• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.55-62
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• 2005

In this paper, we propose the face recognition system using HNMA(Hippocampal Neuron Modeling Algorithm) which can remodel the cerebral cortex and hippocampal neuron as a principle of a man's brain in engineering, then it can learn the feature-vector of the face images very fast and construct the optimized feature each image. The system is composed of two parts. One is feature-extraction and the other is teaming and recognition. In the feature extraction part, it can construct good-classified features applying PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis) in order. In the learning part, it cm table the features of the image data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in the dentate gyrus region and remove the noise through the associate memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are face changes, pose changes and low quality image. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• 한국데이터정보과학회:학술대회논문집
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• 2003.05a
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• pp.77-88
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• 2003

Cluster analysis has been widely used in many applications, such that data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It is more fast than any traditional clustering method and maintains accuracy. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.217-226
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• 2005

Cluster analysis has been widely used in many applications, such as data analysis, pattern recognition, image processing, etc. But clustering requires many hours to get clusters that we want, because it is more primitive, explorative and we make many data an object of cluster analysis. In this paper we propose a new clustering method, 'Clustering algorithm using a center of gravity for grid-based sample'. It reduces running time by using grid-based sample and keeps accuracy by using representative point, a center of gravity.

• Computers and Concrete
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• v.27 no.2
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• pp.175-184
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• 2021

The incorporation of pozzolans to Portland cement pastes adds value in the development of new materials for the construction industry. This study presents a new computational method, complementary to the pozzolanic identification by compressive strength at 28 days method, for supporting the validation of pozzolanic mortars for non-structural purposes. An algorithm capable of classifying the pixels of micrographs of specimens fragments was developed. Therefore, comparative analyses were generated from fractional Gaussian representations in four intervals of the same amplitude that indicated the predispositions to form larger void indices (intervals 1 and 2). The results showed that the computational method indicators are in accordance with the physical and chemical indicators.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.850-853
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• 2017

In this paper, we propose an image analysis system based on omnidirectional image and object tracking image display using super wide angle camera. In order to generate spherical images, the projection process of converting from two wide-angle images to the equirectangular panoramic image was performed and the spherical image was expressed by converting rectangular to spherical coordinate system. Object tracking was performed by selecting the desired object initially, and KCF(Kernelized Correlation Filter) algorithm was used so that robust object tracking can be performed even when the object's shape is changed. In the initial dialog, the file and mode are selected, and then the result is displayed in the new dialog. If the object tracking mode is selected, the ROI is set by dragging the desired area in the new window.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.67-73
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• 2011

The image retrieval generally shows the same or similar images to a query image as a result. In the case of rotated image, however, its performance tends to be debased significantly. We propose a method to ensure a reliable image retrieval of rotated images as follows; First, to obtain feature points of query/DB images by Harris Corner Detector; and then, utilizing the feature points, to find the object's axis and query/DB images into rotation invariant images with Principal Components Analysis algorithm. We have experimented with 6,000 natural images which are 256 pixels in diameter. They are 1,000 Wang's images and their rotated images by $30^{\circ}$, $45^{\circ}$, $90^{\circ}$, $135^{\circ}$ and $180^{\circ}$. The simulation results show that the proposed method retrieves rotated images more effectively than the conventional method.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.379-388
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• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.525-532
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• 2018

Recently, due to the advancements of sensor network technologies and camera technologies, there are increasing needs to effectively monitor the environment in a region that is difficult to access by using the visual sensor network that combines these two technologies. Since the image captured by the visual sensor reflects the natural phenomenon as it is, the quality of the image may deteriorate depending on the weather or time. In this paper, we propose an algorithm to improve the contrast of images using the characteristics of images obtained from visual sensors. In the proposed method, we first set the region of interest and then analyzes the change of the color value of the region of interest according to the brightness value of the image. The contrast of an image is improved by using the high contrast image of the same object and the analysis information. It is shown by experimental results that the proposed method improves the contrast of an image by restoring the color components of the low contrast image simply and accurately.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.4
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• pp.148-156
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• 2009

This paper presents a new image rejection algorithm based on the analysis of the distortion of a single-frequency continuous-wave (CW) signal due to the I/Q mismatch. Existing methods estimated the gain mismatch and phase mismatch on RF receivers and compensated them However, this paper shows that the circular trajectory of a single-frequency CW signal is distorted elliptic-type trajectory due to the I/Q mismatch. Utilizing the analysis, we propose a I/Q mismatch compensation method. It has two processing steps. In the first processing step, the generated signal is rotated to align the major axis of the elliptic-type trajectory diagram with the x-axis. In the second processing step, the Q-channel signal in the regenerated signal is scaled to align the regenerated signal with the transmitted single-frequency CW signal. Simulation results show that a receiver using the proposed image rejection algorithm can achieve an image rejection ratio of more than 70dB. And, simulation results show that the bit error rate performances of receivers using the proposed image rejection algorithm are almost the same as those of conventional coherent demodulators, even in fading channels.