• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.124-127
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• 2009

In this paper, we proposed automatically video colorization method with partial color sources in first frame. The input color sources are propagated to other gray pixels with the high correlation between two pixels. To robust again the errors in portion of the weak boundary, we calculate correlation between two pixels using dual-path comparison. Video colorization method should maintain the color connectivity between frames. Accordingly, we define reliability of primarily color by compare the color of neighborhood frames. We perform the color correction by blending neighboring color when the reliability of primarily color is low. We formalize this premise with energy function, and find the color to minimize the energy function. In this way, using property of video, we reduce the error caused by propagation and get result of natural changes between frames. Through simulation results, we show the proposed method derive a natural result more than previous method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.72-83
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• 2011

In this paper, we propose a method to detect and recover the occluded parts of face images using the correlation between pairs of pixels. In a training stage, correlation coefficients between every pairs of pixels are calculated using the occlusion-free face images. Once a new occluded face image is shown, the occluded area is detected and recovered using the correlation coefficients obtained in the training stage. We compare the performance of the proposed method with the conventional method based on PCA. The results show that the proposed method detects and recovers occluded area with much smaller noises than the conventional PCA based method. Moreover, recovered images by the proposed method were more smooth with reduced blurring effect.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1143-1153
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• 2018

For TOPS InSAR processing, high-precision image co-registration is required. We propose an image co-registration method suitable for the TOPS mode by comparing the performance of cross correlation method, the geometric co-registration and the enhanced spectral diversity (ESD) matching algorithm based on the spectral diversity (SD) on the Sentinel-1 TOPS mode image. Using 23 pairs of interferometric pairs generated from 25 Sentinel-1 TOPS images, we applied the cross correlation (CC), geometric correction with only orbit information (GC1), geometric correction combined with iterative cross-correlation (GC2, GC3, GC4), and ESD iteration (ESD_GC, ESD_1, ESD_2). The mean of co-registration errors in azimuth direction by cross correlation and geometric matching are 0.0041 pixels and 0.0016 pixels, respectively. Although the ESD method shows the most accurate result with the error of less than 0.0005 pixels, the error of geometric co-registration is reduced to 0.001 pixels by repetition through additional cross correlation matching between the reference and resampled slave image. The ESD method is not applicable when the coherence of the burst overlap areas is low. Therefore, the geometric co-registration method through iterative processing is a suitable alternative for time series analysis using multiple SAR data or generating interferogram with long time intervals.

• Anatomy and Cell Biology
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• v.57 no.2
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• pp.271-277
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• 2024

Human dentition is unique to individuals and helps in identification of individuals in forensic odontology. This study proposes to study the manually ground sections of single rooted teeth using digital methods for dental age estimation. To assess the dentinal translucency from the scanned digital images of manually ground section of teeth using commercially available image edition software. Corroborating the root dentinal translucency length and region of interest (ROI) of translucency zone in pixels (as a marker of dental age) with the chronological age of the subject, as stratified by different age groups. Twenty single-rooted extracted teeth from 20 patients each from 6 groups divided as per age. Manual sectioning of the teeth followed by scanning the sections was done. Root area in pixels and ROI of translucency zone were measured. From the observed values, translucency length percentage (TLP) and percentage of ROI in pixels (TPP) was calculated and tabulated. Pearson's correlation coefficients were obtained for age with TLP and TPP. Positive correlation existed between age and TLP and also between age and TPP. With the obtained data, multilinear regression equations for specific age groups based on 10-year intervals were derived. By a step-down analysis method, age was estimated with an average error of around ±7.9 years. This study gives a novel method for age-estimation that can be applied in real-time forensic sciences.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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• pp.229-234
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• 2018

Texture is one of the most useful features in classifying and segmenting images. The LBP-based approach previously presented in the literature has been successful in many applications. However, it's theoretical foundation is based only on the difference of pixel values, and consequently it has a number of drawbacks like it performs poorly for the images corrupted with noise, and especially it cannot be used as a multiscale texture descriptor due to the exploding increase of feature vector dimension with increase of the number of neighbor pixels. In this paper, we present a method to address these drawbacks of LBP-based approach. More specifically, our approach quantizes the range of pixels values and construct a 3D histogram which captures the correlative information of pixels. This histogram is used as a texture feature. Several tests with texture images show that the proposed method outperforms the LBP-based approach in the problem of texture classification.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.1C
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• pp.24-31
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• 2003

