• Journal of Digital Convergence
• /
• v.12 no.1
• /
• pp.359-364
• /
• 2014

In this paper, we propose a method for extracting a road area by using the mean-shift method and connected-component method. Mean-shift method is very effective to divide the color image by the method of non-parametric statistics to find the center mode. Generally, the feature points of road are extracted by using the information located in the middle and bottom of the road image. And it is possible to extract a road region by using this feature-point and the partitioned color image. However, if a road region is extracted with only the color information and the position information of a road image, it is possible to detect not only noise but also off-road regions. This paper proposes the method to determine the road region by eliminating the noise with the closing / opening operation of the morphology, and by extracting only the portion of the largest area using a connected-components method. The proposed method is simulated and verified by applying the captured road images.

• KIPS Transactions on Software and Data Engineering
• /
• v.4 no.7
• /
• pp.297-300
• /
• 2015

In medical ultrasonography, transit time and contrast enhancement patterns are considered as important parameters to analyze liver diseases. In many recent researches, time-intensity curves(TIC) have been used for calculating the transit time of the contrast agents. However, the intensity curve may include the variations which are caused by the micro-bubble effect of contrast agents. In this paper, we propose a complementary approach to diagnostic parameter extraction which utilizes a density information as well as the intensity data. The proposed technique improves the accuracy in extraction of the transit time and velocity of contrast agents for detection and characterization of focal liver lesions. Through the experiments using a set of clinical data, we show that the proposed methods can improve the reliability of the parametric image data.

• Journal of KIISE
• /
• v.42 no.7
• /
• pp.910-918
• /
• 2015

This paper presents a parameter visualization technique to overcome the limitation of the naked eye in contrast-enhanced ultrasonography. A method is also proposed to compensate for the distortion and noise in ultrasound image sequences. Meaningful parameters for diagnosing liver disease can be extracted from the dynamic patterns of the contrast enhancement in ultrasound images. The visualization technique can provide more accurate information by generating a parametric image from the dynamic data. Respiratory motions and noise from micro-bubble in ultrasound data may cause a degradation of the reliability of the diagnostic parameters. A multi-stage algorithm for respiratory motion tracking and an image enhancement technique based on the Markov Random Field are proposed. The usefulness of the proposed methods is empirically discussed through experiments by using a set of clinical data.

• Imaging Science in Dentistry
• /
• v.50 no.4
• /
• pp.339-346
• /
• 2020

Purpose: The purpose of this study was to evaluate the image quality, diagnostic efficacy, and radiation dose associated with the use of a cadmium telluride (CdTe) detector, compared to charge-coupled device (CCD) and complementary metal oxide semiconductor(CMOS) detectors. Materials and Methods: Lateral cephalographs of a phantom (type 1) composed of synthetic polymer filled with water and another phantom (type 2) composed of human skull macerated with polymer coating were obtained with CdTe, CCD, and CMOS detectors. Dosimeters placed on the type 2 phantom were used to measure radiation. Noise levels from each image were also measured. McNamara cephalometric analysis was conducted, the dentoskeletal configurations were assessed, and a subjective evaluation of image quality was conducted. Parametric data were compared via 1-way analysis of variance with the Tukey post-hoc test, with a significance level of 5%. Subjective image quality and dentoskeletal configuration were described qualitatively. Results: A statistically significant difference was found among the images obtained with the 3 detectors(P<0.05), with the lowest noise level observed among the images obtained with the CdTe detector and a higher subjective preference demonstrated for those images. For the cephalometric analyses, no significant difference (P>0.05) was observed, and perfect agreement was seen with regard to the classifications obtained from the images acquired using the 3 detectors. The radiation dose associated with the CMOS detector was higher than the doses associated with the CCD (P<0.05) and CdTe detectors(P<0.05). Conclusion: Considering the evaluated parameters, the CdTe detector is recommended for use in clinical practice.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.6
• /
• pp.2118-2125
• /
• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.43 no.1 s.307
• /
• pp.13-20
• /
• 2006

In this paper, we propose a motion control scheme of robot manipulators based on visual feedback under camera-in-hand configuration. The desired joint velocity and acceleration for motion control is made by the feature-based visual data in the outer loop. The control input for tracking feature points on the image plane uses robot kinematics dynamic. The proposed control input consists of the image feature and the joint velocity error to achieve robustness to the parametric uncertainty. The stability of the closed-loop system is proved by Lyapunov approach. Computer simulations and experiments on a two degree of freedom manipulator with 5 links are presented to illustrate the performance of proposed control system.

• Journal of the Korean Society of Visualization
• /
• v.12 no.2
• /
• pp.9-12
• /
• 2014

Conventional electrospray and air spray methods have the vulnerabilities of limited flow rate (throughput) and droplet size, respectively. Since high throughput with uniform size of droplet is required for various applications, an improved technique should be adopted. Here, we report a combined system of an air pressure and an electric field and evaluate the atomization performance of it. The air flow allowed applying high flow rate range and the electric field reinforced the atomization process to generate fine droplets. A correlation between two forces was investigated by comparing the droplet produced by each method. The atomized droplets were measured and visualized by image processing and a particle image velocimetry (PIV). The quantitative results were achieved from the parametric space and the effect of both forces was analyzed. The motion of charged droplets followed the outer electric field rather than the complex vortex in the shear layer so that the droplets accelerated directly toward the grounded collector.

• Korean Journal of Computational Design and Engineering
• /
• v.20 no.4
• /
• pp.375-384
• /
• 2015

Recently, there has been a gradual increase in the number of people who are interested in cycling, leading to an increasing number of cycling equipment consumers. However, many bicyclists get hurt because of their lack of knowledge about the right size of bicycle for their body. Although it is necessary for a rider to fit their bicycle to prevent injury, they reject a fitting service because of the long hours and high cost. In this study, we propose a bicycle fitting system that uses a depth camera to improve the limitations of existing manual fitting systems. With the defined formula, the system calculates the size of the bicycle using body image information extracted by a depth camera and visualizes a customized bicycle for a specific consumer. This system will not only save the customer time and money, but will prevent injury from the use of a bicycle that does not fit.

• Journal of Biosystems Engineering
• /
• v.38 no.2
• /
• pp.149-162
• /
• 2013

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

• The KIPS Transactions:PartB
• /
• v.15B no.6
• /
• pp.533-542
• /
• 2008

Background subtraction from video data is one of the most important task in various realtime machine vision applications. In this paper, a new scheme for background subtraction based on the hexagonal pixel sampling is proposed. Generally it has been found that hexagonal spatial sampling yields smaller quantization errors and remarkably improves the understanding of connectivity. We try to apply the hexagonally sampled image to the LBP based non-parametric background subtraction algorithm. Our scheme makes it possible to omit the bilinear pixel interpolation step during the local binary pattern generation process, and, consequently, can reduce the computation time. Experimental results revealed that our approach based on hexagonal spatial sampling is very efficient and can be utilized in various background subtraction applications.