• 한국멀티미디어학회논문지
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• 제20권12호
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• pp.2009-2015
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• 2017

A two-dimensional lens is required to reproduce both the horizontal and vertical parallax through an autostereoscopic 3D display. Among the two-dimensional lenses, a hexagonal micro lens array (MLA) having good optical efficiency is mainly used. However, the hexagonal MLA has complex geometric features. The first feature is that the lens cells are zigzagged in the vertical direction, which should be reflected in the view number calculation for each sub-pixel. The second feature is that the four sides of a hexagonal lens cell are tilted, requiring a more careful view index assignment to the lens cell. In this paper, we propose a sub-pixel multiplexing scheme suitable for the features of the hexagonal MLA. We also propose a view-overlay algorithm based on a two-dimensional lens and compare subjective image quality with existing view-selection through autostereoscopic 3D display implementation.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1998년도 Proceedings of International Symposium on Remote Sensing
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• pp.252-256
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• 1998

Accurate mapping of satellite images is one of the most important Parts in many remote sensing applications. Since the position and the attitude of a satellite during image acquisition cannot be determined accurately enough, it is normal to have several hundred meters' ground-mapping errors in the systematically corrected images. The users which require a pixel-level or a sub-pixel level mapping accuracy for high-resolution satellite images must use a number of Ground Control Points (GCPs). In this paper, the performance of two geometric correction algorithms is tested and compared. One is the polynomial warping algorithm which is simple and popular enough to be implemented in most of the commercial satellite image processing software. The other is full camera modelling algorithm using Physical orbit-sensor-Earth geometry which is used in satellite image data receiving, pre-processing and distribution stations. Several criteria were considered for the performance analysis : ultimate correction accuracy, GCP representatibility, number of GCPs required, convergence speed, sensitiveness to inaccurate GCPs, usefulness of the correction results. This paper focuses on the usefulness of the precision correction algorithm for regular image pre-processing operations. This means that not only final correction accuracy but also the number of GCPs and their spatial distribution required for an image correction are important factors. Both correction algorithms were implemented and will be used for the precision correction of KITSAT-3 images.

• 한국측량학회지
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• 제31권6_2호
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• pp.559-565
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• 2013

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집D
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• pp.654-659
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• 2001

A new non-contact method of the frost thickness measurement has been developed. The method is based on the digital image processing technique to identify the reflection edge of the image captured by a CCD camera under laser sheet light illumination. To insure the accuracy of frost layer thickness, an in-situ calibration procedure is carried out with a calibration target with 0.5mm holes. Using the mapping function obtained by the calibration procedure, the contour of frost surface can be estimated with sub-pixel resolutions. The developed method is applied to study the effect of cooling plate temperature on the frost thickness in a small low speed wind tunnel.

• 한국게임학회 논문지
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• 제20권3호
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• pp.65-74
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• 2020

섀도우 매핑 알고리즘에서 섀도우 맵이 확대되면 계단형 섀도우 실루엣이 나타난다. 본 논문에서는 재구성된 실루엣을 이용하여 소프트 섀도우를 생성한다. 먼저 섀도우 실루엣은 선형 또는 2차 곡선 모델을 기반으로 한 서브 텍셀 엣지 검출 방법을 통해 확보한다. 그리고 적분을 이용하여 정확한 섀도우 밝기의 평균을 얻기 위해 적분 기반 섀도우 필터링 알고리즘을 사용한다. 본 논문은 상기 과정을 통해 앨리어싱 현상이 두드러지는 실루엣을 효과적으로 제거하며 소프트 섀도우를 효율적으로 생성하는 방법을 제시한다.

• 한국측량학회지
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• 제32권6호
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• pp.625-635
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• 2014

Multiple strips are required for large area mapping using ALS (Airborne Laser Scanner) system. LiDAR (Light Detection And Ranging) data collected from the ALS system has discrepancies between strips due to systematic errors of on-board laser scanner and GPS/INS, inaccurate processing of the system calibration as well as boresight misalignments. Such discrepancies deteriorate the overall geometric quality of the end products such as DEM (Digital Elevation Model), building models, and digital maps. Therefore, strip adjustment for minimizing discrepancies between overlapping strips is one of the most essential tasks to create seamless point cloud data. This study implemented area-based matching (ABM) to determine conjugate features for computing 3D transformation parameters. ABM is a well-known method and easily implemented for this purpose. It is obvious that the exact same LiDAR points do not exist in the overlapping strips. Therefore, the term "conjugate point" means that the location of occurring maximum similarity within the overlapping strips. Coordinates of the conjugate locations were determined with sub-pixel accuracy. The major drawbacks of the ABM are sensitive to scale change and rotation. However, there is almost no scale change and the rotation angles are quite small between adjacent strips to apply AMB. Experimental results from this study using both simulated and real datasets demonstrate validity of the proposed scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

• 전자공학회논문지
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• 제53권6호
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• pp.101-109
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• 2016

카메라 센서는 사람의 눈에 비해 제한적인 다이나믹 레인지를 갖기 때문에 영상 획득 시 실제 보이는 것과 다른 모습의 영상을 획득하게 된다. 이러한 문제를 영상 처리를 통해 해결하고자 톤 맵핑 함수를 이용한 방법들과 사람의 눈을 모델링한 레티넥스 이론 기반의 방법들이 연구되었다. 하지만 이러한 방법들은 후광 효과가 발생하거나 영상 개선 시 전역 또는 국부 콘트라스트 향상이 제한적이라는 단점이 있다. 제안하는 방법에서는 영상의 광원 정보를 레티넥스 이론을 활용하여 추정한 후 이를 영상의 품질 향상을 위해 다이나믹 레인지를 최적화시키는데 이용한다. 이 과정에서 후광 효과가 발생하는 것을 방지하기 위해 유사 밝기 영역에서만 평탄화가 이루어지고 밝기 차가 나는 영역은 밝기 차를 유지하도록 한다. 또한 톤 맵핑 함수 적용 시 하나의 화소가 아닌 주변 영역 정보와 추정된 광원 정보를 모두 고려하여 전역 및 국부 콘트라스트가 동시에 향상되는 알고리즘을 제안한다. 실험 결과들을 통해 제안하는 방법이 기존 방법에 비해 국부 콘트라스트 수치가 약 0.4 향상 되었고, 시각적인 면에서도 콘트라스트 향상과 함께 암부와 명부를 동시에 효과적으로 표현한 것을 확인 할 수 있다.

• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.