• Journal of the Institute of Convergence Signal Processing
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• v.22 no.3
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• pp.141-148
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• 2021

In this paper, we propose an algorithm of welding bead detection and evaluation using geodesic active contour algorithm and high pass filter with image processing technique. The algorithm uses histogram equalization and high pass filter as gaussian filter to improve contrast. The image processing techniques smoothens the welding beads reduce the noise on an image. Then, the algorithm detects the welding bead area by applying the geodesic active contour algorithm and morphological ooperation. It also applies the balloon force that either inflates in, or deflates out the evolving contour for a better segmentation. After that, we propose a method for determining the quality of welding bead using effective length and width of the detected bead. In the experiments, our algorithm achieved the highest recall, precision, F-measure and IOU as 0.9894, 0.9668, 0.9780, and 0.8957 respectively. We compared the proposed algorithm with the conventional algorithms to evaluate the performance of the proposed algorithm. The proposed algorithm achieved better performance compared to the conventional ones with a maximum computational time of 0.6 seconds for segmenting and evaluating one welding bead.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.109-114
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• 2012

Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.367-370
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• 2015

This paper proposes an improve method of the detection performance of traffic lights for autonomous driving cars. Earlier detection methods used to adopt color thresholding, template matching and based learning maching methods, but its have some problems such as recognition rate decreasing, slow processing time. The proposed method uses both detection mask and morphological preprocessing. Firstly, input color images are converted to YCbCr image in order to strengthen its illumination, and horizontal edge components are extracted in the Y Channel. Secondly, the region of interest is detected according to morphological characteristics of the traffic lights. Finally, the traffic signal is detected based on color distributions. The proposed method showed that the detection rate and processing time improved rather than the conventional algorithm about some surrounding environments.

• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.239-246
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• 2005

Biologist must have to do 2DGE biological experiment for Protein Search and Analysis. This experiment coming into being 2 dimensional image. 2DGE (2D Gel Electrophoresis : two dimensional gel electrophoresis) image is the most widely used method for isolating of the objective protein by comparative analysis of the protein spot pattern in the gel plane. The process of protein spot analysis, firstly segment protein spots that are spread in 2D gel plane by image processing and can find important protein spots through comparative analysis with protein pattern of contrast group. In the algorithm which detect protein spots, previous 2DGE image analysis is applies gaussian fitting, however recently Watersheds region based segmentation algorithm, which is based on morphological segmentation is applied. Watersheds has the benefit that segment rapidly needed field in big sized image, however has under-segmentation and over-segmentation of spot area when gray level is continuous. The drawback was somewhat solved by marker point institution, but needs the split and merge process. This paper introduces a novel marker search of protein spots by watersheds-based hierarchical threshold, which can resolve the problem of marker-driven watersheds.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.410-418
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• 2023

Recently, advancements and commercialization in the field of maritime autonomous surface ships (MASS) has rapidly progressed. Concurrently, studies are also underway to develop methods for automatically surveying the condition of various on-board equipment remotely to ensure the navigational safety of MASS. One key issue that has gained prominence is the method to obtain values from analog gauges installed in various equipment through image processing. This approach has the advantage of enabling the non-contact detection of gauge values without modifying or changing already installed or planned equipment, eliminating the need for type approval changes from shipping classifications. The objective of this study was to identify a dynamically changing indicator needle within noisy images of analog gauges. The needle object must be identified because its position significantly affects the accurate reading of gauge values. An analog pressure gauge attached to an emergency fire pump model was used for image capture to identify the needle object. The acquired images were pre-processed through Gaussian filtering, thresholding, and morphological operations. The needle object was then identified through Hough Transform. The experimental results confirmed that the center and object of the indicator needle could be identified in images of noisy analog gauges. The findings suggest that the image processing method applied in this study can be utilized for shape identification in analog gauges installed on ships. This study is expected to be applicable as an image processing method for the automatic remote survey of MASS.

• Journal of Sensor Science and Technology
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• v.27 no.4
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• pp.216-220
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• 2018

In this paper, we present visual sensing techniques that can be used to teach a robot using a laser pointer. The light spot of an off-the-shelf laser pointer is detected and its movement is tracked on consecutive images of a camera. The three-dimensional position of the spot is calculated using stereo cameras. The light spot on the image is detected based on its color, brightness, and shape. The detection results in a binary image, and morphological processing steps are performed on the image to refine the detection. The movement of the laser spot is measured using two methods. The first is a simple method of specifying the region of interest (ROI) centered at the current location of the light spot and finding the spot within the ROI on the next image. It is assumed that the movement of the spot is not large on two consecutive images. The second method is using a Kalman filter, which has been widely employed in trajectory estimation problems. In our simulation study of various cases, Kalman filtering shows better results mostly. However, there is a problem of fitting the system model of the filter to the pattern of the spot movement.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.321-326
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• 2010

A method that can decide the existence and the severeness of flaws in ceramic materials through the use of non-destructive testing by image processing techniques, is proposed in this paper. The edges of the acquired image are first extracted using Sobel mask and the regions of the image are clustered using another mask after that. Histogram stretching is applied to each of the regions to enhance the image region-wise and objects are extracted by an edge following algorithm. Morphological information is incorporated to remove noise and detect flawed regions. The proposed method can detect flaws in the acquired images and the experimental results also supports that.

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.11-17
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• 2014

While safe and convenient, ultrasound imaging analysis is often criticized by its subjective decision making nature by field experts in analyzing musculoskeletal system. In this paper, we propose a new automatic method to extract muscle area using ART2 neural network based quantization. A series of image processing algorithms such as histogram smoothing and End-in search stretching are applied in pre-processing phase to remove noises effectively. Muscle areas are extracted by considering various morphological features and corresponding analysis. In experiment, our ART2 based Quantization is verified as more effective than other general quantization methods.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.261-267
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• 2007

The research about Intelligence Transport Systems(ITS) is actively studied for the traffic problem solution recently. Also, the various methods to detect vehicles moving in the roads are studied. This research using image processing technology is to give the drivers the road information quickly by developing Vehicle Detection System that detects through traffics. Purpose or this research is developing efficient algorithm to facilitate hardware composition. We use morphology method to extract corner points in the images captured by CCD camera. Also, the proposed algorithm detects vehicle's moving area by using motion vectors between corner points. The experiments of the proposed algorithm whose processing time was shortened show good results in vehicle detection on the live road images.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.125-130
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• 2006

The number of the living endothelial cells and the shape of those are very import clinical parameters for the evaluation of the quality of cornea. In this paper, we developed the automated endothelial cell counting and shape analysis algorithm for a confocal microscope. Since, the endothelial images from the confocal microscope has a non-uniform illumination and low contrast between cell boundaries and cell bodies, it is very difficult to segment the cells from the endothelial images. To cope with these difficulties, we proposed the new two stage image processing algorithm. At first stage algorithm, we used a high-pass filter and histogram equalization to compensate the non-uniform brightness pattern and a morphological filter and a watershed method are applied to detect the boundary of cells. From this stage, we could count the number of cells in an endothelial image. At second stage algorithm, we used a Voronoi diagram method to classify the shape of cells. This cell shape analysis and the percent of hexagonal cells are very sensitive in detecting the early endothelium damage. To evaluate the performance of the proposed system, we p개cessed seven endothelial images obtained using a confocal microscope. The proposed system correctly counted 95.5% cells and classified 92.0% of hexagonal cell shapes. This result is better than any others in this research area.