• Journal of the Korea Society of Computer and Information
• /
• v.12 no.1 s.45
• /
• pp.57-65
• /
• 2007

In the fast block matching algorithm. search patterns of different shapes or sizes and the distribution of motion vectors have a large impact on both the searching speed and the image qualify. In this paper, we propose a new fast block matching algorithm using the flat-hexagon search pattern that ate solved disadvantages of the diamond pattern search algorithm(DS) and the hexagon-based search algorithm(HEXBS). Our proposed algorithm finds mainly the motion vectors that not close to the center of search window using the flat-hexagon search pattern. Through experiments, compared with the DS and HEXBS, the proposed f)at-hexagon search algorithm(FHS) improves about $0.4{\sim}21.3%$ in terms of average number of search point per motion vector estimation and improves about $0.009{\sim}0.531dB$ in terms of PSNR(Peak Signal to Noise Ratio).

• Proceedings of the KIEE Conference
• /
• 2003.07d
• /
• pp.2372-2374
• /
• 2003

With 3-D vision measuring, camera calibration is necessary to calculate parameters accurately. Camera calibration was developed widely in two categories. The first establishes reference points in space, and the second uses a grid type frame and statistical method. But, the former has difficulty to setup reference points and the latter has low accuracy. In this paper we present an algorithm for camera calibration using perspective ratio of the grid type frame with different line widths. It can easily estimate camera calibration parameters such as focal length, scale factor, pose, orientations, and distance. But, radial lens distortion is not modeled. The advantage of this algorithm is that it can estimate the distance of the object. Also, the proposed camera calibration method is possible estimate distance in dynamic environment such as autonomous navigation. To validate proposed method, we set up the experiments with a frame on rotator at a distance of 1,2,3,4[m] from camera and rotate the frame from -60 to 60 degrees. Both computer simulation and real data have been used to test the proposed method and very good results have been obtained. We have investigated the distance error affected by scale factor or different line widths and experimentally found an average scale factor that includes the least distance error with each image. It advances camera calibration one more step from static environments to real world such as autonomous land vehicle use.

• Journal of Korean Neurosurgical Society
• /
• v.41 no.6
• /
• pp.371-376
• /
• 2007

Objective : The aim of this study was to investigate the usefulness of diffusion-weighted imaging [DWI] and apparent diffusion coefficiency [ADC] in distinguishing brain abscesses from cystic or necrotic brain tumors, which are difficult to be differentiated by conventional magnetic resonance imaging techniques. Methods : Seven patients with brain abscesses and ten patients with cystic brain tumors were studied from September 2003 to October 2005. Abscess, subdural empyema and ventriculitis were categorized to the abscess group and cystic or necrotic brain gliomas or metastatic brain tumors into the tumor group. Preoperative magnetic resonance images were performed in all patients and diffusion-weighted images and apparent diffusion coefficiency values of lesions were calculated directly from software of 1.5 tesla MRI [General Electrics, USA]. The ratio of the ADC of the lesion to contralateral regional ADC was also measured [relative ADC, rADC]. Results : The average ADC value of pyogenic abscesses group was $0.82+/-0.14{\times}10^{-3}\;[mean+/-S.D.]\;mm^2/s$ and mean rADC was 0.75. Cystic or necrotic areas had high ADC values [$2.49+/-0.79{\times}10^{-3}\;mm^2/s$, mean rADC=2.14]. ADC and rADC values of abscesses group showed about three times lower values than those of cystic or necrotic tumor group. Conclusion : This study results based on numerical comparison of signal intensities and quantitative analysis to distinguish between brain abscess and cystic or necrotic tumor, DWI and ADC mapping are thought to be very useful diagnostic tools.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.17 no.5
• /
• pp.39-53
• /
• 2007

The purpose of this paper is the development of a compensation algorithm by which the interoperability of fingerprint recognition can be improved among various different fingerprint sensor. In order to compensate for the different characteristics of fingerprint sensor, an initial evaluation of the sensors using both the ink-stamped method and the flat artificial finger pattern method was undertaken. This paper proposes Common resolution method and Relative resolution method for compensating different resolution of fingerprint images captured by disparate sensors. Both methods can be applied to image-level and minutia-level. In order to compensate the direction of minutiae in minutia-level, Unit vector method is proposed. The EER of the proposed method was improved by average 64.8% better than before compensation. This paper will make a significant contribution to interoperability in the system integration using different sensors.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.3
• /
• pp.83-95
• /
• 2022

This paper proposes a system that performs object detection and distance estimation for application to autonomous vehicles. Object detection is performed by a network that adjusts the split grid to the input image ratio using the characteristics of the recently actively used deep learning model YOLOv4, and is trained to a custom dataset. The distance to the detected object is estimated using a bounding box and homography. As a result of the experiment, the proposed method improved in overall detection performance and processing speed close to real-time. Compared to the existing YOLOv4, the total mAP of the proposed method increased by 4.03%. The accuracy of object recognition such as pedestrians, vehicles, construction sites, and PE drums, which frequently occur when driving to the city center, has been improved. The processing speed is approximately 55 FPS. The average of the distance estimation error was 5.25m in the X coordinate and 0.97m in the Y coordinate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.474-476
• /
• 2021

Glioblastoma is the most common brain malignancies arising from glial cells. Early diagnosis and treatment plan establishment are important, and cancer is diagnosed mainly through T1CE imaging through injection of a contrast agent. However, the risk of injection of gadolinium-based contrast agents is increasing recently. Region segmentation that marks cancer regions in medical images plays a key role in CAD systems, and deep neural network models for synthesizing new images are also being studied. In this study, we propose a model that simultaneously learns the generation of T1CE images and segmentation of cancer regions. The performance of the proposed model is evaluated using similarity measurements including mean square error and peak signal-to-noise ratio, and shows average result values of 21 and 39 dB.

