• Journal of IKEEE
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• v.19 no.4
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• pp.526-534
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• 2015

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 local shrinking and expanding operations, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where the 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 scheme is tested by recognition experiments on the handwritten numeral database CENPARMI, where SVM classifier with RBF kernel is used. The experimental results show the usefulness of our scheme and recognition rate of 99.10% is achieved.

• 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%.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.136-142
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• 2007

Recent researches in the recognition system have shown that SVM (Support Vector Machine) classifiers often have superior recognition rates in comparison to other classifiers. In this paper, we present the handwritten numeral recognition algorithm using SVM classifiers. The numeral features used in our algorithm are mesh features, directional features by Kirsch operators and concavity features, where first two features represent the foreground information of numerals and the last feature represents the background information of numerals. These features are complements each of the other. Since SVM is basically a binary classifier, it is required to construct and combine several binary SVMs to get the multi-class classifiers. We use two strategies for implementing multi-class SVM classifiers: "one against one" and "one against the rest", and examine their performances on the features used. The efficiency of our method is tested by the CENPARMI handwritten numeral database, and the recognition rate of 98.45% is achieved.

• Journal of the Korean Geographical Society
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• v.42 no.1 s.118
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• pp.27-40
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• 2007

The streams evolve to diverse forms influenced by various factors such as rock resistance tectonic process, sediments and discharge. This study focuses on erosion resistance of rocks among these factors. The morphology of plane and longitudinal profile has been analysed in upper and lower reach of 6 streams using GIS; Yeoryong-cheon, Heungjeong-cheon, Duhak-cheon, Daehwa-cheon, Namcheon-cheon, Guryong-cheon, having distinct bedrock types between upper and lower reach. While the basins of granite have gentle slope, low concavity and wide valley area, those of gneiss form steep slope, high concavity and narrow valley area. However, the basins of sedimentary rock make steep slope and high relief in main channel, the other features show some differences in each stream. Among the various morphological features, the indices on slope and concavity of main channel, drainage density, ratio of valley area, average slope and average relief of the basin which have clear differences between rocks in upper and lower reach are calculated to interpret the erosion resistance of rocks in upper and lower reach. As a result, the upper reaches composed of gneiss have the highest erosion resistance, sedimentary rocks in upper and lower reaches show moderate resistance, and granite reaches generally have the lowest resistance except the upper reaches bordered by sedimentary rock.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.818-823
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• 2008

In this paper, we propose a feature extraction method which extracts directional features of handwritten numerals by using the projection runlength. Our directional featrures are obtained from four directional images, each of which contains horizontal, vertical, right-diagonal and left-diagonal lines in entire numeral shape respectively. A conventional method which extracts directional features by using Kirsch masks generates edge-shaped double line directional images for four directions, whereas our method uses the projections and their runlengths for four directions to produces single line directional images for four directions. To obtain the directional projections for four directions from a numeral image, some preprocessing steps such as thinning and dilation are required, but the shapes of resultant directional lines are more similar to the numeral lines of input numerals. Four [$4{\times}4$] directional features of a numeral are obtained from four directional line images through a zoning method. By using a hybrid feature which is made by combining our feature with the conventional features of a mesh features, a kirsch directional feature and a concavity feature, higher recognition rates of the handwrittern numerals can be obtained. For recognition test with given features, we use a multi-layer perceptron neural network classifier which is trained with the back propagation algorithm. Through the experiments with the handwritten numeral database of Concordia University, we have achieved a recognition rate of 97.85%.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.309-314
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• 2015

One of typical methods for extracting facial features from face images may be snake. Although snake is simple and fast, performance is very much affected by the initial contour and the shape of object to be extracted. In this paper, the enhanced snake is proposed to extract better facial features from 6 lip and mouth images as snake point is added to the midpoint of snake segment. It is shown that RSD of the proposed method is about 2.8% to 5.8% less than that of Greedy snake about 6 test face images. Since lesser RSD is especially obtained for contours with highly concavity, the contour is more accurately extracted.

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.11-19
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• 2022

In this paper we present a new method for interactive segmentation of a triangle mesh by using the concavity-sensitive harmonic field and anisotropic geodesic. The proposed method only requires a single vertex in a desired feature region, while most of existing methods need explicit information on segmentation boundary. From the user-clicked vertex, a candidate region which contains the desired feature region is defined and concavity-senstive harmonic field is constructed on the region by using appropriate boundary constraints. An initial isoline is chosen from the uniformly sampled isolines on the harmonic field and optimal points on the initial isoline are determined as interpolation points. Final segmentation boundary is then constructed by computing anisotropic geodesics passing through the interpolation points. In experimental results, we demonstrate the effectiveness of the proposed method by selecting several features in various 3D models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.954-958
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• 1988

A jig-saw puzzle matching technique is proposed. Specifically, the geometric patterns of the puzzle pieces are firstly extracted using a boundary tracking algorithm at low resolution. And then, features of the extracted pieces to describe jig-saw puzzle pieces such as angles and distances between corner points, and convexity or concavity of a corner point are obtained from some corner points implying discontinuity of curvature of puzzle pieces' boundary. Finally, a boundary matching algorithm without a priori information of matched puzzle is proposed.

• Animal Systematics, Evolution and Diversity
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• v.35 no.3
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• pp.123-135
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• 2019

Elaphognathia monodi (Gurjanova, 1936) and Elaphognathia kikuchii Nunomura, 1992 are newly reported based on the materials collected from Wando Island and Jeju Island in Korea, respectively. Elaphognathia monodi is distinguished by the following characteristics: the lateral margin of the cephalon is narrowing posteriorly; the frontal border is slightly concave and has a small mediofrontal process, a pair of superior frontolateral processes, and a pair of inferior frontolateral processes. Elaphognathia kikuchii can be distinguished by the following characteristic features: the lateral margin of the cephalon is narrowing anteriorly; the frontal border has a small mediofrontal process and twelve pairs of simple setae along with concavity.