• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3166-3179
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• 2013

A pre-processing system for adaptive noise removal is proposed based on the principle of identifying and filtering object regions and background regions. Human perception of images depends on bright, well-focused object regions; these regions can be treated with the best filters, while simpler filters can be applied to other regions to reduce overall computational complexity. In the proposed method, bright region segmentation is performed, followed by segmentation of object and background regions. Noise in dark, background, and object regions is then removed by the median, fast bilateral, and bilateral filters, respectively. Simulations show that the proposed algorithm is much faster than and performs nearly as well as the bilateral filter (which is considered a powerful noise removal algorithm); it reduces computation time by 19.4 % while reducing PSNR by only 1.57 % relative to bilateral filtering. Thus, the proposed algorithm remarkably reduces computation while maintaining accuracy.

• International Journal of Contents
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• v.10 no.3
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• pp.35-40
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• 2014

Text line segmentation is a critical task in handwritten document recognition. In this paper, we propose a novel text-line-segmentation method using baseline estimation and watershed. The baseline-detection algorithm estimates the baseline using Adaptive Head-Tail Connection (AHTC) on the document. Then, the watershed method segments the line region using the baseline-detection result. Finally, the text lines are separated by watershed result and a post-processing algorithm defines the lines more correctly. The scheme successfully segments text lines with 97% accuracy from the handwritten document images in the ICDAR database.

• Proceedings of the Korea Multimedia Society Conference
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• 1998.04a
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• pp.100-105
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• 1998

지문 검증은 생체 측정학의 다양한 인증 시스템 중에서 기술의 적용 범위 및 사용의 편의성 등에서 가장 우수한 개인 인증 방법이다. 이러한 지문 인식 과정 중에서 Segmentation은 가장 기초적이지만 이후의 처리과정에 지대한 영향을 미칠 수 있는 과정이다. 특히 잡음이 많은 영상, 회전된 영상, 깨끗하지 못한 영상 등은 Segmentation 방법에 따라 원래의 영상이 훼손될 소지가 많다. 그래서 전자와 같은 지문의 경우, 한계 영역을 가변적으로 설정하여 전경 영상을 선택하는 것이 좋은 방법이다. 이 방법의 특징은 블록의 크기를 잘게 나누어 전경 후보자 영상 여러 개를 만들어서, 그 중에서 전경 영상 하나를 선택할 때, 가변적인 한계 영역을 설정하여, 가장 양호한 전경 영상을 선택할 수 있게 하는 것과, 필터링 적용을 통한 노이즈 제거 방법을 적용하는 것이다. 이 방법을 적용함으로써, 양호한 전경 영상을 선택할 수 있었고, 노이즈까지도 깨끗이 제거하여, 정확히 지문 부분만을 분할(Segmentation) 할 수 있었다.

• Journal of Biomedical Engineering Research
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• v.29 no.5
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• pp.348-354
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• 2008

Segmenting vessels in lower extremity CT images is very difficult because of gray level variation, connection to bones, and their small sizes. Instead of segmenting vessels, we propose an approach that segments bones and subtracts them from the original CT images. The subtracted images can contain not only connected vessel structures but also isolated vessels, which are very difficult to detect using conventional vessel segmentation methods. The proposed method initially grows a 3-dimensional (3D) volume with a seeded region growing (SRG) using an adaptive threshold and then detects junctions and forked branches. The forked branches are classified into either bone branches or vessel branches based on appearance, shape, size change, and moving velocity of the branch. The final volume is re-grown by collecting connected bone branches. The algorithm has produced promising results for segmenting bone structures in several tens of vessel-enhanced CT image data sets of lower extremities.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.974-976
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• 1995

Segmentation of image data is an important problem in computer vision, remote sensing, and image analysis. Most objects in the real world have textured surfaces. Segmentation based on texture information is possible even if there are no apparent intensity edges between the different regions. There are many existing methods for texture segmentation and classification, based on different types of statistics that can be obtained from the gray-level images. In this paper, we use a neural network model --- ART-2 (Adaptive Resonance Theory) for textures in an image, proposed by Carpenter and Grossberg. In our experiments, we use Walsh matrix as feature value for textured image.

• Journal of the Semiconductor & Display Technology
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• v.13 no.4
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• pp.49-54
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• 2014

This paper proposes a new color segmentation method of vehicle license plates in the RGB color space. Firstly, the proposed method shifts the histogram of an input image rightwards and then stretches the image of the histogram slide. Secondly, the method separates each of the three RGB color components and performs the adaptive threshold processing with the three components, respectively. Finally, it combines the three components under the condition of making up a segment color and removes noises with the morphological processing. The proposed method is implemented using C language in an embedded Linux system for a high-speed real-time image processing. Experiments were conducted by using real vehicle images. The results show that the proposed algorithm is successful for most vehicle images. However, the method fails in some vehicles when the body and the license plate have the same color.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.145-148
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• 2010

본 논문에서 우리는 도로 영역과 하늘 영역, 그리고 도로와 하늘이 아닌 나머지 영역으로 분할하기 위해 동적인(dynamic) 패턴을 이용한 적응적인(adaptive) 병합 방법을 제안한다. 원본영상에서 Mean Shift 알고리즘과 라벨링(Labeling)을 수행하고 영역을 과분할 한다. 컬러에 의해서 도로와 하늘영역이 검출되지 못하는 영역을 위해서 도로 영역과 하늘 영역에서 동적인 패턴 추출한 후 매칭을 통해 유사 영역을 병합한다. 이것은 도로와 하늘의 정보를 현재 환경에서 적응적으로 추출하는 방법이다. 실험에서 정적인(static) 패턴을 사용해서 병합하는 방법과 동적인 패턴을 사용해서 병합하는 방법을 비교하였다. 그 결과, 동적인 패턴을 사용하였을 때 8.12%의 향상된 성능을 보였다.

• The Journal of Engineering Research
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• v.6 no.2
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• pp.99-107
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• 2004

In stereo matching system, it is efficient using segment patch, which divides the image into homogeneous region in color or similar intensity, because it preserves the disparity boundary and disparity continuity in low textured region. But many miss matching occur in the highly textured region because of the over segmentation that makes patch small and ambiguous. In this paper, in order to solve problems, we propose adaptive patch matching based on the watershed image segmentation. Performance was verified in experimental results.

• 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.

• Smart Media Journal
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• v.1 no.4
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• pp.59-71
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• 2012

Since mobile phones are used as common communication devices, their applications are increasingly important to human's life. Using smart-phones camera to collect daily life environment's information is one of targets for many applications such as text recognition, object recognition or context awareness. Studies have been conducted to provide important information through the recognition of texts, which are artificially or naturally included in images and movies acquired from mobile phones. In this study, a character segmentation method that improves character-recognition accuracy in images obtained from mobile phone cameras is proposed. The proposed method first classifies texts in a given image to printed letters and handwritten letters since segmentation approaches for them are different. For printed letters, rough segmentation process is conducted, then the segmented regions are integrated, deleted, and re-segmented. Segmentation for the handwritten letters is performed after skews are corrected and the characters are classified by integrating them. The experimental result shows our method achieves a successful performance for both printed and handwritten letters as 95.9% and 84.7%, respectively.