• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5522-5540
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• 2018

Vehicular license plate (LP) recognition system has risen as a significant field of research recently because various explorations are currently being conducted by the researchers to cope with the challenges of LPs which include different illumination and angular situations. This research focused on restricted conditions such as using image of only one vehicle, stationary background, no angular adjustment of the skewed images. A real time vehicular LP recognition scheme is proposed for the skewed images for detection, segmentation and recognition of LP. In this research, a polar co-ordinate transformation procedure is implemented to adjust the skewed vehicular images. Besides that, window scanning procedure is utilized for the candidate localization that is based on the texture characteristics of the image. Then, connected component analysis (CCA) is implemented to the binary image for character segmentation where the pixels get connected in an eight-point neighbourhood process. Finally, optical character recognition is implemented for the recognition of the characters. For measuring the performance of this experiment, 300 skewed images of different illumination conditions with various tilt angles have been tested. The results show that proposed method able to achieve accuracy of 96.3% in localizing, 95.4% in segmenting and 94.2% in recognizing the LPs with an average localization time of 0.52s.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.62-65
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• 2000

In this paper we propose skewed angle detection methods for images that contain text that is not aligned horizontally. In most images text areas are aligned along the horizontal axis, however there are many occasions when the text may be at a skewed angle (denoted by 0 < ${\theta}\;{\leq}\;{\pi}$). In the work described, we adapt the Hough transform, Shadow and Threshold Projection methods to detect the skewed angle of text in an input image using the orthogonal angle view property. The results of this method are a primary text skewed angle, which allows us to rotate the original input image into an image with horizontally aligned text. This utilizes document image processing prior to the recognition stage.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3181-3183
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• 2000

As the fact that the simple data of text and sound in early year have been changed to be high quality images and sounds. PLL(Phase-Locked Loop) system plays an important role in communication system. VCO(Voltage Controlled Oscillator) is the most important part in PLL system because it can have critical effects on operation of PLL. Recently, it has been raised the necessity of high speed and high accuracy circuit application. In this paper, a new differential voltage clamped VCO using negative-skewed path is suggested. Using a dual-delay scheme to implement the VCO, higher operation frequency and wider tuning are achieved simultaneously. The dual-delay scheme means that both the negative skewed delay paths and the normal delay paths exist in the same ring oscillator. The negative skewed delay paths decrease the unit delay time of the ring oscillator below the single inverter delay time. As a result, higher operation frequency can be obtained. The whole characteristics of VCO are simulated by using HSPICE. Simulation results show that the resulting operating frequencies are 50% higher than those obtainable from the conventional approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2082-2102
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• 2024

Accurate segmentation of magnetic resonance (MR) images is crucial for providing doctors with effective quantitative information for diagnosis. However, the presence of weak boundaries, intensity inhomogeneity, and noise in the images poses challenges for segmentation models to achieve optimal results. While deep learning models can offer relatively accurate results, the scarcity of labeled medical imaging data increases the risk of overfitting. To tackle this issue, this paper proposes a novel fuzzy c-means (FCM) model that integrates a deep learning approach. To address the limited accuracy of traditional FCM models, which employ Euclidean distance as a distance measure, we introduce a measurement function based on the skewed normal distribution. This function enables us to capture more precise information about the distribution of the image. Additionally, we construct a regularization term based on the Kullback-Leibler (KL) divergence of high-confidence deep learning results. This regularization term helps enhance the final segmentation accuracy of the model. Moreover, we incorporate orthogonal basis functions to estimate the bias field and integrate it into the improved FCM method. This integration allows our method to simultaneously segment the image and estimate the bias field. The experimental results on both simulated and real brain MR images demonstrate the robustness of our method, highlighting its superiority over other advanced segmentation algorithms.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.509-516
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• 2002

This paper proposes an efficient algorithm which recognizes the mixed document image consisting of the images, texts, tables, and straight lines. This system is composed of three steps. The first step is the detection of rotation angle for complementing skewed images, the second is detection of erasing an unnecessary background region and last is the classification of each component included in document images. This algorithm performs preprocessing of detecting rotation angles and correcting documents based on the detected rotation angles in order to minimize the error rate by skewness of the documentation. We detected the rotation angie using only horizontal and vertical components in document images and minimized calculation time by erasing unnecessary background region in the detecting process of component of document. In the next step, we classify various components such as image, text, table and line area included in document images. we applied this method to various document images in order to evaluate the performance of document recognition system and show the successful experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.680-686
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• 2006

