• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• 정보처리학회논문지B
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• 제12B권7호
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• pp.721-728
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• 2005

본 논문에서는 영상 내 물체 영역에 대한 다중정규화와 움직임 색상 정보를 활용하여 이동 물체에 대한 후보 그룹을 추출하고 영상 분할 방법에 의해 대상 물체 영역을 정의하며 최종적으로 목표물체에 대한 검출방법을 제공하였다. 다중 색상변환에 의해 물체의 고유영역 확률을 강화하고 MCWUPC(Moving Color Weighted Unmatched Pixel Count) 연산을 활용하여 이동물체의 영역을 강조하는 두 가지 개념을 결합함으로써 최종적으로 입력 영상 시퀀스에서의 후보영역을 찾아 분할하였으며 매 프레임 정확한 물체의 외곽정보를 검출하였다. 제안된 알고리즘을 검증하기 위하여 이동물체의 이동 실시간이 가능한 시스템을 구축하였고, 다양한 배경을 포함한 실험영상 120 프레임을 처리한 결과 $89\%$ 이상의 추적 성공률을 보여주었다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• 한국정보통신학회논문지
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• 제17권2호
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• pp.423-430
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• 2013

최근 손등이나 손바닥, 손가락의 정맥 혈관 패턴정보를 이용하여 개인을 인증하는 기술은 훼손, 복제 및 위조가 불가능하다는 장점으로 인해 연구가 활발하게 진행 중이다. 정맥영상은 피부층과 내부 골격등에 의한 빛의 산란 및 불균일한 내부 조직 때문에 정맥 영역이 뚜렷하게 나타나지 않아, 영상처리 방법을 통해 정맥 영역을 정확하게 분리하는 것이 어렵다. 특히 한 장의 영상에서도 밝기가 균일하지 않아서 지역 영역 단위로 다른 이진 임계치를 사용함으로 인해 처리시간이 오래 걸리고 혈관의 불연속면이 발생한다는 문제가 있다. 이를 해결하기 위해 본 논문에서는 조명 정규화 기반의 고속 정맥 영역 추출 방법을 제안한다. 본 연구는 기존의 방법에 비해 다음과 같은 장점을 가지고 있다. 첫째, 정맥영상의 불균일한 조명을 제거하기 위해 저역통과필터를 통해 조명 성분을 취득하고 이를 통해 조명성분이 균일한 영상을 얻었다. 둘째, 조명 정규화 영상으로부터 단일 임계치를 통해 얻어진 이진 영상의 처리를 통해 혈관 경로를 추출함으로써, 처리시간을 단축하였다. 실험을 통해 기존 방법들에 비해 혈관 영역 추출 정확도가 상승하고, 처리속도가 단축된 결과를 얻을 수 있었다.

• Journal of the Optical Society of Korea
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• 제18권4호
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• 대한원격탐사학회지
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• 제36권5_2호
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• pp.917-927
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• 2020

우리나라 국토교통부는 GSD가 0.5m 급인 위성영상의 촬영이 가능한 CAS 500-1/2 위성과 함께 이를 활용하기 위한 기술을 개발 중에 있다. 이에 본 연구에서는 CAS 500-1/2 위성영상의 활용을 위한 기술로 자동화를 지향하는 공간객체추출 기술을 개발하고자 하였다. 연구 수행을 위해 CAS 500-1/2와 가장 유사할 것으로 예상되는 KOMPSAT-3A 위성영상을 연구에 이용하였으며, 상대방사보정을 통해 공간객체추출의 자동화 가능성을 분석하고자 하였다. 이를 위하여 상대방사보정에 이용된 참조 영상과 상대방사보정된 영상에서 매개변수 및 임계값을 동일하게 적용하고, 공간객체를 추출하였다. 추출된 공간객체가 참조영상과 상대방사보정된 영상에서 유사한 형태로 추출되는지에 대한 정성적 분석과 분류정확도가 본 연구에서 설정한 목표정확도인 90% 이상을 만족하는지에 대한 정량적 분석을 통해 공간객체추출의 자동화 가능성 여부를 분석하고자 하였다. 그 결과, 참조영상과 상대방사보정된 영상에서 각각 추출한 공간객체가 유사하게 추출되는 것을 확인하였으며, 분류정확도 분석 결과가 모두 목표정확도인 90% 이상을 만족하는 것으로 나타나 상대방사보정을 통해 공간객체추출 시 자동화가 가능할 것으로 판단된다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.367-370
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• 2005

This paper proposes a novel method for content-based watermarking based on feature points of an image. At each feature point, watermark is embedded after affine normalization according to the local characteristic scale and orientation. The characteristic scale is the scale at which the normalized scale-space representation of an image attains a maximum value, and the characteristic orientation is the angle of the principal axis of an image. By binding watermarking with the local characteristics of an image, resilience against affine transformations can be obtained. Experimental results show that the proposed method is robust against various image processing steps including affine transformations, cropping, filtering, and JPEG compression.

• Journal of Multimedia Information System
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• 제4권4호
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• pp.219-224
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• 2017

This paper is to introduce an application of face recognition algorithm in parallel. We have experiments of 25 images with different motions and simulated the image recognitions; grouping of the image vectors, image normalization, calculating average image vectors, etc. We also discuss an analysis of the related eigen-image vectors and a parallel algorithm. To develop the parallel algorithm, we propose a new type of initial matrices for eigenvalue problem. If A is a symmetric matrix, initial matrices for eigen value problem are investigated: the "optimal" one, which minimize ${\parallel}C-A{\parallel}_F$ and the "super optimal", which minimize ${\parallel}I-C^{-1}A{\parallel}_F$. In this paper, we present a general new approach to the design of an initial matrices to solving eigenvalue problem based on the new optimal investigating C with preserving the characteristic of the given matrix A. Fast all resulting can be inverted via fast transform algorithms with O(N log N) operations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.225-227
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• 1995

An object detection algorithm using a modified IDM(Image Differential Method) is proposed for detecting an object in a level crossing area. The conventional object detection method using LASER light has the deadzone that it cannot detect small objects, while the object detection method using image data in a level crossing area can detect such small objects. But the image data in a level crossing area can be changeable easily because the data is outdoor and sensitive to such surrounding environments as the change of the sun beam, the shadow of cars, and so on. So we resolve these problems by adding the normalization and the process for shadow of the image data in a level crossing area to the basic IDM(Image Differential Method).

• 한국의류학회지
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• 제36권7호
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• pp.663-676
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• 2012

This article is to understand the implications and ideological meaning of female normative beauty reproduced by the idealizing phenomena of the hyper-real body as a process of the normalization of the body projected in fashion magazines with a focus on the body created by the increased influence of mass media in consumer capitalism. This study conducts a literature research and semiotic analysis as the method of investigation and focuses on the body images of the beauty articles in Vogue Korea. The idealizing phenomena of the hyper-real body in fashion magazines emphasizes that the body is an exchangeable substance that can be disassembled to adjust to accord with the standards and norms of society, that the ability of individuals to manage their body is enhanced by a rise in social class, and concludes that the superficial alteration of the body image is related to the standard of a moral tendency where a young and slender figure is considered to be a well managed body image.