• The Journal of the Acoustical Society of Korea
• /
• v.27 no.2
• /
• pp.57-65
• /
• 2008

Setting parameters of Ultrasonic scanners influence the quality of ultrasonic images. In order to obtain optimized images sonographers need to understand the effects of the setting parameters on ultrasonic images. The present study considered typical four parameters including TGC (Time Gain Control), Gain, Frequency, DR (Dynamic Range). LCS (low contrast sensitivity) was chosen to quantitatively compare the quality of the images. In the present experiment LCS targets of a standard ultrasonic test phantom (539, ATS, USA) were imaged using a clinical ultrasonic scanner (SA-9000 PRIME, Medison, Korea). Altering the settings in the parameters of the ultrasonic scanner, 6 LCS target images (+15 dB, +6 dB, +3 dB, -3 dB, -6 dB, -15 dB) to each setting were obtained, and their LCS values were calculated. The results show that the mean pixel value (LCS) is the highest at the max setting in TGC, mid to max in gain and pen mode in frequency and 40-66 dB in DR. Among all images, the image being the highest in LCS was obtained at the setting of DR 40 dB. It is expected that the results will be of use in setting the parameters when ultrasonically examining masses often clinically found In either solid lesions (similar to +15, +6, +3 dB targets) or cystic lesions (similar to -15, -6, -3 dB targets).

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.677-687
• /
• 2017

In this paper, we propose the use of hierarchical classification for winter crop mapping based on satellite imagery. A hierarchical classification is a classifier that maps input data into defined subsumptive output categories. This classification method can reduce mixed pixel effects and improve classification performance. The methodology are illustrated focus on winter cropsin Gimje city, Jeonbuk with Landsat-8 imagery. First, agriculture fields were extracted from Landsat-8 imagery using Smart Farm Map. And then winter crop fields were extracted from agriculture fields using temporal Normalized Difference Vegetation Index (NDVI). Finally, winter crop fields were then classified into wheat, barley, IRG, whole crop barley and mixed crop fields using signature from Unmanned Aerial Vehicle (UAV). The results indicate that hierarchical classifier could effectively identify winter crop fields with an overall classification accuracy of 98.99%. Thus, it is expected that the proposed classification method would be effectively used for crop mapping.

• Journal of Advanced Navigation Technology
• /
• v.18 no.2
• /
• pp.151-157
• /
• 2014

Recently UAV(unmanned aerial vehicle) is frequently used not only for military purpose but also for civil purpose. UAV automatically navigates following the coordinates input in advance using GPS information. However it is impossible when GPS cannot be received because of jamming or external interference. In order to solve this problem, we propose a real-time segmentation and classification algorithm for the specific regions from UAV image in this paper. We use the super-pixels algorithm using graph-based image segmentation as a pre-processing stage for the feature extraction. We choose the most ideal model by analyzing various color models and mixture color models. Also, we use support vector machine for classification, which is one of the machine learning algorithms and can use small quantity of training data. 18 color and texture feature vectors are extracted from the UAV image, then 3 classes of regions; river, vinyl house, rice filed are classified in real-time through training and prediction processes.

• Journal of KIISE
• /
• v.42 no.10
• /
• pp.1294-1302
• /
• 2015

A lot of image quality assessment metrics that can precisely reflect the human visual system (HVS) have previously been researched. The Structural SIMilarity (SSIM) index is a remarkable HVS-aware metric that utilizes structural information, since the HVS is sensitive to the overall structure of an image. However, SSIM fails to deal with color difference in terms of the HVS. In order to solve this problem, the Structural and Hue SIMilarity (SHSIM) index has been selected with the Hue, Saturation, Intensity (HSI) model as a color space, but it cannot reflect the HVS-aware color difference between two color images. In this paper, we propose a new image quality assessment method for a color image by using a CIE Lab color space. In addition, by using a support vector machine (SVM) classifier, we also propose an optimization system for applying optimal metric according to the types of distortion. To evaluate the proposed index, a LIVE database, which is the most well-known in the area of image quality assessment, is employed and four criteria are used. Experimental results show that the proposed index is more consistent with the other methods.

