• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3239-3267
• /
• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• Journal of Digital Convergence
• /
• v.12 no.11
• /
• pp.281-288
• /
• 2014

A method for edge detection using K-means clustering is proposed in this paper. The method is performed through there steps. Histogram equalizing is applied to the image for the uniformed intensity distribution. Pixels are clustered by K-means clustering technique. Then Sobel mask is applied to detect edges. Experiments showed that this method detected edges better than conventional method.

• Journal of the Korean institute of surface engineering
• /
• v.54 no.5
• /
• pp.278-284
• /
• 2021

Corrosion of steel plate is common phenomenon which results in the gradual destruction caused by a wide variety of environments. Corrosion monitoring is the tracking of the degradation progress for a long period of time. Corrosion on steel plate appears as a discoloration and any irregularities on the surface. In this study, we developed a quantitative evaluation method of the rust formed on steel plate by using k-means clustering from the corroded area in a given image. The k-means clustering for automated corrosion detection was based on the GrabCut segmentation and Gaussian mixture model(GMM). Image color of the corroded surface at cut-edge area was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.12a
• /
• pp.251-254
• /
• 2001

본 논문에서는 Self Organizing Map을 이용하여 fMRI data를 분석해 보았다. fMRl (functional Magnetic Resonance Imaging)는 인간의 뇌에 대한 비 침투적 연구 방법 중 최근에 각광받고 있는 것이다. Motor task를 수행하고 있는 피험자로부터 image data를 얻어내어 SOM을 적용하여 clustering한 결과 motor cortex 영역이 뚜렷하게 clustering 되었음을 알 수 있었다.

• ETRI Journal
• /
• v.32 no.4
• /
• pp.596-602
• /
• 2010

This paper proposes a computationally efficient learning-based super-resolution algorithm using k-means clustering. Conventional learning-based super-resolution requires a huge dictionary for reliable performance, which brings about a tremendous memory cost as well as a burdensome matching computation. In order to overcome this problem, the proposed algorithm significantly reduces the size of the trained dictionary by properly clustering similar patches at the learning phase. Experimental results show that the proposed algorithm provides superior visual quality to the conventional algorithms, while needing much less computational complexity.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.1
• /
• pp.1-11
• /
• 2006

Data mining technique is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. It has been widely used in many applications, such that pattern analysis or recognition, data analysis, image processing, market research on off-line or on-line and so on. We analyze Gyeongnam social indicator survey data by 2001 using twostep clustering technique for environment information. The twostep clustering is classified as a partitional clustering method. We can apply these twostep clustering outputs to environmental preservation and improvement.

• The KIPS Transactions:PartA
• /
• v.13A no.5 s.102
• /
• pp.465-472
• /
• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Korean Journal of Remote Sensing
• /
• v.19 no.2
• /
• pp.135-147
• /
• 2003

This study used the multistage hierarchical clustering image classification to analyze the satellite images for the land-cover types of an area in the Korean peninsula. The multistage algorithm consists of two stages. The first stage performs region-growing segmentation by employing a hierarchical clustering procedure with the restriction that pixels in a cluster must be spatially contiguous, and finally the whole image space is segmented into sub-regions where adjacent regions have different physical properties. Without spatial constraints for merging, the second stage clusters the segments resulting from the previous stage. The image classification of hierarchical clustering, which merges step-by step two small groups into one large one based on the hierarchical structure of digital imagery, generates a hierarchical tree of the relation between the classified regions. The experimental results show that the hierarchical tree has the detailed information on the hierarchical structure of land-use and more detailed spectral information is required for the correct analysis of land-cover types.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.237-244
• /
• 2021

Clustering is a most powerful un-supervised machine learning techniques for division of instances into homogenous group, which is called cluster. This Clustering is mainly used for generating a good quality of cluster through which we can discover hidden patterns and knowledge from the large datasets. It has huge application in different field like in medicine field, healthcare, gene-expression, image processing, agriculture, fraud detection, profitability analysis etc. The goal of this paper is to explore both hierarchical as well as partitioning clustering and understanding their problem with various approaches for their solution. Among different clustering K-means is better than other clustering due to its linear time complexity. Further this paper also focused on data mining that dealing with high-dimensional datasets with their problems and their existing approaches for their relevancy

• Journal of Korea Multimedia Society
• /
• v.11 no.8
• /
• pp.1169-1178
• /
• 2008

Most of information in Web2.0 is constructed by users and can be classified by tags which are also constructed and added by users. However, as we known, referring by the related works such as automatic tagging techniques and tag cloud's construction techniques, the research to be classified information and resources by tags effectively is to be given users which is still up to the mark. In this paper, we propose and implement a clustering system that does mapping each other according to relationships of the resource's tags collected from Web and then makes the mapping result into clusters to retrieve images. Tn addition, we analyze our system's efficiency by comparing our proposed system's image retrieval result with the image retrieval results searched by Flickr website.