• Korean Circulation Journal
• /
• v.54 no.1
• /
• pp.30-39
• /
• 2024

Background and Objectives: Intravascular ultrasound (IVUS) evaluation of coronary artery morphology is based on the lumen and vessel segmentation. This study aimed to develop an automatic segmentation algorithm and validate the performances for measuring quantitative IVUS parameters. Methods: A total of 1,063 patients were randomly assigned, with a ratio of 4:1 to the training and test sets. The independent data set of 111 IVUS pullbacks was obtained to assess the vessel-level performance. The lumen and external elastic membrane (EEM) boundaries were labeled manually in every IVUS frame with a 0.2-mm interval. The Efficient-UNet was utilized for the automatic segmentation of IVUS images. Results: At the frame-level, Efficient-UNet showed a high dice similarity coefficient (DSC, 0.93±0.05) and Jaccard index (JI, 0.87±0.08) for lumen segmentation, and demonstrated a high DSC (0.97±0.03) and JI (0.94±0.04) for EEM segmentation. At the vessel-level, there were close correlations between model-derived vs. experts-measured IVUS parameters; minimal lumen image area (r=0.92), EEM area (r=0.88), lumen volume (r=0.99) and plaque volume (r=0.95). The agreement between model-derived vs. expert-measured minimal lumen area was similarly excellent compared to the experts' agreement. The model-based lumen and EEM segmentation for a 20-mm lesion segment required 13.2 seconds, whereas manual segmentation with a 0.2-mm interval by an expert took 187.5 minutes on average. Conclusions: The deep learning models can accurately and quickly delineate vascular geometry. The artificial intelligence-based methodology may support clinicians' decision-making by real-time application in the catheterization laboratory.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.3
• /
• pp.75-88
• /
• 2009

This paper presents a video quality assessment method based on quality degradation factors of real-time multimedia streaming services. The video quality degradation is caused by video source compression and network states. In this paper, we propose a blocky metric on an image domain to measure quality degradation by video compression. In this paper, the proposed boundary strength index for the blocky metric is defined by ratio of the variation of two pixel values adjacent to $8{\times}8$ block boundary and the average variation at several pixels adjacent to the two boundary pixels. On the other hand, unnatural image movement caused by network performance deterioration such as jitter and delay factors can be observed. In this paper, a temporal-Jerkiness measurement method is proposed by computing statistics of luminance differences between consecutive frames and play-time intervals between frames. The proposed final Perceptual Video Quality Metric (PVQM) is proposed by consolidating both blocking strength and temporal-jerkiness. To evaluate performance of the proposed algorithm, the accuracy of the proposed algorithm is compared with Difference of Mean Opinion Score (DMOS) based on human visual system.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.43 no.3 s.309
• /
• pp.78-84
• /
• 2006

Our study aimed at quantitative assessment of a cleft palate nose deformity condition by analyzing the following parameters gathered from a photographic image of a cleft palate patient: (1) angle difference between two nostril axes, (2) center of the nostril and distance between two centers, (3) overlapped area of two nostrils, and (4) the overlapped area ratio of the two nostrils. A regression equation of doctor's grades was obtained using the eight parameters. Three plastic surgeons gave us the glades for the each photographic image by to increments with maximum grade of 100. The average reproducibility of the grades given by the three plastic surgeons and the three laymen using the developed program was $10.8{\pm}4.6%\;and\;7.4{\pm}1.8%$, respectively. Kappa values representing the degree of consensus of the plastic surgeons and the three laymen were 0.43 and 0.83, respectively. Correlation coefficient of the grades evaluated by the surgeons and obtained by the regression equation was 0.642 and that of the grades by the surgeons and by the neural network was 0.798. In conclusion, the developed neural network model provided us better reproducibility, much better consensus, and better correlation than doctor's subjective evaluation in addition to objectiveness and easy application.

• Journal of radiological science and technology
• /
• v.36 no.4
• /
• pp.299-304
• /
• 2013

Whole body bone scan has been used to confirm bone metastasis and follow-up study with radio isotope. However, if the factors related to $^{99m}Tc$ uptake and waiting time for study are inappropriate, it would be image of low quality. The purpose of present study was to investigate correlation between the evaluation index of renal function and uptake of radiopharmaceuticals. The population for this retrospective study consisted of 387 patients who underwent whole body bone scan between June 2012 and December 2012. As a result of quantitative and qualitative analysis, we were able to confirm that GFR of less than normal range and creatinine levels in blood of more than average are more likely to be under the mean uptake rate. As a result of analysis on the indicator affecting soft-tissue and bone uptake, the correlation of all elements was somewhat low. Also there are no statistically significances due to the other parameters we did not deal with. Therefore, further research on additional factors is needed for exact study and improvement of the image quality.