• Journal of KIISE:Software and Applications
• /
• v.32 no.8
• /
• pp.769-780
• /
• 2005

This paper addresses a novel application of meshes to analyzing the deformation patterns of 2D signals. The proposed mesh is distinguished form the previous models in that it includes simulated charges in each node that interact with external charges comprising an input pattern. Therelaxation of the mesh given an input is carried out by any of the well-known numerical integration techniques. The result of the relaxation is a deformed mesh. This Paper provides four criterion functions for measuring the pattern deformation. A set of trained meshes was created from the simple average of target patterns. Experimental results show that these measures, although highly intuitive, are not good enough to capture the amount and characteristics of pattern deformation. If more sophisticated measures are found and incorporated into the relaxation process, we expect that a better and high-performance mesh framework is realized.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.6
• /
• pp.227-233
• /
• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.8
• /
• pp.159-165
• /
• 2015

In this paper, we designed and tested of the passive millimeter-wave imaging system in near range. The proposed passive millimeter-wave imaging system consists two parts. The first part is a 94 GHz band millimeter imaging sensor which is coupled to an antenna, two LNAs, and a diode detector. The second part is a control unit. The control unit is consists of the 2-axes Cartesian robot, the data acquisition (DAQ), and imaging program. The 2-axes Cartesian robot should be able to scan a 2-D image of the metalic tools, IC card and plastic objects, with a raster scanning method. The passive millimeter-wave image of $20{\times}20$ pixels is acquired within less than 60s, and is immediately displayed and stored for post processing.In order to improve the image quality, interpolation methods are applied.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.21 no.4
• /
• pp.512-517
• /
• 2011

In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.

• International Journal of Highway Engineering
• /
• v.10 no.3
• /
• pp.19-31
• /
• 2008

Video image processing systems(VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on wide-area detection, i.e., multi-lane surveillance algorithm provide traffic parameters with single camera such as flow and velocity, as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. The objective of this research was to relate traffic safety to VIPS tracking and this paper has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, volumes, speed, and occupancy rate as well as traffic information via tripwire image detectors. Also the developed system has been verified by comparing with commercial VIP detectors.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
• /
• 2001.04a
• /
• pp.184-184
• /
• 2001

캘린더령은 지펼의 표면을 평활하게 하고, 두께를 감소시켜 균일하게 하는 역할을 한다. 그러나 이는 필연적으로 불투명도와 같은 광학적 성질과 인장강도 등의 강도적 성질 의 저하를 유발한다. 따라서 캘린더링 공정변수인 온도, 압력, 속도 등이 종이의 물성에 미 치는 영향을 정확하게 파악하는 것은 캘린더령에 따라 발생할 수 있는 물성 저하를 최소화 하기 위해 필수적으로 요청된다. 본 연구에서는 최근들어 저평량화에 대한 관심이 증가하면 서 그 중요성이 더해지고 있는 불투명도가 캘린더링에 따라 변화되는 양상을 분석하기 위해 서 화상분석 기법을 이용하여 종이의 두께방향 밀도 변이를 평가하고 밀도변이와 불투명도 와의 상관관계를 해석코자 하였다. 또 캘린더링에 따른 불투명도를 저하를 최소화시키기 위 한 캘린더링 조건을 모색하였다. 캘린더링에 의해 발생하는 종이의 두께 변형은 두께방향의 위치에 따라 다르게 나 타난다. 이러한 종이의 두께 방향으로 발생하는 밀도 변이와 이에 따른 불투명도 변화를 평 가하기 위하여 동일한 평량의 종이를 캘린더령 조건을 달리하여 두께방향 밀도변이가 다른 시편을 준비하고 두께 방향 단면을 SEM으로 촬영하였다. 이후 화상분석기를 통해 단면을 이치화하고, 각 픽셀의 흑백 값을 구해 CD방향으로 평균을 내어 두께 방향에 대한 밀도 변 이를 평가하였다. 그 결과 압력보다는 온도를 높여 캘린더링한 경우 종이의 두께 방향 밀도 경사가 커진다는 사실을 확인할 수 있었다. 이는 고온에 의해 표층이 고밀화되고 상대적으 로 내부가 별크해졌기 때문이다. 이러한 밀도 변이가 종이의 광학적 성질인 불투명도에 미 치는 영향을 구명하기 위해서 캘린더링 전후에 두께 및 불투명도를 측정하여 5% 유의수준 에서 회귀분석을 실시하였다. 밀도경사를 지닌 종이의 불투명도를 이론적으로 해석하기 위해 다층 모델을 가정하 고 각 층의 비광산란계수(5)와 비광흡수계수(k)를 달리 부여하고 Kubelka-Munk 이론을 근 거로 하여 이론적 불투명도를 계산하였다. 불투명도에 대한 분석를 통해 동일한 두께 변형 을 가지는 샘플에 대해서 압력보다는 온도를 증가시켜 두께를 감소시키는 캘린더링 처리가 불투명도의 저하를 최소화한다는 것을 확인하였다.