• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.564-579
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• 2008

It is often challenging to evaluate local spatial association among onedimensional vectors generally representing paired-location events where two points are physically or functionally connected. This is largely because of complex process of such geographic phenomena itself and partially representational complexity. This paper addresses an alternative way to identify spatially autocorrelated paired-location events (or vectors) at a local scale. In doing so, we propose a statistical algorithm combining univariate point pattern analysis for evaluating local clustering of origin-points and similarity measure of corresponding vectors. For practical use of the suggested method, we present an empirical application using transactions data in a local housing market, particularly recorded from 2004 to 2006 in Franklin County, Ohio in the United States. As a result, several locally characterized similar transactions are identified among a set of vectors showing various local moves associated with communities defined.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.1-7
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• 2006

Linear stability analysis of 2-dimensional wall jet is conducted by using parabolized stability equation (PSE). Wall jet is found to be modelled well by boundary layer approximation except for the neighborhood of the nozzle exit, and the introduction of local similarity variable makes the streamwise basic flow Reynolds number independent. Stability characteristics of the wall jet obtained

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.116-125
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• 2019

The fire should extinguish as soon as possible because it causes economic loss and loses precious life. In this study, we propose a new atypical fire and smoke detection algorithm using deep learning and color histogram of fire and smoke. First, input frame images obtain from the ONVIF surveillance camera mounted in factory search motion candidate frame by motion detection algorithm and mean square error (MSE). Second deep learning (Faster R-CNN) is used to extract the fire and smoke candidate area of motion frame. Third, we apply a novel algorithm to detect the fire and smoke using color histogram algorithm with local area motion, similarity, and MSE. In this study, we developed a novel fire and smoke detection algorithm applied the local motion and color histogram method. Experimental results show that the surveillance camera with the proposed algorithm showed good fire and smoke detection results with very few false positives.

• International Journal of Contents
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• v.11 no.2
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• pp.15-19
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• 2015

In this paper, we propose an efficient document layout analysis algorithm that includes table detection. Typical methods of document layout analysis use the height and gap between words or columns. To correspond to the various styles and sizes of documents, we propose an algorithm that uses the mean value of the distance transform representing thickness and compare with components in the local area. With this algorithm, we combine a table detection algorithm using the same feature as that of the text classifier. Table candidates, separators, and big components are isolated from the image using Connected Component Analysis (CCA) and distance transform. The key idea of text classification is that the characteristics of the text parallel components that have a similar thickness and height. In order to estimate local similarity, we detect a text region using an adaptive searching window size. An improved adaptive run-length smoothing algorithm (ARLSA) was proposed to create the proper boundary of a text zone and non-text zone. Results from experiments on the ICDAR2009 page segmentation competition test set and our dataset demonstrate the superiority of our dataset through f-measure comparison with other algorithms.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.8
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• pp.837-841
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• 2010

This paper proposes an effective similarity measure for content-based image retrieval using MPEG-7 DCD. The proposed method can measure the similarity of images with the percentage of dominant colors extracted from images. As the result of experiments, we achieved a significant improvement of 18.92% with global DCD and 47.22% with local DCD in ANMRR than the result by QHDM. This result shows that the proposed method is an effective similarity measure for content-based image retrieval. Especially, our method is useful for region-based image retrieval.

• The Journal of the Korea Contents Association
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• v.5 no.3
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• pp.175-181
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• 2005

Recent measurements of local-area and wide-area traffic have shown that network traffic exhibits at a wide range of scales-Self-similarity. Self-similarity is expressed by long term dependency, this is contradictory concept with Poisson model that have relativity short term dependency. Therefore, first of all for design and dimensioning of next generation communication network, traffic model that are reflected burstness and self-similarity is required. Here self-similarity can be characterized by Hurst parameter. In this paper, when different many data traffic being integrated under various environments is arrived to communication network, Hurst Parameter's change is analyzed and compared with simulation results.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.1
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• pp.49-54
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• 2016

Regarding global and local factors of a set of features, a given single image or multiple images is a common approach in image processing. This paper introduces an application of an adaptive version of non-local filter whose original version searches non-local similarity for removing noise. Since most images involve texture partner in both foreground and background, extraction of signified regions with texture is a challenging task. Aiming to the detection of visual attention regions for images with texture, we present the contrast analysis of image patches located in a whole image but not nearby with assistance of the adaptive filter for estimation of non-local divergence. The method allows extraction of signified regions with texture of images of wild life. Experimental results for a benchmark demonstrate the ability of the proposed method to deal with the mentioned challenge.

• Journal of KIISE:Software and Applications
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• v.37 no.4
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• pp.305-310
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• 2010

In this paper, we propose a heuristic algorithm for motion correspondence based on a point series similarity. A point series is a sequence of points which are sorted in the ascending order of their x-coordinate values. The proposed algorithm clusters the points of a previous frame based on their local adjacency. For each group, we construct several potential point series by permuting the points in it, each of which is compared to the point series of the following frame in order to match the set of points through their similarity based on a proximity constraint. The longest common subsequence between two point series is used as global information to resolve the local ambiguity. Experimental results show an accuracy of more than 90% on two image sequences from the PETS 2009 and the CAVIAR data sets.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.960-966
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• 2017

In this paper, we proposed an algorithm that can extract lesion by inputting a medical image. Feature points are extracted using SIFT algorithm to extract genetic training of medical image. To increase the intensity of the feature points, the input image and that raining image are matched using vector similarity and the lesion is extracted. The vector similarity match can quickly lead to lesions. Since the direction vector is generated from the local feature point pair, the direction itself only shows the local feature, but it has the advantage of comparing the similarity between the other vectors existing between the two images and expanding to the global feature. The experimental results show that the lesion matching error rate is 1.02% and the processing speed is improved by about 40% compared to the case of not using the feature point intensity information.