Document images captured by scanners have only skewing distortion. But camera captured document images have not only skew but also vignetting effect and geometric distortion. Vignetting effect, which makes the border areas to be darker than the center of the image, make it difficult to separate characters from the document images. But this effect has being decreased, as the lens manufacturing skill is developed. Geometric distortion, occurred by the mismatch of angle and center position between the document image and the camera, make the shape of characters to be distorted, so that the character recognition is more difficult than the case of using scanner. In this paper, we propose a method that can increase the performance of character recognition by correcting the geometric distortion of document images using a linear approximation which changes the quadrilateral region to the rectangle one. The proposed method also determine the quadrilateral transform region automatically, using the alignment of character lines and the skewed angles of characters located in the edges of each character line. Proposed method, therefore, can correct the geometric distortion without getting positional information from camera.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.7
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• pp.1487-1494
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• 2012

This paper proposes an algorithm detecting the skew of the degraded as well as the clear document images using edge and correcting it. The proposed algorithm detects edges in a character region selected by image complexity and generates projection histograms by projecting them to various directions. And then it detects the document skew by estimating the edge concentrations in the histograms and corrects the skewed document image. For the fast skew detection, the proposed algorithm uses downsampling and 3 step coarse-to-fine searching. In the skew detection of the clear and the degraded images, the maximum and the average detection errors in the proposed algorithm are about 50% of one in a conventional similar algorithm and the processing time is reduced to about 25%. In the non-uniform luminance images acquired by a mobile device, the conventional algorithm can't detect skews since it can't get valid binary images, while the proposed algorithm detect them with the average detection error of 0.1o or under.

• The Journal of the Korea Contents Association
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• v.7 no.11
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• pp.34-42
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• 2007

In this paper, we propose a staff-line detection and removal algorithm from a music score image obtained by a mobile phone camera. As a preprocessing technique to recognize a music score image, staff-line detection and removal should be efficiently applied to the skewed or curved images. The proposed method detects a staff-line by dividing a staff according to the degree of distortion. The number of division is calculated by dividing a staff repletely until an average of differences of y coordinates in every divided position is smaller than a threshold. Therefore, the number of division can be adaptively estimated according to the degree of the distortion. For an experiment, we make various kinds of images by rotating one from $1^{\circ}\;to\;3^{\circ}$ or curving slightly upward. The results show that the proposed method performed well on the experiment images.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.143-150
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• 2005

Camera image is very sensitive to illumination that result in difficulties for recognizing character. Also Camera captured document images have not only skew but also vignetting effect and geometric distortion. Vignetting effect make it difficult to separate characters from the document images. Geometric distortion, occurred by the mismatch of angle and center position between the document image and the camera, make the shape of characters to be distorted, so that the character recognition is more difficult than the case of using scanner. In this paper, we propose a method that can increase the performance of character recognition by correcting the geometric distortion of document images using a linear approximation which changes the quadrilateral region to the rectangle one. The proposed method also determine the quadrilateral transform region automatically, using the alignment of character lines and the skewed angles of characters located in the edges of each character line. Proposed method, therefore, can correct the geometric distortion without getting positional information from camera.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.436-438
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• 2001

본 논문에서는 기울어진 얼굴 영상에서 얼굴 구성 요소를 추출하는 방법을 제안한다. 제안하는 방법은 먼저 피부 색상 정보를 이용하여 얼굴 후보 영역을 추출한다. 이때 YIQ 색상 좌표계를 이용하고 조명의 영향을 반영하기 위하여 피부색상 영역을 다단계로 분할하여 색상 영역을 각각 결정한 뒤 적중률을 계산하여 얼굴 후보 영역을 결정하는 방법을 제안하였다. 2단계에서는 얼굴의 구성 요소중 가장 두드러진 특징인 눈동자 영역을 기준으로 한국인의 표준 얼굴 통계치를 적응하여 탐색하는 방법을 사용하였다. 이때 탐색된 눈동자 좌표로부터 얼굴의 기울기를 추정한다. 다음 단계에서는 얼굴 후보 영역에 대하여 기울어짐 보정을 수행한 뒤, 수평 수직 투영값을 이용하여 얼굴의 구성요소를 탐색한 뒤 얼굴 포함 최소 사각형을 정의하였다. 마지막으로 얼굴 영상 데이터 베이스로부터 얼굴 포함 최소 사각형에 대한 명암값 표준템플릿을 정의하고, 입력 영상에서 탐색된 최소 포함 사각형에 대하여 얼굴 영역 검증하는 방법을 제안하였다.