In this paper, a new opto-digital multiple information hiding and real-time extracting system is implemented. That is, multiple information is hidden in a cover image by using the stego keys which are generated by combined use of random sequence(RS) and Hadamard matrix(HM) and these hidden information is extracted in real-time by using a new optical correlator-based extraction system. In the experiment, 3 kinds of information, English alphabet of "N", "R", "L" having 512$\times$512 pixels, are formulated 8$\times$8 blocks and each of these information is multiplied with the corresponding stego keys having 64$\times$64 pixels one by one. And then, by adding these modulated data to a cover image of "Lena"having 512$\times$512 pixels, a stego image is finally generated. In this paper, as an extraction system, a new optical nonlinear joint transform correlator(NJTC) is introduced to extract the hidden data from a stego image in real-time, in which optical correlation between the stego image and each of the stego keys is performed and from these correlation outputs the hidden data can be asily exacted in real-time. Especially, it is found that the SNRs of the correlation outputs in the proposed optical NJTC-based extraction system has been improved to 7㏈ on average by comparison with those of the conventional JTC system under the condition of having a nonlinear parameter less than k=0.4. This good experimental results might suggest a possibility of implementation of an opto-digital multiple information hiding and real-time extracting system.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.157-157
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• 2017

To minimize the damage by wild birds and acquire the benefits such as protection against weeds and maintenance of water content in soil, the mulching black color vinyl after seeding should be carried out. Non-contact and non-destructive methods that can continuously determine the locations are necessary. In this study, a crop position detection method was studied that uses infrared thermal image sensor to determine the cotyledon position under vinyl mulch. The moving system for acquiring image arrays has been developed for continuously detecting crop locations under plastic mulching on the field. A sliding mechanical device was developed to move the sensor, which were arranged in the form of a linear array, perpendicular to the array using a micro-controller integrated with a stepping motor. The experiments were conducted while moving 4.00 cm/s speed of the IR sensor by the rotational speed of the stepping motor based on a digital pulse width modulation signal from the micro-controller. The acquired images were calibrated with the spatial image correlation. The collected data were processed using moving averaging on interpolation to determine the frame where the variance was the smallest in resolution units of 1.02 cm. Non-linear integral interpolation was one of method for analyzing the frequency using the normalization image and then arbitrarily increasing the limited data value of $16{\times}4pixels$ in one frame. It was a method to relatively reduce the size of overlapping pixels by arbitrarily increasing the limited data value. The splitted frames into 0.1 units instead of 1 pixel can propose more than 10 times more accurate and original method than the existing correction method. The non-integral calibration method was conducted by applying the subdivision method to the pixels to find the optimal correction resolution based on the first reversed frequency. In order to find a correct resolution, the expected location of the first crop was indicated on near pixel 4 in the inversion frequency. For the most optimized resolution, the pixel was divided by 0.4 pixel instead of one pixel to find out where the lowest frequency exists.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.5
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• pp.487-496
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• 2011

This study is aimed to perform more reliable unsupervised change detection through the re-extraction of the changed pixels which were extracted with global thresholding by applying buffer zone concept. First, three buffer zone was divided on the basis of the thresholding value which was determined using range average and the maximum distance point from a straight line. We re-extracted the changed pixels by performing unsupervised classification for buffer zone II which consists of changed pixels and unchanged pixels. The proposed method was implemented in Hyperion hyperspectral images and evaluated comparing to the existing global thresholding method. The experimental results demonstrated that the proposed method performed more accuracy change detection for vegetation area even if extracted slightly more changed pixels.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.10-16
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• 2007

In this paper, we propose a technique for retrieving images using spatial color correlation and texture characteristics based on local fourier transform. In order to retrieve images, two new descriptors are proposed. One is a color descriptor which represents spatial color correlation. The other is a descriptor combining the proposed color descriptor with texture descriptor. Since most of existing color descriptors including color correlogram which represent spatial color correlation considered just color distribution between neighborhood pixels, the structural information of neighborhood pixels is not considered. Therefore, a novel color descriptor which simultaneously represents spatial color distribution and structural information is proposed. The proposed color descriptor represents color distribution of Min-Max color pairs calculating color distance between center pixel and neighborhood pixels in a block with 3x3 size. Also, the structural information which indicates directional difference between minimum color and maximum color is simultaneously considered. Then new color descriptor(min-max color correlation descriptor, MMCCD) containing mean and variance values of each directional difference is generated. While the proposed color descriptor includes by far smaller feature vector over color correlogram, the proposed color descriptor improves 2.5 % ${\sim}$ 13.21% precision rate, compared with color correlogram. In addition, we propose a another descriptor which combines the proposed color descriptor and texture characteristics based on local fourier transform. The combined method reduces size of feature vector as well as shows improved results over existing methods.

• Current Optics and Photonics
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• v.5 no.3
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.