• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.327-334
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• 2024

In this paper, we developed a system with a near-infrared LED light source with a wavelength of 950nm to acquire palm vein images and a white LED light source to acquire palm creases based on Raspberry Pi. In addition, we implemented a unique pattern-extractable image processing technology that can prevent counterfeiting and enhance security of mixed creases and palmprints through image pre-processing (Gray scaling, Histogram Equalization, Blurring, Thresholding, Thinning) for the acquired vein and palm images, and secured a source technology that can be used in a security-enhanced system.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.621-631
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• 1996

It is said that robot can be used for multi-purpose use by changing end effector or/and visual sensor with its software. In this study, it was investigated what multi-purpose robot for fruit-production was using a tomato harvesting robot and a robot to work in vineyard. Tomato harvesting robot consisted of manipulator, end-effector, visual sensor and traveling device. Plant training system of larger size tomato is similar with that of cherry-tomato. Two end-effectors were prepared for larger size tomato and cherry-tomato fruit harvesting operations, while the res components were not changed for the different work objects. A color TV camera could be used for the both work objects, however fruit detecting algorithm and extracted features from image should be changed. As for the grape-robot , several end-effector for harvesting , berry thinning , bagging and spraying were developed and experimented after attaching each end-effector to manipulator end. The manipulator was a polar coordinate type and had five degrees of freedom so that it could have enough working space for the operations. It was observed that visual sensor was necessary for harvesting, bagging and berry-thinning operations and that spraying operation requires another sensor for keeping certain distance between trellis and end-effector. From the experimental results, it was considered that multi-operations by the same robot could be appropriately done on the same or similar plant training system changing some robot components . One of the important results on having function of multi-operation was to be able to make working period of the robot longer.

• Journal of the Korean Society of Radiology
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• v.2 no.1
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• pp.23-29
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• 2008

The present study proposed a method that dissolves ultrasonographic images into multiple resolutions using wavelet conversion and a boundary detection filter and improves the quality of ultrasonographic images through boundary detection filtering. In order to reduce noises and strengthen edges, the proposed method adjusted selectivity coefficient by area step by step from a low resolution image obtained from wavelet converted images to a high resolution image and performed edge filtering in consideration of direction. Through this method, we generated a selective low pass filtering effect in areas except edges by decreasing the wavelet coefficient for pixels in spot areas, improved continuity by smoothing edges in the tangential direction, and enhanced contrast by thinning in the normal direction. Through an experiment, we compared the filtering method using a non linear anisotropic expansion model and the filtering method using wavelet contraction structure in single resolution.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.40-47
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• 2011

Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, $R^2$ values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1315-1318
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• 2004

Based on fuzzy 2-mean classification and template matching method, we propose a new algorithm to detect the edges of an image. In the algorithm, fuzzy 2-mean classification can classify all pixels in the mask into two clusters whatever the mask in the dark or light region; and template matching not only determines the edge's direction, but also thins the detected edge by a set of inference rules and, by the way, reduces the impulse noises.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.900-904
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• 1991

In this paper, our study for the recognition of Hand-written Korean characters, Arabic numbers and alphabets by neural netwoks. This System extracts feature of character by using the MESH feature point of handwritten character, Arabic numbers and alphabets. To reduce the input image data, features are extracted from each input images. A MLP(multi-layer perceptron) with one hidden layer was trained with a modified BEP(back error propagation) algorithm. This method extracts feature sets of the characters directly from the scanner and can enhance computation speed without using the special preprocesses such as size normalization, smoothing, and thinning.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.900-903
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• 1996

The objective of this paper is to make the weighted graph map for path planning using the ultrasonic sensor measurements that are acquired when an A.M.R (autonomous mobile robot) explores the unknown circumstance. First, The A.M.R navigates on unknown space with wall-following and gathers the sensor data from the environments. After this, we constructs the occupancy grid map by interpreting the gathered sensor data to occupancy probability. For the path planning of roadmap method, the weighted graph map is extracted from the occupancy grid map using morphological image processing and thinning algorithm. This methods is implemented on an A.M.R having a ultrasonic sensor.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.11a
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• pp.655-658
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• 2003

The crack of concrete structure plays an important role in the durability and safety of structure. Therefore, the features such as width, length, and direction of that must be measured periodically. The conventional method of measurement of cracks is manually sketched, however. it takes a fairly long time and lacks quantitative objectivity. This study proposes the algorithm to extract and analyze cracks automatically. The proposed algorithm is composed of two sub-algorithms. The extraction algorithm includes elimination of effect due to light, binarization. and noise reduction. The analysis algorithm includes thinning process, labeling, and calculation of crack width, length, and direction. The test to demonstrate the algorithm is fulfilled using the images of cracks on real concrete surface.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.346-355
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• 2002

This study will offer multimodal recognition instead of an existing monomodal bioinfomatics by using face, lips, to improve the accuracy of recognition. Each bioinfomatics vector can be found by the following ways. For a face, the feature is calculated by principal component analysis with wavelet multiresolution. For a lip, a filter is used to find out an equation to calculate the edges of the lips first. Then by using a thinning image and least square method, an equation factor can be drawn. A voice recognition is found with MFCC by using mel frequency. We＇ve sorted backpropagation neural network and experimented with the inputs used above. Based on the experimental results we discuss the advantage and efficiency.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.34-36
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• 2009

This paper proposes an effective lane detection algorithm using hugh transform and lane model. The proposed lane detection algorithm includes two major components, i.e., lane marks segmentation and an exact lane extraction using a novel postprocessing technique. The first step is to segment lane marks from background images using HSV color model. Then, a novel postprocessing is used to detect an exact lane using Hugh transform and lane models(linear and curved lane models). The postprocessing consists of three parts, i.e, thinning process, Hugh Transform and filtering process. We divide input image into three regions of interests(ROIs). Based on lane curve function(LCF), we can detect an exact lane from various extracted lane lines. The lane models(linear and curved lane mode]) are used in order to judge whether each lane segment is fit or not in each ROIs. Experimental results show that the proposed scheme is very effective in lane detection.