• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.135-141
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• 2004

Image interpolation is an image enlargement method that calculates an empty pixel value using the information of given pixel values. Since a natural image is composed of various spatial frequency components, it is difficult for one method to interpolate pixels with various spatial frequencies. In this paper, we propose an image interpolation method using multiple neural networks with spatial frequency characteristic. Input image is segmented according to spatial frequency by local variance, and each segmented image is interpolated using neural network established for spatial frequency band. The proposed method is applied to line doubling that becomes an important part in image interpolation because of deinterlacing. In simulation the proposed algorithm shows the improved PSNR result compared with conventional algorithms and method using single neural network.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.6
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• pp.793-799
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• 2016

A multi-purpose sensor interface that provides dual-mode operation of fingerprint sensing and physiological signal detection is presented. The dual-mode sensing capability is achieved by utilizing inter-pixel shielding patterns as capacitive amplifier's input electrodes. A prototype readout circuit including a fingerprint panel for feasibility verification was fabricated in a $0.18{\mu}m$ CMOS process. A single-channel readout circuit was implemented and multiplexed to scan two-dimensional fingerprint pixels, where adaptive calibration capability against pixel-capacitance variations was also implemented. Feasibility of the proposed multi-purpose interface was experimentally verified keeping low-power consumption less than 1.9 mW under a 3.3 V supply.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.360-373
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• 2017

This paper presents a novel digital video steganalysis scheme against the spatial domain video steganography technology based on a spatial temporal detector (ST_D) that considers both spatial and temporal redundancies of the video sequences simultaneously. Three descriptors are constructed on XY, XT and YT planes respectively to depict the spatial and temporal relationship between the current pixel and its adjacent pixels. Considering the impact of local motion intensity and texture complexity on the histogram distribution of three descriptors, each frame is segmented into non-overlapped blocks that are $8{\times}8$ in size for motion and texture analysis. Subsequently, texture and motion factors are introduced to provide reasonable weights for histograms of the three descriptors of each block. After further weighted modulation, the statistics of the histograms of the three descriptors are concatenated into a single value to build the global description of ST_D. The experimental results demonstrate the great advantage of our features relative to those of the rich model (RM), the subtractive pixel adjacency model (SPAM) and subtractive prediction error adjacency matrix (SPEAM), especially for compressed videos, which constitute most Internet videos.

• Journal of Information Display
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• v.3 no.2
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• pp.1-5
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• 2002

A CMOS driving circuit for active matrix type polymer electroluminescent displays was designed to develop an on-chip microdisplay on the single crystal silicon wafer substrate. The driving circuit is a conventional structure that is composed of the row, column and pixel driving parts. 256 gray scales were implemented using pulse amplitude modulation method. The 2-transistor driving scheme was adopted for the pixel driving part. The layout was carried out considering the compatibility with the standard CMOS process. Judging from the layout of the driving circuit, it turns that it is possible to implement a high-resolution display about 400 ppi resolution. Through the HSPICE simulation, it was verified that this circuit is capable of driving a VGA signal mode display and implementing 256 gray levels.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.432-435
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• 2005

The Mega-pixel camera phones become main trends in mobile phone market. The lens modules used in mesa-pixel camera phones need high resolution. One of the main factors of resolution drop is the defects of bare lens. Though there are many advantages in auto-inspection of defects of bare lens, high technical problems take the defect inspections to be done with manual process. In this paper, the type and the source of defects were described and bare lens defect auto-inspection system design was explained. The designed auto-inspection system is composed of illumination optics part, focusing optics part and auto-moving system. With the proposed auto-inspection system, fast and uniform inspection of bare lens can be achieved.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.5
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• pp.209-214
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• 2021

Panoptic segmentation, which is now widely used in computer vision such as medical image analysis, and autonomous driving, helps understanding an image with holistic view. It identifies each pixel by assigning a unique class ID, and an instance ID. Specifically, it can classify 'thing' from 'stuff', and provide pixel-wise results of semantic prediction and object detection. As a result, it can solve both semantic segmentation and instance segmentation tasks through a unified single model, producing two different contexts for two segmentation tasks. Semantic segmentation task focuses on how to obtain multi-scale features from large receptive field, without losing low-level features. On the other hand, instance segmentation task focuses on how to separate 'thing' from 'stuff' and how to produce the representation of detected objects. With the advances of both segmentation techniques, several panoptic segmentation models have been proposed. Many researchers try to solve discrepancy problems between results of two segmentation branches that can be caused on the boundary of the object. In this survey paper, we will introduce the concept of panoptic segmentation, categorize the existing method into two representative methods and explain how it is operated on two methods: top-down method and bottom-up method. Then, we will analyze the performance of various methods with experimental results.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.625-628
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• 2010

