• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1853-1858
• /
• 2021

O-rings fill the gaps between mechanical parts. Until now, the sorting of defective products has been performed visually and manually, so classification errors often occur. Therefore, a camera-based defect classification system without human intervention is required. However, a binarization process is required to separate the required region from the background in the camera input image. In this paper, an adaptive binarization technique that considers the surrounding pixel values is applied to solve the problem that single-threshold binarization is difficult to apply due to factors such as changes in ambient lighting or reflections. In addition, the convex hull technique is also applied to compensate for the missing pixel part. And the learning model to be applied to the separated region applies the residual error-based deep learning neural network model, which is advantageous when the defective characteristic is non-linear. It is suggested that the proposed system through experiments can be applied to the automation of O-ring defect detection.

• Smart Structures and Systems
• /
• v.31 no.5
• /
• pp.469-483
• /
• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.4
• /
• pp.101-109
• /
• 2014

It is an essential element for the establishment of image processing related systems to find the exact contour from the image of an arbitrary object. In particular, if a vision system is established to inspect the products in the automated production process, it is very important to detect the contours for standardized shapes such lines and curves. In this paper, we propose a prototype adaptive dynamic contour model, p-Snake with improved contour extraction algorithms by adding the prototype energy. The proposed method is to find the initial contour by applying the existing Snake algorithm after Sobel operation is performed for prototype analysis. Next, the final contour of the object is detected by analyzing prototypes such as lines and circles, defining prototype energy and using it as an additional energy item in the existing Snake function on the basis of information on initial contour. We performed experiments on 340 images obtained by using an environment that duplicated the background of an industrial site. It was found that even if objects are not clearly distinguished from the background due to noise and lighting or the edges being insufficiently visible in the images, the contour can be extracted. In addition, in the case of similarity which is the measure representing how much it matches the prototype, the prototype similarity of contour extracted from the proposed p-ACM is superior to that of ACM by 9.85%.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.3
• /
• pp.25-35
• /
• 2014

This paper proposes a method of boosting-based classification for the purpose of real-time face detection. The proposed method uses depth images to ensure strong performance of face detection in response to changes in lighting and face size, and uses the depth difference feature to conduct learning and recognition through the I-MCTBoost classifier. I-MCTBoost performs recognition by connecting the strong classifiers that are constituted from weak classifiers. The learning process for the weak classifiers is as follows: first, depth difference features are generated, and eight of these features are combined to form the weak classifier, and each feature is expressed as a binary bit. Strong classifiers undergo learning through the process of repeatedly selecting a specified number of weak classifiers, and become capable of strong classification through a learning process in which the weight of the learning samples are renewed and learning data is added. This paper explains depth difference features and proposes a learning method for the weak classifiers and strong classifiers of I-MCTBoost. Lastly, the paper presents comparisons of the proposed classifiers and the classifiers using conventional MCT through qualitative and quantitative analyses to establish the feasibility and efficiency of the proposed classifiers.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.8
• /
• pp.35-44
• /
• 2014

This paper proposes a 3D pointing interface that is designed for the efficient application of a hand mouse. The proposed method uses depth images to secure high-quality results even in response to changes in lighting and environmental conditions and uses the normal vector of the palm of the hand to perform 3D pointing. First, the hand region is detected and tracked using the existing conventional method; based on the information thus obtained, the region of the palm is predicted and the region of interest is obtained. Once the region of interest has been identified, this region is approximated by the plane equation and the normal vector is extracted. Next, to ensure stable control, interpolation is performed using the extracted normal vector and the intersection point is detected. For stability and efficiency, the dynamic weight using the sigmoid function is applied to the above detected intersection point, and finally, this is converted into the 2D coordinate system. This paper explains the methods of detecting the region of interest and the direction vector and proposes a method of interpolating and applying the dynamic weight in order to stabilize control. Lastly, qualitative and quantitative analyses are performed on the proposed 3D pointing method to verify its ability to deliver stable control.

• KIPS Transactions on Software and Data Engineering
• /
• v.2 no.10
• /
• pp.713-722
• /
• 2013

This paper proposes a method of using depth information to detect the hand region in real-time based on the cascade method. In order to ensure stable and speedy detection of the hand region even under conditions of lighting changes in the test environment, this study uses only features based on depth information, and proposes a method of detecting the hand region by means of a classifier that uses boosting and cascading methods. First, in order to extract features using only depth information, we calculate the difference between the depth value at the center of the input image and the average of depth value within the segmented block, and to ensure that hand regions of all sizes will be detected, we use the central depth value and the second order linear model to predict the size of the hand region. The cascade method is applied to implement training and recognition by extracting features from the hand region. The classifier proposed in this paper maintains accuracy and enhances speed by composing each stage into a single weak classifier and obtaining the threshold value that satisfies the detection rate while exhibiting the lowest error rate to perform over-fitting training. The trained classifier is used to classify the hand region, and detects the final hand region in the final merger stage. Lastly, to verify performance, we perform quantitative and qualitative comparative analyses with various conventional AdaBoost algorithms to confirm the efficiency of the hand region detection algorithm proposed in this paper.

