• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.670-675
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• 2022

This study proposes an improved binarization method to improve image recognition rate. The research goal is to minimize the information loss that occurs during the binarization process, and to transform the object of the original image that cannot be determined through the transformation process into an image that can be judged. The proposed method uses a stepwise segmentation method of an image and divides blocks using prime numbers. Also, within one block, a trapezoidal type of fuzzy is applied. The fuzzy trapezoid is binarized by dividing the brightness histogram area into three parts according to the degree of membership. As a result of the experiment, information loss was minimized in general images. In addition, it was found that the converted binarized image expressed the object better than the original image in the special image in which the brightness region was tilted to one side.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1281-1285
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• 2013

In this paper, we propose a method to extract the area of appendix from ultrasonographic image via computational vision. A series of image processing techniques such as Ends-in search stretching for enhancing the brightness contrast, block binarization, grassfire algorithm for extracting lower part of fascia, and fuzzy sigma binarization method to finalize the appendix area are used to achieve our goal. The strength of this paper is using fuzzy sigma binarization instead of other binarization technique to handle the sensitivity of extracting the target area from regio hypogastrica. The experiment verifies the efficacy of the proposed method successfully.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.163-164
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• 2015

In order to decode the bar code image binarization process is indispensable. The traditional binarization method is a global threshold binarization and local threshold binarization. Global threshold binarization method using a single threshold. In some cases there is a blur, or if the brightness is different from the bar code image. Therefore, binary pattern information is not retained. Local threshold method is binaized pattern information is maintained but processing speed is slow than global threshold binarization. The algorithm for solving this problem, there is modified binary algorithm. In this paper, we proposed hardware IP implemented by Vivado of modified binary algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1778-1797
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• 2021

Document image binarization is an important pre-processing step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of encoder-decoder architectures, such as FCN (fully convolutional network) and U-Net. Despite their success, they still suffer from three limitations: (1) reduced feature map resolution due to consecutive strided pooling or convolutions, (2) multiple scales of target objects, and (3) reduced localization accuracy due to the built-in invariance of deep convolutional neural networks (DCNNs). To overcome these three challenges, we propose an improved semantic segmentation model, referred to as DP-LinkNet, which adopts the D-LinkNet architecture as its backbone, with the proposed hybrid dilated convolution (HDC) and spatial pyramid pooling (SPP) modules between the encoder and the decoder. Extensive experiments are conducted on recent document image binarization competition (DIBCO) and handwritten document image binarization competition (H-DIBCO) benchmark datasets. Results show that our proposed DP-LinkNet outperforms other state-of-the-art techniques by a large margin. Our implementation and the pre-trained models are available at https://github.com/beargolden/DP-LinkNet.

• The Journal of the Korea Contents Association
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• v.9 no.8
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• pp.9-17
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• 2009

License plate recognition is an active research area. but few comparative studies on license plate binarization have been conducted. Many related researchers have experienced similar trial and error for finding an effective binarization method. To reduce this trial and error, this study implemented some binarization methods and quantitatively compared the performance of the methods. The performance evaluation consists of a low level measure and a high level measure, so it can evaluate not only the quality of binarized image itself but also the usefulness of the result. The performance evaluation was separately performed with three groups of images so as to understand the properties of the binarization methods. Experimental results show that the quality of binarization is more dependent on the evenness of illumination than the intensity of illumination. The Otsu's method has acquired the most effective performance in the group of even illumination images and the Niblack's method with parameter correction has shown the best quality in the group of uneven illumination images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.721-726
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• 2004

The image binarization is applied frequently as one part of the preprocessing phase for a variety of image processing techniques such as character recognition and image analysis, etc. The performance of binarization algorithms is determined by the selection of threshold value for binarization, and most of the previous binarization algorithms analyze the intensity distribution of the original images by using the histogram and determine the threshold value using the mean value of Intensity or the intensity value corresponding to the valley of the histogram. The previous algorithms could not get the proper threshold value in the case that doesn't show the bimodal characteristic in the intensity histogram or for the case that tries to separate the feature area from the original image. So, this paper proposed the novel algorithm for image binarization, which, first, segments the intensity range of grayscale images to several intervals and calculates mean value of intensity for each interval, and next, repeats the interval integration until getting the final threshold value. The interval integration of two neighborhood intervals calculates the ratio of the distances between mean value and adjacent boundary value of two intervals and determine as the threshold value of the new integrated interval the intensity value that divides the distance between mean values of two intervals according to the ratio. The experiment for performance evaluation of the proposed binarization algorithm showed that the proposed algorithm generates the more effective threshold value than the previous algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.485-492
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• 2016

A role of the binarization is very important in separating the foreground and the background in the field of the computer vision. In this study, an adaptive fuzzy binarization is proposed. An ${\alpha}$-cut control ratio is obtained by the distribution of grey level of pixels in a sliding window, and binarization is performed using the value. To obtain the ${\alpha}$-cut, existing thresholding methods which execution speed is fast are used. The threshold values are set as the center of each membership function and the fuzzy intervals of the functions are specified with the distribution of grey level of the pixel. Then ${\alpha}$-control ratio is calculated using the specified function and binarization is performed according to the membership degree of the pixels. The experimental results show the proposed method can segment the foreground and the background well than existing binarization methods and decrease loss of the foreground.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1514-1519
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• 2019

IVUS is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. IVUS is regularly used to locate the atherosclerosis lesions in the coronary arteries. Auto-segmentation of the vessel structure is important to detect the disorder of coronary artery. In this paper, we propose a simple strategy to extract Intima/Adventitia area effectively using fuzzy binarization from intravascular images. The proposed method apply fuzzy binarization to find the adventitia but apply average binarization to locate the intima since they have different homogeneity of pixel intensity comparing with the environment. In this paper, we demonstrate an effective auto-segmentation method for detecting the interior/exterior of the vessel walls by differentiating the fuzzy binarization result and average binarization result from IVUS image. Important statistics such as Intima-Media Thickness (IMT) or volume of a target area can be easily computed from result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1877-1884
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• 2006

Image binarization is an important preprocessing to identify objects of interest by dividing pixels into background and objects. Usually binarization methods are classified into global and local thresholding approaches. In this paper, we propose an efficient and adaptive binarization method for the character segmentation by combining both advantages of the global and the local thresholding methods. Experimental results with the korean character images present that the proposed method binarizes character image faster and better than other local binarization methods.

• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.161-166
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• 2011

We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user's marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.