• Journal of information and communication convergence engineering
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• v.4 no.4
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• pp.174-179
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• 2006

In this paper we present a text detection method inspired by wavelet packet analysis and improved fuzzy clustering algorithm(IAFC).This approach assumes that the text and non-text regions are considered as two different texture regions. The text detection is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multi scale features, we adapt the improved fuzzy clustering algorithm based on the unsupervised learning rule. The results show that our text detection method is effective for document images scanned from newspapers and journals.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.717-728
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• 2011

An efficient method for the unsupervised classification of high resolution imagery is suggested in this paper. It employs pixel-linking and merging based on the adjacency graph. The proposed algorithm uses the neighbor lines of 8 directions to include information in spatial proximity. Two approaches are suggested to employ neighbor lines in the linking. One is to compute the dissimilarity measure for the pixel-linking using information from the best lines with the smallest non. The other is to select the best directions for the dissimilarity measure by comparing the non-homogeneity of each line in the same direction of two adjacent pixels. The resultant partition of pixel-linking is segmented and classified by the merging based on the regional and spectral adjacency graphs. This study performed extensive experiments using simulation data and a real high resolution data of IKONOS. The experimental results show that the new approach proposed in this study is quite effective to provide segments of high quality for object-based analysis and proper land-cover map for high resolution imagery of urban area.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.233-235
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• 2005

This paper presents an unsupervised change detection methodology designed for the detection of landslide areas. The proposed methodology consists of two analytical steps: one for multi-temporal segmentation and the other for automatic selection of thresholding values. By considering the conditions of landslide occurrences and the spectral behavior of multi-temporal remote sensing images, some specific procedures are included in the analytical steps mentioned above. The effectiveness and applicability of the methodology proposed here were illustrated by a case study of the Gangneung area, Korea. The case study demonstrated that the proposed methodology could detect about $83\%$ of landslide occurrences.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.428-431
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• 2004

A multi-stage hierarchical clustering technique, which is an unsupervised technique, has been proposed in this paper for classifying the hyperspectral data .. The multistage algorithm consists of two stages. The 'local' segmentor of the first stage performs region-growing segmentation by employing the hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous. The 'global' segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage, using a context-free similarity measure. This study applied the multistage hierarchical clustering method to the data generated by band reduction, band selection and data compression. The classification results were compared with them using full bands.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.1-8
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• 2021

Prostate cancer is one of the most diagnosed malignancies found across the world today. American cancer society in recent research predicted that over 174,600 new prostate cancer cases found and nearly 31,620 death cases recorded. Researchers are developing modest and accurate methodologies to detect and diagnose prostate cancer. Recent work has been done in radiology to detect prostate tumors using ultrasound imaging and resonance imaging techniques. Transrectal ultrasound and Magnetic resonance images of the prostate gland help in the detection of cancer in the prostate gland. The proposed paper is based on comparison and analysis between two novel image segmentation approaches. Seed region growing and cluster based image segmentation is used to extract the region from trans-rectal ultrasound prostate and MR prostate images. The region of extraction represents the abnormality area that presents in men's prostate gland. Detection of such abnormalities in the prostate gland helps in the identification and treatment of prostate cancer

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.421-430
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• 2010

Image segmentation technique is becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification to extract object regions of interest within images. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Improved Seeded Region Growing (ISRG) and Region merging. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained multi-spectral edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying ISRG to consider spectral and edge information. Finally the region merging process, integrating region texture and spectral information, was carried out to get the final segmentation result. The accuracy assesment was done using the unsupervised objective evaluation method for evaluating the effectiveness of the proposed method. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

• Broadcasting and Media Magazine
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• v.25 no.2
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• pp.44-51
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• 2020

동영상 프레임 내 객체 영역들을 배경으로부터 분할하는 기술인 동영상 객체 분할(video object segmentation)은 다양한 컴퓨터 비전 분야에 활용 가능한 연구 분야이다. 최근, 동영상 객체 분할과 관련된 연구 내용으로 CVPR, ICCV, ECCV의 컴퓨터 비전 최우수 학회에 매년 20편 가까이 발표될 정도로 많은 관심을 받고 있다. 동영상 객체 분할은 사용자가 제공하는 정보에 따라 비지도(unsupervised) 동영상 객체 분할, 준지도(semi-supervised) 동영상 객체 분할, 인터렉티브(interactive) 동영상 객체 분할의 세 카테고리로 분류할 수 있다. 본 고에서는 최근 연구가 활발하게 수행되고 있는 비지도 동영상 객체 분할과 준지도 동영상 객체 분할 연구의 최신 동향에 대해 소개하고자 한다.

• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.69-86
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• 1991

An unsupervised region based image segmentation algorithm implemented with a pyramid structure has been developed. Rather than depending on thraditional local splitting and merging of regions with a similarity test of region statistics, the algorithm identifies the homogenous and boundary regions at each level of pyramid, then the global parameters of esch class are estimated and updated with values of the homogenous regions represented at the level of the pyramid using the mixture distribution estimation. The image is then classified through the pyramid structure. Classification results obtained for both simulated and SPOT imagery are presented.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.17-34
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• 2007

Organizations have realized the importance of CRM. To obtain the maximum possible lifetime value from a customer base, it is critical that customer data is analyzed to understand patterns of customer response. As customer databases assume gigantic proportions due to Internet and e-commerce activity, data-mining-based market segmentation becomes crucial for understanding customers. Here we raise a question and some issues of using single SOM approach for clustering while proposing multiple self-organizing maps approach. This methodology exploits additional themes on the attributes that characterize customers in a typical CRM system. Since this additional theme is usually ignored by traditional market segmentation techniques we here suggest careful application of SOM for market segmentation.