• 제어로봇시스템학회논문지
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• 제14권8호
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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%.

• 전기학회논문지
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• 제58권12호
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• pp.2529-2531
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• 2009

A simple multimodal fingerprint recognition method based on two types of feature vectors such as minutiae points and directional features is proposed, where Directional Filter Bank (DFB) is used to extract directional features. Experimental results show that the proposed method can effectively combine minutiae- and DFB-based methods and produce a better matching capability in the poor quality fingerprint image.

• 전기전자학회논문지
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• 제19권4호
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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.

• 제어로봇시스템학회논문지
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• 제18권9호
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• pp.887-893
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• 2012

In this paper, we propose a new approach to extract directional features based on directional patterns of each region in fingerprint images. The proposed approach computes the center of gravity to extract features from fingerprint images of various shapes. According to it, we divide a fingerprint image into four regions and compute the directional values of each region. To extract directional features of each region from a fingerprint image, we spilt direction values of ridges in a region into 18 classes and compute frequency distribution of each region. Through the result of our experiment using FVC2002 DB database acquired by electronic devices, we show that directional features are effectively extracted from various fingerprint images of exceptional inputs which lost all or part of singularities. To verify the performance of the proposed approach, we explained the process to model Arch, Left, Right and Whorl class using the extracted directional features of four regions and analyzed the classification result.

• 산업기술연구
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• 제20권B호
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• pp.207-211
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• 2000

For efficient massive image retrieval, an image retrieval requires that several important objectives are satisfied, namely: automated extraction of features, efficient indexing and effective retrieval. In this work, we present a technique for extracting the 4-dimension directional feature. By directional detail, we imply strong directional activity in the horizontal, vertical and diagonal direction present in region of the image texture. This directional information also present smoothness of region. The 4-dimension feature is only indexed in the 4-D space so that complex high-dimensional indexing can be avoided.

• 한국정보통신학회논문지
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• 제14권12호
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• pp.2761-2768
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• 2010

본 논문에서는 숫자의 전경특징과 배경특징을 이용하고 SVM 분류기를 사용하여 오프라인 필기체 숫자인식에서 인식률을 향상시키는 방안을 제시한다. 숫자의 전경특징은 숫자의 에지선을 추출한 Kirsch 방향특징과 숫자선 자체를 추출한 projection 방향특징으로 구성되며, 숫자의 배경특징은 숫자의 볼록외피로 부터 추출되는 오목특징이다. 여기서 오목특징은 방향특징에 대해 보완적인 특징으로 작용하여 분류 성능 향상에 기여한다. 인식기로는 RBF 커널을 이용한 SVM 분류기를 사용하고, CENPAMI 숫자특징 데이터베이스를 사용하여 제시된 방법의 성능을 검사하였다. 실험 결과 각기 다른 분류 성능을 갖는 이들 3종의 특징들이 상호 보완적으로 작용하여 인식률 향상에 기여함을 확인할 수 있었으며, 제시된 복합특징에 의해 98.90%의 인식률을 달성하였다.

• 한국멀티미디어학회논문지
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• 제20권5호
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• 전자공학회논문지B
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• 제29B권11호
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• pp.102-110
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• 1992

In this paper, a new method of character recognition is proposed. It uses density information, in addition to positional and directional information generally used, to recognize a character. Four directional feature primitives are extracted from the thinning templates on the observation that the output of the templates have directional property in general. A simple and fast feature extraction scheme is possible. Features are organized from recursive nonary tree(N-tree) that corresponds to normalized character area. Each node of the N-tree has four directional features that are sum of the features of it's nine sub-nodes. Every feature primitive from the templates are added to the corresponding leaf and then summed to the upper nodes successively. Recognition can be accomplished by using appropriate feature level of N-tree. Also, effectiveness of each node's feature vector was tested by experiment. A method to implement the proposed feature vector organization algorithm into hardware is proposed as well. The third generation node, which is 4$\times$4, is used as a unit processing element to extract features, and it was implemented in hardware. As a result, we could observe that it is possible to extract feature vector for real-time processing.

• 융합신호처리학회논문지
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• 제10권1호
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• pp.7-12
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• 2009

본 논문에서는 local min/max 연산을 이용한 필기체 숫자의 방향특징 추출 기법을 제안한다. 숫자의 방향특징은 숫자를 이루는 선에서 수평, 수직 및 두 대각방향인 4개 방향의 선들로 구성된 방향선분 영상으로부터 구해진다. Kirsch 마스크를 사용하는 기존의 방향특징 추출기법은 에지형태인 두 겹으로 된 방향선분 영상을 생성하는데 반해 본 논문에서 제시하는 방법은 방향성 수축연산을 사용하여 한 겹으로 된 방향선분 영상을 생성한다. 본 방향성 수축연산을 숫자영상에 적용하기 위해서는 먼저 세선화, 영상 팽창 등의 전처리가 필요하지만 이 방법은 숫자를 이루는 선 자체와 더욱 유사한 형태를 갖는 방향선분을 제공한다. 우리가 구하고자 하는 [$4{\times}4$] 크기인 4개의 방향특징은 4개의 방향선분 영상으로부터 조닝방법을 통해 구해진다. 보다 높은 필기체 숫자인식을 얻기 위해, 본 연구에서는 우리가 제안한 방향특징에 기존의 Kirsch 방향특징과 오목특징을 결합한 다중특징을 사용하였다. 본 숫자 특징에 의한 인식률을 테스트를 위해 오류역전파 알고리즘으로 학습되는 다층퍼셉트론 신경회로망을 인식기로 사용하였으며, Concordia 대학의 CENPARMI 숫자 데이터베이스를 사용하여 실험한 결과 98.35%의 인식률을 얻을 수 있었다.

• ETRI Journal
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• 제36권5호
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• pp.772-782
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• 2014

Two-microphone binary mask speech enhancement (2mBMSE) has been of particular interest in recent literature and has shown promising results. Current 2mBMSE systems rely on spatial cues of speech and noise sources. Although these cues are helpful for directional noise sources, they lose their efficiency in diffuse noise fields. We propose a new system that is effective in both directional and diffuse noise conditions. The system exploits two features. The first determines whether a given time-frequency (T-F) unit of the input spectrum is dominated by a diffuse or directional source. A diffuse signal is certainly a noise signal, but a directional signal could correspond to a noise or speech source. The second feature discriminates between T-F units dominated by speech or directional noise signals. Speech enhancement is performed using a binary mask, calculated based on the proposed features. In both directional and diffuse noise fields, the proposed system segregates speech T-F units with hit rates above 85%. It outperforms previous solutions in terms of signal-to-noise ratio and perceptual evaluation of speech quality improvement, especially in diffuse noise conditions.