• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.1-101
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• 2001

This paper describes a grid mapping for a vision based mobile robot in uncertain indoor environment. The map building is a prerequisite for navigation of a mobile robot and the problem of feature correspondence across two images is well known to be of crucial Importance for vision-based mapping We use a stereo matching algorithm obtained by singular value decomposition of an appropriate correspondence strength matrix. This new correspondence strength means a correlation weight for some local measurements to quantify similarity between features. The visual range data from the reconstructed disparity image form an occupancy grid representation. The occupancy map is a grid-based map in which each cell has some value indicating the probability at that location ...

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4176-4197
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• 2020

Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.141-149
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• 2023

Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.1
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• pp.1-8
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• 2003

Various methods have been studying to maintain and apply the multimedia inform abruptly increasing over all social fields, in recent years. For retrieval of still images, we is implemented content-based image retrieval system in this paper that make possible to retrieve similar objects from image database after segmenting query object from background if user request query. Query image is processed median filtering to remove noise first and then object edge is detected it by canny edge detection. And query object is segmented from background by using convex hull. Similarity value can be obtained by means of histogram intersection with database image after securing color histogram from segmented image. Also segmented image is processed gray convert and wavelet transform to extract spacial gray distribution and texture feature. After that, Similarity value can be obtained by means of banded autocorrelogram and energy. Final similar image can be retrieved by adding upper similarity values that it make possible to not only robust in background but also better correct object retrieval by using object segmentation method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.57-63
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• 2009

The rapid growth of computer-based image database, necessity of a system that can manage an image information is increasing. This paper presents a region-based image retrieval method using the combination of color(autocorrelogram), texture(CWT moments) and shape(Hu invariant moments) features. As a color feature, a color autocorrelogram is chosen by extracting from the hue and saturation components of a color image(HSV). As a texture, shape and position feature are extracted from the value component. For efficient similarity confutation, the extracted features(color autocorrelogram, Hu invariant moments, and CWT moments) are combined and then precision and recall are measured. Experiment results for Corel and VisTex DBs show that the proposed image retrieval algorithm has 94.8% Precision, 90.7% recall and can successfully apply to image retrieval system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.485-494
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• 2017

This study focuses on automatic image registration between multiple IR images using simple preprocessing method and modified local feature extraction algorithm. The input images were preprocessed by using the median and absolute value after histogram equalization, and it could be effectively applied to reduce the brightness difference value between images by applying the similarity of extracted features to the concept of angle instead of distance. The results were evaluated using visual and inverse RMSE methods. The features that could not be achieved by the existing local feature extraction technique showed high image matching reliability and application convenience. It is expected that this method can be used as one of the automatic registration methods between multi-sensor images under specific conditions.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.32-38
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• 1993

In this paper a effective classification method for character recognition is proposed. The existing classification methods select candidates by comparing an unknown input character, with all the standard patterns based on the similarity measur. The proposed method, however, groups similiar characters together and uses their average distance as representative value of the group. We divided the character region into several sub-region and applied ISODATA algorithm to partial vectors of each sub-region to anstruct appropriate number of groups. After computing the distance between partial feature vector and its mapping group, we could collect all the information of input character ultimately. The proposed method showed improvement in the processing speed and certainty in classification than the existing methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.9
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• pp.1269-1282
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• 1993

In this paper, we propose to method of recognizing printed chinese characters which combine the coventional deterministic methods and the neural networks. Firstly, we extract four directional vector of strokes from chinese characters. Secondly, we make the mesh of the center of gravity in the vector and then constitute the H x8 feature matrix using black pixel lenth from each meshs. This normalized feature matrix value offer as the input of neural network for classifying into the 14 character types. And this calssified character classify again into Busu group by the Busu recognizing neural network. Finally, we recognize each characters using the distance of similarity between input characters and reference characters. The usefulness of the proposed algorithm is evaluated by experimenting with recognizing the chinese characters.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.17-24
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• 2021

There is a problem that unknown paintings are sophisticated in the level of forgery, making it difficult for even experts to determine whether they are genuine or counterfeit. These problems can be suspected of forgery even if the genuine product is submitted, which can lead to a decline in the value of the work and the artist. To address these issues, in this paper, we propose a system to classify chromaticity data among extracted data through objective analysis into quadrants, extracting comparisons and intersections, and estimating authors of unknown paintings using XRF and hyperspectral spectrum data from corresponding points.