This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation based in one dimensional combined neighborhood differences (1D CND). In contrast with the original CND, which is applied with traditional image, 1D CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to these codeword to form another unique value. These values are then grouped to construct the 1D CND feature image where is used in the unsupervised image segmentation. Various experiments using two LANDSAT multispectral images have been performed to evaluate the segmentation and classification accuracy of the proposed method. The result shows that 1D CND feature outperforms the spectral feature, with average classification accuracy of 87.55% whereas that of spectral feature is 55.81%.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.334-334
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• 2013

In the past decade, the infrared detectors based on intersubband transition in quantum dots (QDs) have attracted much attention due to lower dark currents and increased lifetimes, which are in turn due a three-dimensional confinement and a reduction of scattering, respectively. In parallel, focal plane array development for infrared imaging has proceeded from the first to third generations (linear arrays, 2D arrays for staring systems, and large format with enhanced capabilities, respectively). For a step further towards the next generation of FPAs, it is envisioned that a two-dimensional metal hole array (2D-MHA) structures will improve the FPA structure by enhancing the coupling to photodetectors via local field engineering, and will enable wavelength filtering. In regard to the improved performance at certain wavelengths, it is worth pointing out the structural difference between previous 2D-MHA integrated front-illuminated single pixel devices and back-illuminated devices. Apart from the pixel linear dimension, it is a distinct difference that there is a metal cladding (composed of a number of metals for ohmic contact and the read-out integrated circuit hybridization) in the FPA between the heavily doped gallium arsenide used as the contact layer and the ROIC; on the contrary, the front-illuminated single pixel device consists of two heavily doped contact layers separated by the QD-absorber on a semi-infinite GaAs substrate. This paper is focused on analyzing the impact of a two dimensional metal hole array structure integrated to the back-illuminated quantum dots-in-a-well (DWELL) infrared photodetectors. The metal hole array consisting of subwavelength-circular holes penetrating gold layer (2DAu-CHA) provides the enhanced responsivity of DWELL infrared photodetector at certain wavelengths. The performance of 2D-Au-CHA is investigated by calculating the absorption of active layer in the DWELL structure using a finite integration technique. Simulation results show the enhanced electric fields (thereby increasing the absorption in the active layer) resulting from a surface plasmon, a guided mode, and Fabry-Perot resonances. Simulation method accomplished in this paper provides a generalized approach to optimize the design of any type of couplers integrated to infrared photodetectors.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.527-532
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• 2008

This paper proposes a novel approach to shadow detection from a single natural image regardless of orientation and type of light sources. This approach is based on the assumption that shadow brightness changes linearly, and the axiom that a region cast shadow on is darker than that not having shadow under the same environment. Firstly, candidates for shadow are extracted by preprocessing. Then, they are quantized to replace the similar values with a representative value because of the more quantization steps of a pixel brightness, the higher linear independency among the neighboring pixels. Finally, shadows are detected according to linear independency of shadow brightness based on the assumption. The experimental results showed the proposed approach can robustly detect umbra as well as self-shadow and penumbra cast on a single-colored background.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.7
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• pp.1117-1122
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• 2017

Pedestrian detection is a very important and valuable part of artificial intelligence and computer vision. It can be used in various areas for example automatic drive, video analysis and others. Many works have been done for the pedestrian detection. The accuracy of pedestrian detection on multiple pedestrian image has reached high level. It is not easily get more progress now. This paper proposes a new structure based on the idea of HOG and convolutional filters to do the pedestrian detection in single pedestrian image. It can be a method to increase the accuracy depend on the high accuracy in single pedestrian detection. In this paper, we use Multiblock HOG and magnitude of the pixel as the feature and use convolutional filter to do the to extract the feature. And then use NEWFM to be the classifier for training and testing. We use single pedestrian image of the INRIA data set as the data set. The result shows that the Convolutional Multiblock HOG we proposed get better performance which is 0.015 miss rate at 10-4 false positive than the other detection methods for example HOGLBP which is 0.03 miss rate and ChnFtrs which is 0.075 miss rate.