• The KIPS Transactions:PartB
• /
• v.11B no.3
• /
• pp.327-336
• /
• 2004

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments if not stable and therefore intensity variation from uncontrolled lights gives many roubles for applying directly NGC as pattern matching algorithm in this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are preyed from experiments using real images.

• Journal of the Korean Society of Radiology
• /
• v.13 no.2
• /
• pp.209-216
• /
• 2019

The purpose of this study was to suggest the optimum ambient illumination level for proper visualization in image inspection and reading on CRT and LCD monitors used for ultrasound and reading. The evaluators were divided into 4 groups: 20 (Ultra-sonographer: 20 groups (4 groups: ultra-sonographer, 1-5 years, 5 ultra-sonographers, 6 to 10 years, 5 ultra-sonographers, 11 to 15 years, The subjects were 32 questions. The evaluation method was image evaluation of ultrasonic soft copy images for 30 seconds per 10, 25, 100Lux ambient illumination. The evaluation results were evaluated as 6 points (Normal = Definitely no lesion), 2 points = possibly not a lesion, 3 points = probably not a lesion, 4 points = possibly a lesion, 5 points = probably a lesion, 6 points = Definitely a lesion). In this study, the results of ROC analysis according to ambient light illumination reading softcopy images used for lesion detection of all ultrasound images showed the highest sensitivity, specificity, and AUC results at 10Lux. It was found that optimal use of 10Lux for ambient light illumination would provide optimal detection of lesions in ultrasound soft copy images. Based on the future research data, it will be presented as basic data for designing ambient light brightness of ultrasound imaging laboratory and reading room.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.306-315
• /
• 2021

This study analyzed the effects of light-shielding curtains and halogens on spectrum when acquiring hyperspectral images in a greenhouse. The image data of tarp (1.4*1.4 m, 12%) with 30 degrees of angles was achieved three times with four conditions depending on 14 heights using the automatic image acquisition system installed in the greenhouse at the department of Southern Area of National Institute of Crop Science. When the image was acquired without both a light-shielding curtain and halogen lamp, there was a difference in spectral tendencies between direct light and shadow parts on the base of 550 nm. The average coefficient of variation (CV) for direct light and shadow parts was 1.8% and 4.2%, respective. The average CV value was increased to 12.5% regardless of shadows. When the image was acquired only used a halogen lamp, the average CV of the direct light and shadow parts were 2 .6% and 10.6%, and the width of change on the spectrum was increased because the amount of halogen light was changed depending on the height. In the case of shading curtains only used, the average CV was 1.6%, and the distinction between direct light and shadows disappeared. When the image was acquired using a shading curtain and halogen lamp, the average CV was increased to 10.2% because the amount of halogen light differed depending on the height. When the average CV depending on the height was calculated using halogen and light-shielding curtains, it was 1.4% at 0.1m and 1.9% at 0.2 m, 2 .6% at 0.3m, and 3.3% at 0.4m of height, respectively. When hyperspectral imagery is acquired, it is necessary to use a shading curtain to minimize the effect of shadows. Moreover, in case of supplementary lighting by using a halogen lamp, it is judged to be effective when the size of the object is less than 0.2 m and the distance between the object and the housing is kept constant.

• Korean Institute of Interior Design Journal
• /
• v.23 no.2
• /
• pp.40-50
• /
• 2014

The purpose of this study was to investigate how brand identity expressional elements in cosmetic road shops' facades would affect the brand awareness of consumers while extracting those brand identity expressional elements observed in the facades of the cosmetic road shops. In order to achieve the research goal, the study used Q methodology, a method to measure subjectivity. The results have been summarized as follows. (1) The elements to express the brand identity found in the facades of the cosmetic road shops were observed to be two-dimensional expressional elements, and they should include a symbol, a logo, a signboard, materials to express an image (products, models) and a brand color. As for the three-dimensional expressional elements, they were a building (form, materials, pattern), decorations (lean-to roof, canopy, sculptures, lighting, screen) and a display window (focusing on products, visuality or the inside of a shop). (2) The findings of the analyses on the brand awareness using Q methodology have been presented as follows. (1) When multiple identity expressional elements which would be associated with each other are used, the brand awareness gets increased relatively efficiently. (2) In case of men, they would perceive a brand more easily through those formative expressional elements such as a form of a building. (3) In case of women, they would perceive a brand more conveniently through those visual expressional elements such as a brand color. (3) In conclusion, the study figured out that, among the brand identity expressional elements, the one which would influence the brand awareness most would be (1) the brand color, followed by (2) the building-form, (3) the lean-to roof, (4) the display window and (5) the logo. Based upon what has been learned so far, the study confirmed that when it comes to securing the brand awareness in the market, cosmetic companies should, first, realize how important it is to make good use of the two-dimensional (visual) expressional element, the brand color, and the three-dimensional expressional element, the form of the building, together before they even try to design facades of their shops on the streets.