• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.101-109
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• 2011

This paper describes the attempts to improve the performance of an image matching method for multiple image. Typically, matching between two images is performed by using correlation between a reference and corresponding images. The proposed multiple image matching algorithm performs matching in an object space, chooses the image closest to the true vertical image as a reference image, calculates the correlation based on the chosen reference image. The algorithm also detects occluded regions automatically and keep them from matching. We could find that it is possible to create high quality DEM by this method, regardless of the location of image. From the performance improvement experiments through the occlusion detection, we could confirm the possibility of a more accurate representation of 3D information.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.2 no.2 s.3
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• pp.85-95
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• 2003

This paper proposes a multiple moving objects tracking system which is adaptable itself to circumstances. Snake model is sensitive to the start position value because it does not accurately express contours of objects in complex image. It can be improved as the proposed system gets background images by using difference images, segments objects using neighborhood pixels and assesses the position feature values acquired on the start position value to deformable Snake model. And also the system can simplify complex background images and reduce search regions by the constituent points of a Snake laid in Positions of object. It is showed that the proposed system can be appBied to multiple moving vehicle racking systems by the experimental results of 30fps AVI file.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.3
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• pp.269-276
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• 2010

In this paper, we present the detection method of moving objects based on two background models. These background models support to understand multi layered environment belonged in images taken by shaking camera and each model is MBM(Multiple Background Model) and TMBM (Temporal Median Background Model). Because two background models are Pixel-based model, it must have noise by camera movement. Therefore correlation coefficient calculates the similarity between consecutive images and measures camera motion vector which indicates camera movement. For the calculation of correlation coefficient, we choose the selected region and searching area in the current and previous image respectively then we have a displacement vector by the correlation process. Every selected region must have its own displacement vector therefore the global maximum of a histogram of displacement vectors is the camera motion vector between consecutive images. The MBM classifies the intensity distribution of each pixel continuously related by camera motion vector to the multi clusters. However, MBM has weak sensitivity for temporal intensity variation thus we use TMBM to support the weakness of system. In the video-based experiment, we verify the presented algorithm needs around 49(ms) to generate two background models and detect moving objects.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.148-153
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• 2009

Few methods have dealt with segmenting multiple images with analogous content. Concurrent images of a scene and gathered images of a similar foreground are examples of these images, which we term consistent scene images. In this paper, we present a method to segment these images based on manual segmentation of one image, by iteratively propagating information via multi-level cues with adaptive confidence. The cues are classified as low-, mid-, and high- levels based on whether they pertain to pixels, patches, and shapes. Propagated cues are used to compute potentials in an MRF framework, and segmentation is done by energy minimization. Through this process, the proposed method attempts to maximize the amount of extracted information and maximize the consistency of segmentation. We demonstrate the effectiveness of the proposed method on several sets of consistent scene images and provide a comparison with results based only on mid-level cues [1].

• Fashion & Textile Research Journal
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• v.18 no.4
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• pp.477-488
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• 2016

This study classified and analyzed the types of images posted on official accounts operated by domestic and foreign SPA brands on Instagram and Pinterest, which are image-based fashion curations, and performed a survey on preferred image types in the fashion curations of SPA brands. It aims to induce active apparel purchasing behavior of consumers through the suggestion of image types about fashion curations for effective communication between fashion brands and consumers. The survey to targets the 20s and 30s was carried out from October 23, 2015 until November 22 and conducted factor analysis, paired t-test. The above images were classified into four types based on previous studies: product images, brand images, lifestyle images, multiple images. The results of the survey were also divided into four factors in line with the classification of image types. Generally, foreign SPA brands(H&M, Uniqlo, Zara) used image-based fashion curation services more frequently than domestic SPA brands(8Seconds, Mixxo, Spao, Tngt). The analysis of image types in the fashion curations of SPA brands showed that product images accounted for the highest proportion of images used in the official accounts of SPA brands. However, the comparison of averages on the preferred image types of survey respondents showed that the users who had once visited the official accounts of SPA brands on Instagram and Pinterest preferred in the order of lifestyle information > product information > brand information > multiple information provided by SPA brands, which was statistically significant.

