• ETRI Journal
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• v.41 no.4
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.3
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• pp.202-208
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• 2013

In the image processing for VS (Video Surveillance), the detection of moving entities in a monitored scene is an important step. A background subtraction technique has been widely employed to find the moving entities. However, the extracted foreground regions often include not only real entities but also their cast shadows, and this can cause errors in following image processing steps, such as tracking, recognition, and analysis. In this paper, a novel technique is proposed to determine the shadow pixels of moving objects in the foreground image of a VS camera. Compared to existing techniques where the same decision criteria are applied to all moving pixels, the proposed technique determines shadow pixels using local features based on two facts: First, the amount of pixel intensity drop due to a shadow depends on the intensity level of background. Second, the distribution pattern of pixel intensities remains even if a shadow is cast. The proposed method has been tested at various situations with different backgrounds and moving humans in different colors.

• Korean Journal of Optics and Photonics
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• v.7 no.1
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• pp.9-16
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• 1996

We perform computer simulations of a lenticular autostereoscopic display system and obtain two one-dimensional intensity distributions seen by the left and right eyes of the observer, respectively. Our result shows that the lens period should be smaller than the LCD pixel period as the observer moves closer to the system. We obtain the values of the system parameters for which the intensity distributions are uniform across the lenticular lens array. We also obtain the system crosstalks which represent the right pixel image seen by the left eye and the left pixel image seen by the right eye. Our simulation also shows that the crosstalk increases as the observer moves away from the lenticular system.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.49-60
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• 2020

To solve the edge ringing or block effect caused by the partial differential diffusion in image enhancement domain, a new image enhancement algorithm based on bidirectional diffusion, which smooths the flat region or isolated noise region and sharpens the edge region in different types of defect images on aviation composites, is presented. Taking the image pixel's neighborhood intensity and spatial characteristics as the attribute descriptor, the presented bidirectional diffusion model adaptively chooses different diffusion criteria in different defect image regions, which are elaborated are as follows. The forward diffusion is adopted to denoise along the pixel's gradient direction and edge direction in the pixel's smoothing area while the backward diffusion is used to sharpen along the pixel's gradient direction and the forward diffusion is used to smooth along the pixel's edge direction in the pixel's edge region. The comparison experiments were implemented in the delamination, inclusion, channel, shrinkage, blowhole and crack defect images, and the comparison results indicate that our algorithm not only preserves the image feature better but also improves the image contrast more obviously.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.693-701
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• 2011

Image registration is an essential process to analyze the time series of satellite images for the purpose of image fusion and change detection. The Mutual Information (MI) is commonly used as similarity measure for image registration because of its robustness to noise. Due to the radiometric differences, it is not easy to apply MI to multi-temporal satellite images using directly the pixel intensity. Image features for MI are more abundantly obtained by employing a Gabor filter which varies adaptively with the filter characteristics such as filter size, frequency and orientation for each pixel. In this paper we employed Bidirectional Gabor Filter Energy (BGFE) defined by Gabor filter features and applied the BGFE to similarity measure calculation as an image feature for MI. The experiment results show that the proposed method is more robust than the conventional MI method combined with intensity or gradient magnitude.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.1220-1222
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• 2004

We developed a new device structure using anisotropic phase separation from liquid crystal (LC) and polymer composite materials using UV intensity variation and polymer wetting properties. In the device, the LC molecules are isolated in pixels where LCs are surrounded by the inter-pixel vertical polymer walls and the horizontal polymer films on the upper substrate. These devices show very good mechanical stability against external pressure. The electro-optic characteristics and the mechanical stability of the devices are discussed in view of the flexible display applications.

• Proceedings of the KSRS Conference
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• v.2
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• pp.772-775
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• 2006

Muilti-dimensional feature vector matching algorithm uses multiple features such as intensity, gradient, variance, first or second derivative of a pixel to find correspondence pixels in stereo images. In this paper, we proposed a new method for adjusting automatically the weight of feature in multi-dimensional feature vector matching considering sharpeness of a pixel in feature vector distance curve. The sharpeness consists of minimum and maximum vector distances of a small window mask. In the experiment we used IKONOS satellite stereo imagery and obtained accurate matching results comparable to the manual weight-adjusting method.

• Journal of Sensor Science and Technology
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• v.26 no.2
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• pp.85-90
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• 2017

A wide dynamic range (WDR) CMOS image sensor (CIS) was developed with a specialized readout architecture for realizing high-sensitivity (HS) and low-sensitivity (LS) reading modes. The proposed pixel is basically a three-transistor (3T) active pixel sensor (APS) structure with an additional transistor. In the developed WDR CIS, only one mode between the HS mode for relatively weak light intensity and the LS mode for the strong light intensity is activated by an external controlling signal, and then the selected signal is read through each column-parallel readout circuit. The LS mode is implemented with the column capacitors and a feedback structure for adjusting column capacitor size. In particular, the feedback circuit makes it possible to change the column node capacitance automatically by using the incident light intensity. As a result, the proposed CIS achieved a wide dynamic range of 94 dB by synthesizing output signals from both modes. The prototype CIS is implemented with $0.18-{\mu}m$ 1-poly 6-metal (1P6M) standard CMOS technology, and the number of effective pixels is 176 (H) ${\times}$ 144 (V).

• Journal of Sensor Science and Technology
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• v.17 no.1
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• pp.41-52
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• 2008

In this paper, a circuit analysis of the complementary CMOS active pixel and readout circuit is carried out. Complementary pixel structure which is different from conventional 3TR APS structure is consist of photo diode, reset PMOS, several NMOSs and PMOSs sets for complementary signals. Photo diode is modelled with Medici device program. HSPICE was used to analyze the variation of the signal feature depending on light intensity using $0.5{\mu}M$ standard CMOS process. Simulation results show that the output signal range is from 0.8 V to 4.5 V. This signal range increased 135 % output dynamic range compared to conventional 3TR pixel in the condition of 5 V power supply.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.12
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• pp.84-92
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• 1998

In this paper, we propose the novel cost functions for finding the disparity between the left and the right images in the stereo matching problem. The dynamic programming method is used in solving the stereo matching problem by Cox et al[10]. In the reference[10], only the intensity of the pixels in the epipolar line is used as the cost functions to find the corresponding pixels. We propose the two new cost functions. The information of the slope of the pixel is introduced to the constraints in determining the weights of intensity and direction(the historical information). The pixels with the higher slope are matched mainly by the intensity of pixels. As the slope becomes lower, the matching is performed mainly by the direction. Secondly, the disparity information of the previous epipolar line the pixel is used to find the disparity of the current epipolar line. If the pixel in the left epipolar line, $p-i$ and the pixel in the right epipolar line, $p-j$ satisfy the following conditions, the higher matching probability is given to the pixels, $p-i$ and $p-j$. i) The pixels, $p-i$ and $p-j$ are the pixles on the edges in the left and the right images, respectively. ⅱ) For the pixels $p-k$ and $p-l$ in the previous epipolar line, $p-k$and $p-l$ are matched and are the pixels on the same edge with $p-i$ and $p-j$, respectively. The proposed method compared with the original method[10] finds the better matching results for the test images.