• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.10-14
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• 2016

Holographic data storage (HDS) features short access times, high storage capacities, and fast transfer rates since the data is recorded and read not by lines but by pages within a volume of holographic material. Furthermore, a single pixel can store more than 1 bit if it is multi-level. However, there is a problem of inter-symbol interference (ISI) between the adjacent symbols if the level difference between neighboring symbols is large. Hence, one should avoid side by side placement of the smallest level symbol and the largest level symbol in any direction. This paper proposes a 12/16 modulation code for 4-level holographic data storage, so that the largest symbol is 3 and the smallest symbol is 0, in order to reduce the ISI.

• Journal of Forest and Environmental Science
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• v.24 no.3
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• pp.151-154
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• 2008

Dramatic price increases of fossil fuels and the economic development of emerging nations accelerates the transformation of forest lands into monocultures, e.g. for biofuel production. On this account, cost efficient methods to enable the monitoring of land resources has become a vital ambition. The application of remote sensing techniques has become an integral part of forest attribute estimation and mapping. The aim of this study was to evaluate the potentials of the kNN method by combining terrestrial with remotely sensed data for the development of a pixel-based monitoring system for the small scaled mosaic of different land use types of the off-reserve forests of the Goaso forest district in Ghana, West Africa. For this reason, occurrence and distribution of land use types like cocoa and non-timber forest resources, such as bamboo and raphia palms, were estimated, applying the kNN method to ASTER satellite data. Averaged overall accuracies, ranging from 79% for plantain, to 83% for oil palms, were found for single-attribute classifications, whereas a multi-attribute approach showed overall accuracies of up to 70%. Values of k between 3 and 6 seem appropriate for mapping bamboo. Optimisation of spectral bands improves results considerably.

• Journal of Korea Game Society
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• v.7 no.3
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• pp.31-38
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• 2007

In this paper, we proposed an effective visibility test architecture with improving the mid-texturing architecture. The proposed architecture uses the property of fragments that the visibility of adjacent fragments is identical, and performs only a single visibility test per fragment. To compare with the mid-texturing architecture, simulation results show that the bandwidth requirements and the cell area of the depth cache in the proposed architecture are reduce by 25% and 34%, respectively, in exchange for less than 5% performance decline.

• Journal of Korea Multimedia Society
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• v.23 no.2
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• pp.135-145
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• 2020

Recently, deep-learning based methods for low-light image enhancement accomplish great success through supervised learning. However, they still suffer from the lack of sufficient training data due to difficulty of obtaining a large amount of low-/normal-light image pairs in real environments. In this paper, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP), which gives the constraint that the brightest pixel in a small patch is likely to be close to 1. With this prior, pseudo ground-truth is first generated to establish an unsupervised loss function. The proposed enhancement network is then trained using the proposed unsupervised loss function. To the best of our knowledge, this is the first attempt that performs a low-light image enhancement through unsupervised learning. In addition, we introduce a self-attention map for preserving image details and naturalness in the enhanced result. We validate the proposed method on various public datasets, demonstrating that our method achieves competitive performance over state-of-the-arts.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.506-509
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• 2016

We present real-time pedestrian detection that exploit accuracy of Faster R-CNN network. Faster R-CNN has shown to success at PASCAL VOC multi-object detection tasks, and their ability to operate on raw pixel input without the need to design special features is very engaging. Therefore, in this work we apply and adjust Faster R-CNN to single object detection, which is pedestrian detection. The drawback of Faster R-CNN is its failure when object size is small. Previously, small sized object problem was solved by Scale-aware Network. We incorporate Scale-aware Network to Faster R-CNN. This made our method Scale-aware Faster R-CNN (DF R-CNN) that is both fast and very accurate. We separated Faster R-CNN networks into two sub-network, that is one for large-size objects and another one for small-size objects. The resulting approach achieves a 28.3% average miss rate on the Caltech Pedestrian detection benchmark, which is competitive with the other best reported results.