• Journal of the Optical Society of Korea
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• v.12 no.2
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• pp.98-102
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• 2008

Optical Coherence Tomography (OCT) is an important noninvasive medical imaging technique that can reveal subsurface structures of biological tissue. OCT has demonstrated a good correlation with histology in sufficient resolution to identify morphological changes in articular cartilage to differentiate normal through progressive stages of degenerative joint disease. Current OCT systems provide individual cross-sectional images that are representative of the tissue directly under the scanning beam, but they may not fully demonstrate the degree of degeneration occurring within a region of a joint surface. For a full understanding of the nature and degree of cartilage degeneration within a joint, multiple OCT images must be obtained and an overall assessment of the joint surmised from multiple individual images. This study presents frequency domain three-dimensional (3-D) OCT imaging of degenerative joint cartilage extracted from bovine knees. The 3-D OCT imaging of articular cartilage enables the assembly of 126 individual, adjacent, rapid scanned OCT images into a full 3-D image representation of the tissue scanned, or these may be viewed in a progression of successive individual two-dimensional (2-D) OCT images arranged in 3-D orientation. A fiber-based frequency domain OCT system that provides cross-sectional images was used to acquire 126 successive adjacent images for a sample volume of $6{\times}3.2{\times}2.5\;mm^3$. The axial resolution was $8\;{\mu}m$ in air. The 3-D OCT was able to demonstrate surface topography and subsurface disruption of articular cartilage consistent with the gross image as well as with histological cross-sections of the specimen. The 3-D OCT volumetric imaging of articular cartilage provides an enhanced appreciation and better understanding of regional degenerative joint disease than may be realized by individual 2-D OCT sectional images.

• Spatial Information Research
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• v.19 no.1
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• pp.29-40
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• 2011

GOCI the world first Ocean Color Imager in Geostationary Orbit, which could obtain total 8 images of the same region a day, however, its spatial resolution(500m) is not enough to use for the accurate land application, Super Resolution(SR), reconstructing the high resolution(HR) image from multiple low resolution(LR) images introduced by computer vision field. could be applied to the time-series remotely sensed images such as GOCI data, and the higher resolution image could be reconstructed from multiple images by the SR, and also the cloud masked area of images could be recovered. As the precedent study for developing the efficient SR method for GOCI images, on this research, it reproduced the simulated data under the acquisition process of the remote sensed data, and then the simulated images arc applied to the proposed algorithm. From the proposed algorithm result of the simulated data, it turned out that low resolution(LR) images could be registered in sub-pixel accuracy, and the reconstructed HR image including RMSE, PSNR, SSIM Index value compared with original HR image were 0.5763, 52.9183 db, 0.9486, could be obtained.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.953-958
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• 2022

Holographic Stereogram can provide the depth perception without the visual fatigue and dizziness because it use multiple images acquired from the multiple viewpoints. In order to produce a holographic stereogram, it is necessary to obtain perspective images of a live object and record it on film using a digital hologram printer. when acquiring perspective images, the hologram without distortion can be produced only when the perspective images with a constant distance between the camera and the target is obtained. If the target is small, it is possible to keep the constant distance from the camera to object. but if it is large, this is difficult to keep the constant distance. In this study, we photograph the large object using the POI (Point of Interest) function which is one of the smart flight modes of drone to produce perspective images required for the hologram production. after that, problems such as the unexpected shakings and distance change between camera and object is corrected in post production. as a result, we produce the perspective images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.1-8
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• 2006

This paper presents a fast full search algorithm for motion estimation applicable to multiple reference images. The proposed method is an extended version of the rate constrained successive elimination algorithm (RSEA) for multiple reference frame applications. We will show that motion estimation for the reference images temporally preceding the first reference image can be less intensive in computation compared with that for the first reference image. for computational reduction, we will drive a new condition to lead the smaller number of candidate blocks for the best matched block. Simulation results explain that our method reduces computation complexity although it has the same quality as RSEA.