• Title/Summary/Keyword: large scale image

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The Effect of Teacher's Image and Recognition of Teaching Practice for Student Teacher's Practicum Satisfaction (교사이미지와 교육실습에 대한 인식이 예비유아교사의 교육실습만족도에 미치는 영향)

  • Lee, Jeong Hee;Cho, Songyon
    • Journal of the Korean Home Economics Association
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    • v.50 no.8
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    • pp.113-123
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    • 2012
  • This research has an objective of investigating the challenges that student teachers encounter during practicum, the difference between teachers' image based on sociodemographic variables, recognition and satisfaction for practicum, and the effect of teachers' image and practicum experience on the satisfaction for the practicum. The participants of this study included 500 student teachers who enrolled in the department of early childhood education in a 2 or 3-year college or a 4-year university and completed practicum in the Chungcheong area. The instruments for this study were Teacher's Image Scale, Practicum Experience Scale, Practicum Satisfaction Scale and a questionnaire for sociodemographic variables. The results were as follows: First, the highest level of teachers' image was obtained for student teachers enrolled in a 2-year college, completed practicum in large cities and finished practicum at an institution with a size of 3-6 classes. Also, the highest level of 'recognition for practicum' of student teachers was observed when they were instructed by a guidance teacher with a degree from a graduate school and with a first degree certification in public kindergarten. Second, the satisfaction for practicum of student teachers was mostly affected by' the atmosphere of the practice institutions', while personality affected the teachers' image.

PARALLAX ADJUSTMENT FOR REALISTIC 3D STEREO VIEWING OF A SINGLE REMOTE SENSING IMAGE

  • Kim, Hye-Jin;Choi, Jae-Wan;Chang, An-Jin;Yu, Ki-Yun
    • Proceedings of the KSRS Conference
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    • 2007.10a
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    • pp.452-455
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    • 2007
  • 3D stereoscopic viewing of large scale imagery, such as aerial photography and satellite images, needs different parallaxes relative to the display scale. For example, when a viewer sees a stereoscopic image of aerial photography, the optimal parallax of its zoom-in image should be smaller than that of its zoom-out. Therefore, relative parallax adjustment according to the display scale is required. Merely adjusting the spacing between stereo images is not appropriate because the depths of the whole image are either exaggerated or reduced entirely. This paper focuses on the improving stereoscopic viewing with a single remote sensing image and a digital surface model (DSM). We present the parallax adjustment technique to maximize the 3D realistic effect and the visual comfort. For remote sensing data, DSM height value can be regarded as disparity. There are two possible kinds of methods to adjust the relative parallax with a single image performance. One is the DSM compression technique: the other is an adjustment of the distance between the original image and its stereo-mate. In our approach, we carried out a test to evaluate the optimal distance between a single remote sensing image and its stereo-mate, relative to the viewing scale. Several synthetic stereo-mates according to certain viewing scale were created using a parallel projection model and their anaglyphs were estimated visually. The occlusion of the synthetic stereo-mate was restored by the inpainting method using the fields of experts (FoE) model. With the experiments using QuickBird imagery, we could obtain stereoscopic images with optimized parallax at varied display scales.

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Volume Rendering using Grid Computing for Large-Scale Volume Data

  • Nishihashi, Kunihiko;Higaki, Toru;Okabe, Kenji;Raytchev, Bisser;Tamaki, Toru;Kaneda, Kazufumi
    • International Journal of CAD/CAM
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    • v.9 no.1
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    • pp.111-120
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    • 2010
  • In this paper, we propose a volume rendering method using grid computing for large-scale volume data. Grid computing is attractive because medical institutions and research facilities often have a large number of idle computers. A large-scale volume data is divided into sub-volumes and the sub-volumes are rendered using grid computing. When using grid computing, different computers rarely have the same processor speeds. Thus the return order of results rarely matches the sending order. However order is vital when combining results to create a final image. Job-Scheduling is important in grid computing for volume rendering, so we use an obstacle-flag which changes priorities dynamically to manage sub-volume results. Obstacle-Flags manage visibility of each sub-volume when line of sight from the view point is obscured by other subvolumes. The proposed Dynamic Job-Scheduling based on visibility substantially increases efficiency. Our Dynamic Job-Scheduling method was implemented on our university's campus grid and we conducted comparative experiments, which showed that the proposed method provides significant improvements in efficiency for large-scale volume rendering.

Implementation of External Memory Expansion Device for Large Image Processing (대규모 영상처리를 위한 외장 메모리 확장장치의 구현)

  • Choi, Yongseok;Lee, Hyejin
    • Journal of Broadcast Engineering
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    • v.23 no.5
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    • pp.606-613
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    • 2018
  • This study is concerned with implementing an external memory expansion device for large-scale image processing. It consists of an external memory adapter card with a PCI(Peripheral Component Interconnect) Express Gen3 x8 interface mounted on a graphics workstation for image processing and an external memory board with external DDR(Dual Data Rate) memory. The connection between the memory adapter card and the external memory board is made through the optical interface. In order to access the external memory, both Programmable I/O and DMA(Direct Memory Access) methods can be used to efficiently transmit and receive image data. We implemented the result of this study using the boards equipped with Altera Stratix V FPGA(Field Programmable Gate Array) and 40G optical transceiver and the test result shows 1.6GB/s bandwidth performance.. It can handle one channel of 4K UHD(Ultra High Density) image. We will continue our study in the future for showing bandwidth of 3GB/s or more.