• Journal of Information Processing Systems
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• v.12 no.4
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• pp.631-643
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• 2016

Due to the block-based discrete cosine transform (BDCT), JPEG compressed images usually exhibit blocking artifacts. When the bit rates are very low, blocking artifacts will seriously affect the image's visual quality. A bilateral filter has the features for edge-preserving when it smooths images, so we propose an adaptive-weighted bilateral filter based on the features. In this paper, an image-deblocking scheme using this kind of adaptive-weighted bilateral filter is proposed to remove and reduce blocking artifacts. Two parameters of the proposed adaptive-weighted bilateral filter are adaptive-weighted so that it can avoid over-blurring unsmooth regions while eliminating blocking artifacts in smooth regions. This is achieved in two aspects: by using local entropy to control the level of filtering of each single pixel point within the image, and by using an improved blind image quality assessment (BIQA) to control the strength of filtering different images whose blocking artifacts are different. It is proved by our experimental results that our proposed image-deblocking scheme provides good performance on eliminating blocking artifacts and can avoid the over-blurring of unsmooth regions.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.611-622
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• 2012

This study proposed two multisensor fusion methods for segment-based image classification utilizing a region-growing segmentation. The proposed algorithms employ a Gaussian-PDF measure and an evidential measure respectively. In remote sensing application, segment-based approaches are used to extract more explicit information on spatial structure compared to pixel-based methods. Data from a single sensor may be insufficient to provide accurate description of a ground scene in image classification. Due to the redundant and complementary nature of multisensor data, a combination of information from multiple sensors can make reduce classification error rate. The Gaussian-PDF method defines a regional measure as the PDF average of pixels belonging to the region, and assigns a region into a class associated with the maximum of regional measure. The evidential fusion method uses two measures of plausibility and belief, which are derived from a mass function of the Beta distribution for the basic probability assignment of every hypothesis about region classes. The proposed methods were applied to the SPOT XS and ENVISAT data, which were acquired over Iksan area of of Korean peninsula. The experiment results showed that the segment-based method of evidential measure is greatly effective on improving the classification via multisensor fusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3136-3150
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• 2015

A vision-based 3D tracking of articulated human hand is one of the major issues in the applications of human computer interactions and understanding the control of robot hand. This paper presents an improved approach for tracking and recovering the 3D position and orientation of a human hand using the Kinect sensor. The basic idea of the proposed method is to solve an optimization problem that minimizes the discrepancy in 3D shape between an actual hand observed by Kinect and a hypothesized 3D hand model. Since each of the 3D hand pose has 23 degrees of freedom, the hand articulation tracking needs computational excessive burden in minimizing the 3D shape discrepancy between an observed hand and a 3D hand model. For this, we first created a 3D hand model which represents the hand with 17 different parts. Secondly, Random Forest classifier was trained on the synthetic depth images generated by animating the developed 3D hand model, which was then used for Haar-like feature-based classification rather than performing per-pixel classification. Classification results were used for estimating the joint positions for the hand skeleton. Through the experiment, we were able to prove that the proposed method showed improvement rates in hand part recognition and a performance of 20-30 fps. The results confirmed its practical use in classifying hand area and successfully tracked and recovered the 3D hand pose in a real time fashion.

• Journal of the Optical Society of Korea
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• v.18 no.2
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• pp.146-150
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• 2014

We propose an experimental holographic imaging scheme combining compressive sensing (CS) theory with digital holography in phase-shifting conditions. We use the Mach-Zehnder interferometer for hologram formation, and apply the compressive sensing (CS) approach to the holography acquisition process. Through projecting the hologram pattern into a digital micro-mirror device (DMD), finally we will acquire the compressive sensing measurements using a photodiode. After receiving the data of two holograms via conventional communication channel, we reconstruct the original object using certain signal recovery algorithms of CS theory and hologram reconstruction techniques, which demonstrated the feasibility of the proposed method.

• International Journal of Control, Automation, and Systems
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• v.3 no.1
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• pp.130-136
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• 2005

A new optical digitizing system for determining the position of a cursor in three dimensions(3D) and an experimental device for its measurement are presented. A semi-passive system using light intensity modulation, a technology that is well known in radar ranging, is employed in order to overcome precision limitations imposed by background light. This system consists of a charge-coupled device camera placed before a rotating mirror and a light-emitting diode whose intensity is modulated. Using a Fresnel pattern for light modulation, it is verified that a substantial improvement of the signal to noise ratio is realized for the background noise and that a resolution of less than a single pixel can be achieved. This opens the doorway to the realization of high precision 3D digitized measurement. We further propose that a 3D position measurement with a monocular optical system can be realized by a numerical experiment if a linear-period modulated waveform is adopted as the light-modulating one.