• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.3
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• pp.84-95
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• 2005

This paper presents solution heuristics to solving optimal multiple-viewpoint location problems that are based on topographic features. The visibility problem is to maximise the viewshed area for a set of viewpoints on digital elevation models (DEM). For this analysis, five areas are selected, and fundamental topographic features (peak, pass, and pit) are extracted from the DEMs of the study areas. To solve the visibility problem, at first, solution approaches based on the characteristics of the topographic features are explored, and then, a benchmark test is undertaken that solution performances of the solution methods, such as computing times, and visible area sizes, are compared with the performances of traditional spatial heuristics. The feasibility of the solution methods, then, are discussed with the benchmark test results. From the analysis, this paper can conclude that fundamental topographic features based solution methods suggest a new sight of visibility analysis approach which did not discuss in traditional algorithmic approaches. Finally, further research avenues are suggested such as exploring more sophisticated selection process of topographic features related to visibility analysis, exploiting systematic methods to extract topographic features, and robust spatial analytical techniques and optimization techniques that enable to use the topographic features effectively.

• Journal of KIISE
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• v.44 no.12
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• pp.1313-1318
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• 2017

Aggregating local features in a single vector is a fundamental problem in an image search. In this process, the image search process can be speeded up if binary features which are extracted almost two order of magnitude faster than gradient-based features are utilized. However, in order to utilize the binary features in an image search, it is necessary to study the techniques for clustering binary features to generate binary visual words. This investigation is necessary because traditional clustering techniques for gradient-based features are not compatible with binary features. To this end, this paper studies the techniques for clustering binary features for the purpose of generating binary visual words. Through experiments, we analyze the trade-off between the accuracy and computational efficiency of an image search using binary features, and we then compare the proposed techniques. This research is expected to be applied to mobile applications, real-time applications, and web scale applications that require a fast image search.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1833-1836
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• 2008

Recently as the micro surface features become higher and diverse in their shapes, the releasing of the molded features becomes more crucial for manufacturing of the micro patterned products. The higher aspect ratio of the features or more complex shape of the features results in larger releasing force, elongation or cohesive failure of the features during the releasing. Another issue would be the uniformity of the released surface features after molding, especially for applications with large area surface. The micro patterned optical film, one of typical applications for micro surface features, consists of two layers, the thermoplastic base film and the micro formed UV resin layer. Therefore two interfaces are typically involved during the forming of this micro featured film; one is between the base film and the UV resin and another is between the resin and the pattern master. To improve the releasing of the molded surface features, the adhesive characteristic was investigated at these two interfaces. A PET film was used as a base film and two UV curable resins with different surface energy were prepared for different adhesiveness. Also the two different pattern masters were employed; one is made from brass-copper alloy and fabricated with PMMA. The adhesiveness at each interface was measured for some combinations of these base film, UV resins and the masters and the effect of this adhesiveness on the releasing was investigated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2761-2768
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• 2010

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by projection runlength, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our feature sets was tested by recognition experiments on the handwritten numeral database CENPARMI, where we used SVM with RBF kernel as a classifier. The experimental results showed that each combination of two or three features gave a better performance than a single feature. This means that each single feature works with a different discriminating power and cooperates with other features to enhance the recognition accuracy. By using the composite feature of the three features, we achieved a recognition rate of 98.90%.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.8
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• pp.343-348
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• 2019

In recent years, extractive summarization systems based on end-to-end deep learning models have become popular. These systems do not require human-crafted features and adopt data-driven approaches. However, previous related studies have shown that linguistic analysis features such as part-of-speeches, named entities and word's frequencies are useful for extracting important sentences from a document to generate a summary. In this paper, we propose an extractive summarization system based on deep neural networks using conventional linguistic analysis features. In order to prove the usefulness of the linguistic analysis features, we compare the models with and without those features. The experimental results show that the model with the linguistic analysis features improves the Rouge-2 F1 score by 0.5 points compared to the model without those features.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.40.2-40.2
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• 2013

Although galaxy mergers are thought to play an important role in forming elliptical galaxies, mergers in galaxy clusters have drawn less attention compared to mergers in field environments because galaxies with high peculiar velocities are unlikely to merge with each other. However, comparable fractions of merger features in cluster galaxies have been reported from deep imaging of Abell clusters, suggesting the relevance of mergers in the transformation of cluster early-type galaxies (Sheen et al. 2012). As a more direct approach to understanding the origin of tidal features in clusters, we perform hydrodynamic re-simulations on a cluster of galaxies. Based on mock observation images of the simulated cluster galaxies, we construct and analyze the cluster early-type galaxy sample in a consistent manner with Sheen et al. 2012. We find that the fraction of tidal feature from the simulated cluster is comparable to that of the observation. Evolutionary history of the galaxies with merger features shows that most of the mergers responsible for the merger features in the present originate from outside the cluster more than 2Gyrs ago. We also find that many of the galaxies with tidal features show correlations with subgroups in the cluster. All these results suggest that merger features in the cluster are due to preprocessing before accretion into the cluster.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.64-79
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• 2017

Identifying disease genes from human genome is a critical task in biomedical research. Important biological features to distinguish the disease genes from the non-disease genes have been mainly selected based on traditional feature selection approaches. However, the traditional feature selection approaches unnecessarily consider many unimportant biological features. As a result, although some of the existing classification techniques have been applied to disease gene identification, the prediction performance was not satisfactory. A small set of the most important biological features can enhance the accuracy of disease gene identification, as well as provide potentially useful knowledge for biologists or clinicians, who can further investigate the selected biological features as well as the potential disease genes. In this paper, we propose a new stepwise random forests (SRF) approach for biological feature selection and disease gene identification. The SRF approach consists of two stages. In the first stage, only important biological features are iteratively selected in a forward selection manner based on one-dimensional random forest regression, where the updated residual vector is considered as the current response vector. We can then determine a small set of important biological features. In the second stage, random forests classification with regard to the selected biological features is applied to identify disease genes. Our extensive experiments show that the proposed SRF approach outperforms the existing feature selection and classification techniques in terms of biological feature selection and disease gene identification.

• Proceedings of the KSRS Conference
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• v.1
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• pp.181-183
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• 2006

Digital topographic map in Korea contains layers of spatial and attribute data for 8 land features such as railroads, watercourses, roads, buildings and etc. Some of the layers such as building and forest don't include any information about height, which can be just prepared by interpretation of remote sensed data or field survey. LiDAR(Light Detection And Ranging) data using active pulse and digital camera provides data about height and form of land features. LiDAR data can be used not only to extract the outline of land features but also to estimate the height. This study presents technical availability for extraction and estimation of land feature's outline and height using LiDAR data which composes of natural and artificial land features, and digital aerial photograph which was taken simultaneously with the LiDAR. The estimated location, outline and height of land features were compared with the field survey data, and we could find that LiDAR data and digital aerial photograph can be a useful source for estimating the height of land features as well as extracting the outline.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.63-69
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• 2013

A feature-based image recognition method, using features of an object, can be performed faster than a template matching technique. Invariant feature-based panoramic image generation, an application of image recognition, requires large amount of time to match features between two images. This paper proposes a speed-up method of feature matching using feature strength information. Our algorithm extracts features in images, computes their feature strength information, and selects strong features points which are used to match the selected features. The strong features can be referred to as meaningful ones than the weak features. In the experiments, it was shown that our method speeded up over 40% of processing time than the technique without using feature strength information.