RPC-based epipolar image resampling of Kompsat-2 across-track stereos (RPC를 기반으로 한 아리랑 2호 에피폴라 영상제작)

  • Oh, Jae-Hong;Lee, Hyo-Seong
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.29 no.2
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    • pp.157-164
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    • 2011
  • As high-resolution satellite images have enabled large scale topographic mapping and monitoring on global scale with short revisit time, agile sensor orientation, and large swath width, many countries make effort to secure the satellite image information. In Korea, KOMPSAT-2 (KOrea Multi-Purpose SATellite-2) was launched in July 28 2006 with high specification. These satellites have stereo image acquisition capability for 3D mapping and monitoring. To efficiently handle stereo images such as stereo display and monitoring, the accurate epipolar image generation process is prerequisite. However, the process was highly limited due to complexity in epipolar geometry of pushbroom sensor. Recently, the piecewise approach to generate epipolar images using RPC was developed and tested for in-track IKONOS stereo images. In this paper, the piecewise approach was tested for KOMPSAT-2 across-track stereo images to see how accurately KOMPSAT-2 epipolar images can be generated for 3D geospatial applications. In the experiment, two across-track stereo sets from three KOMPSAT-2 images of different dates were tested using RPC as the sensor model. The test results showed that one-pixel level of y-parallax was achieved for manually measured tie points.

Affine Local Descriptors for Viewpoint Invariant Face Recognition

  • Gao, Yongbin;Lee, Hyo Jong
    • Proceedings of the Korea Information Processing Society Conference
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    • 2014.04a
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    • pp.781-784
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    • 2014
  • Face recognition under controlled settings, such as limited viewpoint and illumination change, can achieve good performance nowadays. However, real world application for face recognition is still challenging. In this paper, we use Affine SIFT to detect affine invariant local descriptors for face recognition under large viewpoint change. Affine SIFT is an extension of SIFT algorithm. SIFT algorithm is scale and rotation invariant, which is powerful for small viewpoint changes in face recognition, but it fails when large viewpoint change exists. In our scheme, Affine SIFT is used for both gallery face and probe face, which generates a series of different viewpoints using affine transformation. Therefore, Affine SIFT allows viewpoint difference between gallery face and probe face. Experiment results show our framework achieves better recognition accuracy than SIFT algorithm on FERET database.

Drone Image AI Analysis Model for Ecological Environment Investigation (생태 환경 조사를 위한 드론영상 AI분석 모델)

  • Shin, Kwang-seong;Shin, Seong-yoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.05a
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    • pp.355-356
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    • 2021
  • Geological and biological surveys are conducted every year to investigate the state of tidal flat loss and ecological changes in the Saemangeum embankment. In addition, various activities for forest monitoring and large-scale environmental monitoring are being actively carried out throughout Korea. Due to the recent development of drone technology and artificial intelligence technology, various studies are being conducted to perform these activities more efficiently and economically. In this study, we propose an image segmentation technique using semantic segmentation to efficiently investigate and analyze large-scale ecological environments using Drone.

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Large-scale Language-image Model-based Bag-of-Objects Extraction for Visual Place Recognition (영상 기반 위치 인식을 위한 대규모 언어-이미지 모델 기반의 Bag-of-Objects 표현)

  • Seung Won Jung;Byungjae Park
    • Journal of Sensor Science and Technology
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    • v.33 no.2
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    • pp.78-85
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    • 2024
  • We proposed a method for visual place recognition that represents images using objects as visual words. Visual words represent the various objects present in urban environments. To detect various objects within the images, we implemented and used a zero-shot detector based on a large-scale image language model. This zero-shot detector enables the detection of various objects in urban environments without additional training. In the process of creating histograms using the proposed method, frequency-based weighting was applied to consider the importance of each object. Through experiments with open datasets, the potential of the proposed method was demonstrated by comparing it with another method, even in situations involving environmental or viewpoint changes.

Large-Scale Realtime Crowd Simulation Using Image-Based Affordance and Navigation Potential Fields (이미지 기반의 유도장과 항해장을 활용한 실시간 대규모 군중 시뮬레이션)

  • Ok, Soo-Yol
    • Journal of Korea Multimedia Society
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    • v.17 no.9
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    • pp.1104-1114
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    • 2014
  • In large-scale crowd simulations, it is very important for the decision-making system of manipulating interactive behaviors to minimize the computational cost for controlling realistic behaviors such as collision avoidance. In this paper, we propose a large-scale realtime crowd simulation method using the affordance and navigation potential fields such as attractive and repulsive forces of electromagnetic fields. In particular, the model that we propose locally handles the realistic interactions between agents, and thus radically reduces the cost of expensive computation on interactions which has been the most problematic in crowd simulation. Our method is widely applicable to the expression and analysis of various crowd behaviors that are needed in behavior control in computer games, crowd scenes in movies, emergent behaviors of evacuation, etc.

A Deep Convolutional Neural Network approach to Large Scale Structure

  • Sabiu, Cristiano G.
    • The Bulletin of The Korean Astronomical Society
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    • v.44 no.2
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    • pp.53.3-53.3
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    • 2019
  • Recent work by Ravanbakhsh et al. (2017), Mathuriya et al. (2018) showed that convolutional neural networks (CNN) can be trained to predict cosmological parameters from the visual shape of the large scale structure, i.e. the filaments, clusters and voids of the cosmic density field. These preliminary works used the dark matter density field at redshift zero. We build upon these works by considering realistic mock galaxy catalogues that mimic true observations. We construct light-cones that span the redshift range appropriate for current and near future cosmological surveys such as LSST, EUCLID, WFIRST etc. In summary, we propose a novel multi-image input CNN to track the evolution in the morphology of large scale structures over cosmic time to constrain cosmology and the expansion history of the Universe.

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