• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1728-1743
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• 2021

Three-dimensional (3D) video scenes are complex and difficult to control, especially when scene switching occurs. In this paper, we propose two algorithms based on an incremental proportional-integral-derivative (PID) algorithm and a similarity analysis between views to improve the method of bit allocation for multi-view high efficiency video coding (MV-HEVC). Firstly, an incremental PID algorithm is introduced to control the buffer "liquid level" to reduce the negative impact on the target bit allocation of the view layer and frame layer owing to the fluctuation of the buffer "liquid level". Then, using the image similarity between views is used to establish, a bit allocation calculation model for the multi-view video main viewpoint and non-main viewpoint is established. Then, a bit allocation calculation method based on hierarchical B frames is proposed. Experimental simulation results verify that the algorithm ensures a smooth transition of image quality while increasing the coding efficiency, and the PSNR increases by 0.03 to 0.82dB while not significantly increasing the calculation complexity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.7
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• pp.127-132
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• 2019

Influenza is an infectious disease caused by an influenza virus with symptoms of high fever and headache. Since influenza especially mutates into multiple subtypes in the carrier's body, it is a serious threat for mankind such as Spanish influenza. The treatment of influenza infection mandates the use of antiviral drugs through rapid diagnostic test. Generally, immunochromatography-based rapid influenza diagnostic tests are used for rapid diagnosis in an emergency. In this paper, we propose an influenza analysis algorithm based on image processing to examine a large number of patients suspected of being infected with influenza. Also, we propose a robust influenza analysis algorithm based on the joint cumulative mass function under varying radiometric conditions such as illuminant and exposure differences. Simulation results show that the proposed algorithm significantly reduces the error of influenza diagnosis under different radiometric conditions.

• Journal of the Korea Institute of Military Science and Technology
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• v.5 no.3
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• pp.62-76
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• 2002

This paper describes the design of a Coordinates Tracking algorithm for EOTS and its error analysis. EOTS stabilizes the image sensors such as FLIR, CCD TV camera, LRF/LD, and so on, tracks targets automatically, and provides navigation capability for vehicles. The Coordinates Tracking algorithm calculates the azimuth and the elevation angle of EOTS using the inertial navigation system and the attitude sensors of the vehicle, so that LOS designates the target coordinates which is generated by a Radar or an operator. In the error analysis in this paper, the unexpected behaviors of EOTS that is due to the time delay and deadbeat of the digital signals of the vehicle equipments are anticipated and the countermeasures are suggested. This algorithm is verified and the error analysis is confirmed through simulations. The application of this algorithm to EOTS will improve the operational capability by reducing the time which is required to find the target and support especially the flight in a night time flight and the poor weather condition.

• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.176-181
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• 2023

During cancer treatment, the patient's response to drugs appears differently at the cellular level. In this paper, an image-based cell phenotypic feature quantification and key feature selection method are presented to predict the response of patient-derived cancer cells to a specific drug. In order to analyze the viability characteristics of cancer cells, high-definition microscope images in which cell nuclei are fluorescently stained are used, and individual-level cell analysis is performed. To this end, first, image stitching is performed for analysis of the same environment in units of the well plates, and uneven brightness due to the effects of illumination is adjusted based on the histogram. In order to automatically segment only the cell nucleus region, which is the region of interest, from the improved image, a superpixel-based segmentation technique is applied using the fluorescence expression level and morphological information. After extracting 242 types of features from the image through the segmented cell region information, only the features related to cell viability are selected through the ReliefF algorithm. The proposed method can be applied to cell image-based phenotypic screening to determine a patient's response to a drug.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.339-346
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• 2000

Medical image is analyzed to get an anatomical information for diagnostics. Segmentation must be preceded to recognize and determine the lesion more accurately. In this paper, we propose automatic segmentation algorithm for MR brain images using T1-weighted, T2-weighted and PD images complementarily. The proposed segmentation algorithm is first, extracts cerebrum images from 3 input images using cerebrum mask which is made from PD image. And next, find 3D clusters corresponded to cerebrum tissues using scale filtering and 3D clustering in 3D space which is consisted of T1, T2, and PD axis. Cerebrum images are segmented using FCM algorithm with its initial centroid as the 3D cluster's centroid. The proposed algorithm improved segmentation results using accurate cluster centroid as initial value of FCM algorithm and also can get better segmentation results using multi spectral analysis than single spectral analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.1
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• pp.9-16
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• 2005

In this paper, we compare face recognition rate by distance change using Principal Component Analysis algorithm being input left and right image in stereo image. Change to YCbCr color space from RGB color space in proposed method and face region does detection. Also, after acquire distance using stereo image extracted face image's extension and reduce do extract robust face region, experimented recognition rate by using PCA algorithm. Could get face recognition rate of 98.61%(30cm), 98.91%(50cm), 99.05%(100cm), 99.90%(120cm), 97.31%(150cm) and 96.71%(200cm) by average recognition result of acquired face image. Therefore, method that is proposed through an experiment showed that can get high recognition rate if apply scale up or reduction according to distance.

• Journal of the Korean Society of Radiology
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• v.17 no.6
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• pp.843-849
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• 2023

This study attempted to determine the optimal algorithm after quantitatively analyzing noise, SNR, and CNR measurements by reconstructing four algorithms (Standard, Soft, Bone, and Detail) from head and neck CT images of patients who underwent implant surgery. As an analysis method, pixel values were calculated through the region of interest in the reconstructed image using the Image J program. For noise, SNR, and CNR, the region of interest was measured at the location of the pharynx, masseter muscle, and parotid gland in the image, and the mean and SD values were obtained. The values of SNR and CNR were calculated based on the given formula. As a result, the standard algorithm showed the lowest noise and the highest SNR. CNR was highest in the Soft algorithm, but showed no significant difference from the Standard algorithm. Therefore, it is believed that the Standard algorithm is the optimal algorithm for examining patients wearing intraoral implants in head and neck CT examinations. We hope that the data from this study will be used as basic data for image evaluation in head and neck CT examinations, and that the quality of images will be further improved through various algorithm changes. It is believed that this will be an opportunity to do so.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.210-218
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• 2002

This paper proposes an efficient separation method of the composite images by using independent component analysis(ICA) based on neural networks of the approximate learning algorithm. The Proposed learning algorithm is the fixed point(FP) algorithm based on Secant method which can be approximately computed by only the values of function for estimating the root of objective function for optimizing entropy. The secant method is an alternative of the Newton method which is essential to differentiate the function for estimating the root. It can achieve a superior property of the FP algorithm for ICA due to simplify the composite computation of differential process. The proposed algorithm has been applied to the composite signals and image generated by random mixing matrix in the 4 signal of 500-sample and the 10 images of $512{\times}512-pixel$, respectively The simulation results show that the proposed algorithm has better performance of the learning speed and the separation than those using the conventional algorithm based method. It also solved the training performances depending on initial points setting and the nonrealistic learning time for separating the large size image by using the conventional algorithm.

• Convergence Security Journal
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• v.19 no.5
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• pp.37-45
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• 2019

Video security systems change from analog based systems to network based CCTVs. Therefore, such network based systems are always exposed not only to threats of eavesdropping and hacking, but to personal damage or public organizations' damage due to image information leakage. Therefore, in order to solve the problem, this study conducts a comparative analysis on proposes the optimal ECC(Elliptic Curve Cryptography) scalar multiplication algorithms for image information protection in data communication process and thereby proposes the optimal operation algorithm of video security system.