, all camera frames of a multi-view sequence are adjusted to a predefined reference through the histogram matching. A Cosited filter that is used for chroma subsampling in many video encoding schemes is applied to each color component prior to histogram matching to improve its performance. The histogram matching is carried out in the RGB color space after color space converting from YCbCr color space. The effective color conversion skill that has respect to direction of edge and range of pixel value in an image is employed in the process. Experimental results show that the compression ratio for the proposed algorithm is improved comparing with other methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.192-198
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• 2010

With the popularization of the mobile phone with a built-in camera, there are a lot of effort to provide useful information to users by detecting and recognizing the text in the video which is captured by the camera in mobile phone, and there is a need to detect the text regions in such mobile phone video. In this paper, we propose a method to detect the text regions in the mobile phone video. We employ morphological operation as a preprocessing and obtain binarized image using modified k-means clustering. After that, candidate text regions are obtained by applying connected component analysis and general text characteristic analysis. In addition, we increase the precision of the text detection by examining the frequency of the candidate regions. Experimental results show that the proposed method detects the text regions in the mobile phone video with high precision and recall.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.1
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• pp.115-123
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• 2002

In this paper, we present two types of vision algorithm that mobile robot has CCD camera. for obstacle avoidance and path plan. One is simple algorithm that compare with grey level from input images. Also, The mobile robot depend on image processing and move command from PC host. we has been studied self controlled mobile robot system with CCD camera. This system consists of TMS320F240 digital signal processor, step motor, RF module and CCD camera. we used wireless RF module for movable command transmitting between robot and host PC. This robot go straight until 95 percent filled screen from input image. And the robot recognizes obstacle about 95 percent filled something, so it could avoid the obstacle and conclude new path plan. Another is complex algorithm that image preprocessing by edge detection, converting, thresholding and image processing by labeling, segmentation, pixel density calculation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.519-525
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• 2022

The face detection task of detecting a person's face in an image is used as a preprocess or core process in various image processing-based applications. The neural network models, which have recently been performing well with the development of deep learning, are dependent on 2D images, so if noise occurs in the image, such as poor camera quality or pool focus of the face, the face may not be detected properly. In this paper, we propose a face detection method that uses depth information together to reduce the dependence of 2D images. The proposed model was trained after generating and preprocessing depth information in advance using face detection dataset, and as a result, it was confirmed that the FRN model was 89.16%, which was about 1.2% better than the RetinaNet model, which showed 87.95%.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06a
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• pp.252-253
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• 2010

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.538-540
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• 2003

본 논문에서는 얼굴 영상의 변화를 보상할 수 있는 전처리 과정으로서 기하학적 특징에 기반한 순수 얼굴 영역 검출 기법을 도입하고 분류기로서 간단한 정합 기법을 사용한 얼굴 인식 기법을 제안한다. 실험결과를 보면 제안한 기법은 NNC, BPN, SVM 분류 기법에 비해 월등한 성능을 가진다.

• Analytical Science and Technology
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• v.14 no.6
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• pp.1109-1124
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• 2001