• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.3
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• pp.465-472
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• 2015

This paper presents a visual touch recognition for NUI(Natural User Interface) using Voronoi-tessellation algorithm. The proposed algorithms are three parts as follows: hand region extraction, hand feature point extraction, visual-touch recognition. To improve the robustness of hand region extraction, we propose RGB/HSI color model, Canny edge detection algorithm, and use of spatial frequency information. In addition, to improve the accuracy of the recognition of hand feature point extraction, we propose the use of Douglas Peucker algorithm, Also, to recognize the visual touch, we propose the use of the Voronoi-tessellation algorithm. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.89-90
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• 2019

본 논문에서는 ultrasound image Right Parasagittal Liver에 edge detection을 적용한 후, 특징점 검출 알고리즘인 Scale Invarient Feature Transfom(SIFT)를 이용하여 특징점의 위치를 살펴보도록 한다. edge detection 알고리즘으로는 Canny edge detection과 Prewitt edge detection을 적용하기로 한다.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.10
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• pp.768-776
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• 2006

Given an unstructured point set, we use an MLS (melting least-squares) approximation to estimate the local curvatures and their derivatives at a point by means of an approximation surface Then, we compute neighbor information using a Delaunay tessellation. feature points can then be detected as zero-crossings, and connected using curvature directions. Also this approach has a fast computation time than previous methods, which based on triangle meshes. We demonstrate our method on several large point-sampled models, rendered by point-splatting, on which the feature lines are rendered with line width determined from curvatures.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.210.2-210.2
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• 2005

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.95-101
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• 2011

In this paper, using pixel connectivity tracking feature to remove a doctor has been studied. Feature extraction method is a method using the crossing. However, by crossing a lot of feature extraction method sis a doctor. Extracted using the method of crossing the wrong feature to remove them from the downside and the eight pixels around the fork to trace if it satisfies the conditions in the actual feature extraction and feature conditions are not satisfied because the doctor was removed. To evaluate the performance using crossing methods and extracted using pixel connectivity trace was compared to the actual feature, the experimental results using pixel connectivity trace arcuate sentence, croissants sentence, sentence the defrost feature on your doctor about47%, respectively, 40%, 30%were found to remove.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.106-110
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• 2014

This paper proposed the face recognition method which can be effectively applied to the robot's vision system. The proposed algorithm is recognition using hue extraction and feature point. hue extraction was using difference of skin color, pupil color, lips color. Features information were extraction from eye, nose and mouth using feature parameters of the difference between the feature point, distance ratio, angle, area. Feature parameters fuzzified data with the data generated by membership function, then evaluate the degree of similarity was the face recognition. The result of experiment are conducted with frontal color images of face as input images the received recognition rate of 96%.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.331-335
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• 2008

In this paper, we parallelized a feature point extraction algorithm to reduce panorama image generation time in multi-core environment. While we compose a panorama image with several images, the step to extract feature points of each picture is needed to find overlapped region of pictures. To perform rapidly feature extraction stage which requires much calculation, we developed a parallel algorithm to extract feature points and examined the performance using CBE(Cell Broadband Engine) which is asymmetric multi-core architecture. As a result of the exam, the algorithm we proposed has a property of linear scalability-the performance is increased in proportion the number of processors utilized. In this paper, we will suggest how Image processing operation can make high performance result in multi-core environment.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.6
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• pp.929-936
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• 1990

This paper describes the method of the Korean character recognition by means of feature points extraction. Also, the preprocessing neural chip for noise elimination, smoothing, thinning and feature point extraction has been designs. The subpatterns were separated by means of advanced index algorithm using mask, and recognized by means of feature points classification. The separation of the Korean character subpatterns was abtained about 97%, and the recognition of the Korean characters was abtained about 95%. The preprocessing neural chip was simulated on SPICE and layouted by double CMOS 2\ulcorner design rule.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.113-118
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• 2024

Pursuance Sentiment Analysis on Twitter is difficult then performance it's used for great review. The present be for the reason to the tweet is extremely small with mostly contain slang, emoticon, and hash tag with other tweet words. A feature extraction stands every technique concerning structure and aspect point beginning particular tweets. The subdivision in a aspect vector is an integer that has a commitment on ascribing a supposition class to a tweet. The cycle of feature extraction is to eradicate the exact quality to get better the accurateness of the classifications models. In this manuscript we proposed Term Frequency-Inverse Document Frequency (TF-IDF) method is to secure Principal Component Analysis (PCA) with Naïve Bayes Classifiers. As the classifications process, the work proposed can produce different aspects from wildly valued feature commencing a Twitter dataset.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.859-861
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• 2003

In order to present a space in details, it is indispensable to acquire 3D shape and texture simultaneously from the same platform. 3D shape is acquired by Laser Scanner as point cloud data, and texture is acquired by CCD sensor. Positioning data is acquired by IMU (Inertial Measurement Unit). All the sensors and equipments are assembled on a hand-trolley. In this research, a method of integrating the 3D shape and texture for automated construction of Digital Surface Model is developed. This Digital Surface Model is applied for efficient feature extraction. More detailed extraction is possible , because 3D Digital Surface Model has both 3D shape and texture information.