• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1315-1318
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• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• Journal of the Korea Society of Computer and Information
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• v.7 no.4
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• pp.204-210
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• 2002

The self-organizing feature map of Kohonen has disadvantage that needs too much input patterns in order to converge into the equilibrium state when it trains. In this paper we proposed the method of improving the convergence speed and rate of self-organizing feature map converting the interaction set into Dynamic Gaussian function. The proposed method Provides us with dynamic Properties that the deviation and width of Gaussian function used as an interaction function are narrowed in proportion to learning times and learning rates that varies according to topological position from the winner neuron. In this Paper. we proposed the method of improving the convergence rate and the degree of self-organizing feature map.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.321-328
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• 2004

Since National Geographic Information System was started, topographic maps have been made with computer aided editing of digital map, instead of etching map-size negative film. topographic mapping system's necessity is growing more and more, because digital map has changed into Ver.2.0 which include attributes of feature. On the basis of the previous study for analyzing correlation between the digital map feature code and the 1/5,000 topographic map specifications and trying to develop fundamental modules which will play a core role in topographic mapping system, in this study, we apply some 1/5,000 digital maps Ver.2.0 to topographic mapping system have implemented and try to analyze the result.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.103-111
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• 2021

This study addresses a low-cost lidar sensor-based glass feature extraction method for an accurate map representation using statistical moments, i.e. the mean and variance. Since the low-cost lidar sensor produces range-only data without intensity and multi-echo data, there are some difficulties in detecting glass-like objects. In this study, a principle that an incidence angle of a ray emitted from the lidar with respect to a glass surface is close to zero degrees is concerned for glass detection. Besides, all sensor data are preprocessed and clustered, which is represented using statistical moments as glass feature candidates. Glass features are selected among the candidates according to several conditions based on the principle and geometric relation in the global coordinate system. The accumulated glass features are classified according to the distance, which is lastly represented on the map. Several experiments were conducted in glass environments. The results showed that the proposed method accurately extracted and represented glass windows using proper parameters. The parameters were empirically designed and carefully analyzed. In future work, we will implement and perform the conventional SLAM algorithms combined with our glass feature extraction method in glass environments.

• Journal of the Korea Convergence Society
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• v.8 no.6
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• pp.29-36
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• 2017

In this paper, we propose a method for generating improved saliency map by learning multiple features to improve the accuracy and reliability of saliency map which has similar result to human visual perception type. In order to overcome the inaccurate result of reverse selection or partial loss in color based salient area estimation in existing salience map generation, the proposed method generates multi feature data based on learning. The features to be considered in the image are analyzed through the process of distinguishing the color pattern and the region having the specificity in the original image, and the learning data is composed by the combination of the similar protrusion area definition and the specificity area using the LAB color space based color analysis. After combining the training data with the extrinsic information obtained from low level features such as frequency, color, and focus information, we reconstructed the final saliency map to minimize the inaccurate saliency area. For the experiment, we compared the ground truth image with the experimental results and obtained the precision-recall value.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.56-68
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• 2022

In this paper, we propose a compression method for multi-resolution feature maps for VCM. The proposed compression method removes the redundancy between the channels and resolution levels of the multi-resolution feature map through PCA-based transformation. According to each characteristic, the basis vectors and mean vector used for transformation, and the transformation coefficient obtained through the transformation are compressed using a VVC-based coder and DeepCABAC. In order to evaluate performance of the proposed method, the object detection performance was measured for the OpenImageV6 and COCO 2017 validation set, and the BD-rate of MPEG-VCM anchor and feature map compression anchor proposed in this paper was compared using bpp and mAP. As a result of the experiment, the proposed method shows a 25.71% BD-rate performance improvement compared to feature map compression anchor in OpenImageV6. Furthermore, for large objects of the COCO 2017 validation set, the BD-rate performance is improved by up to 43.72% compared to the MPEG-VCM anchor.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.761-766
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• 2007

이 논문에서는 커널 Edge Map 방식의 얼굴의 특징점을 검출하는 방법과 Adaboost를 이용한 얼굴의 특징점을 검출하는 방법을 이용하여 좀 더 강인한 얼굴의 특징점을 검출해 낸다. 커널 Edge Map을 이용한 방법은 기존의 10개의 커널을 이용하여 검출된 Edge를 이용하지 않고 좀 더 빠르게 검출해내기 위해 2개의 커널을 이용하여 얼굴의 특징점을 검출해 낸다. 이렇게 만들어진 얼굴의 특징점 후보군들에서 Adaboost를 이용하여 좀 더 정확하고 빠른 특징점을 찾을 수 있게 된다. Adaboost를 이용한 방법은 각각의 특징점들을 오프라인 상에서 학습을 하고 실시간으로 특징점을 검출하는 방법을 사용하였다. Edge를 이용한 방법으로 이미지의 전처리를 하여 후보군을 찾고 그 후보군과 Adaboost를 이용한 후보군들의 조합으로 인해 좀 더 강인하게 얼굴의 특징점을 찾을 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.38.4-38
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• 2002

We propose a fast map-matching algorithm based on the length and the slope for the sequence of lines extracted from a laser range finder and a map. After finding two feature set from laser data and a map, the position and heading of the mobile robot can be determined exactly.

• Journal of KSNVE
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• v.10 no.2
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.583-589
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• 2013

In this paper, we proposed a system that efficiently provides support maps which includes the grid based terrain analysis information. To do this, we use the FDB which is defined as a GIS database that contains features with attributes attached to the features. The FDB is composed of a number of features and feature classes. In order to create support maps, it is necessary to classify feature classes that are associated with each support map and to search them in a grid map. The proposed system use a ontology model to classify semantically feature classes and the quad-tree data structure to find them in a grid map quickly. Therefore, our system is expected to be utilized for global path planning of UGV. In this paper, we show the possibility through an experimental implementation.