• Journal of Digital Convergence
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• v.10 no.9
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• pp.351-356
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• 2012

This paper presents the 2D/3D conversion technique using depth map which is generated based on the haze and relative height cue. In cases that only the conventional haze information is used, errors in image without haze could be generated. To reduce this kind of errors, a new approach is proposed combining the haze information with depth map which is constructed based on the relative height cue. Also the gray scale image from Mean Shift Segmentation is combined with depth map of haze information to sharpen the object's contour lines, upgrading the quality of 3D image. Left and right view images are generated by DIBR(Depth Image Based Rendering) using input image and final depth map. The left and right images are used to generate red-cyan 3D image and the result is verified by measuring PSNR between the depth maps.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.160-171
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• 1994

The wide dynamic range and severely attenuated contrast in mediastinal area appearing in typical chest radiographs have often caused difficulties in effective visualization and diagnosis of lung diseases. This paper proposes a new adaptive image enhancement technique which potentially solves this problem and there by improves observer performance through image processing. In the proposed method image processing is applied to the chest radiograph with different processing parameters for the lung field and mediastinum adaptively since there are much differences in anatomical and imaging properties between these two regions. To achieve this the chest radiograph is divided into the lung and mediastinum by gray level thresholding using the cumulative histogram and the dynamic range compression and local contrast enhancement are carried out selectively in the mediastinal region. Thereafter a gray scale transformation is performed considering the JND(just noticeable difference) characteristic for effective image displa. The processed images showed apparenty improved contrast in mediastinum and maintained moderate brightness in the lung field. No artifact could be observed. In the visibility evaluation experiment with 5 radiologists the processed images with better visibility was observed for the 5 important anatomical structures in the thorax.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.89-102
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• 2013

The paper presents a fuzzy based impulse noise filter for both gray scale and color images. The proposed approach is based on the technique of boundary discriminative noise detection. The algorithm is a multi-step process comprising detection, filtering and color correction stages. The detection procedure classifies the pixels as corrupted and uncorrupted by computing decision boundaries, which are fuzzified to improve the outputs obtained. In the case of color images, a correction term is added by examining the interactions between the color components for further improvement. Quantitative and qualitative analysis, performed on standard gray scale and color image, shows improved performance of the proposed technique over existing state-of-the-art algorithms in terms of Peak Signal to Noise Ratio (PSNR) and color difference metrics. The analysis proves the applicability of the proposed algorithm to random valued impulse noise.

• Journal of information and communication convergence engineering
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• v.5 no.1
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• pp.50-53
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• 2007

This paper presents face features detection and a new physiological neuro-fuzzy learning method by using two-dimensional variances based on variation of gray level and by learning for a statistical distribution of the detected face features. This paper reports a method to learn by not using partial face image but using global face image. Face detection process of this method is performed by describing differences of variance change between edge region and stationary region by gray-scale variation of global face having featured regions including nose, mouse, and couple of eyes. To process the learning stage, we use the input layer obtained by statistical distribution of the featured regions for performing the new physiological neuro-fuzzy algorithm.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.187-193
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• 2012

An image authentication scheme for gray scale image through embedding a digital watermark by quantization of Integer Wavelet Transform (IWT) coefficients of the image is proposed in this paper. Proposed method is designed to detect modification of an image and to identify tampered location of the image. To embed the watermark mid-frequency band of a second level IWT was used. An approximation of the original image based on LL band was stored in LSB bits of the pixel data as a recovery mark for restoration of the image. Watermarked image has achieved a good PSNR of 40 dB compared to original cover image. Restored image quality was also very good with a PSNR of more than 35 dB compared to unmodified watermarked image even when 25% of the received image is cropped. Thus, the proposed method ensures a proper balance between the fidelity of the watermarked image and the quality of the restored image.

• Journal of Internet Computing and Services
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• v.13 no.4
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• pp.53-62
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• 2012

Object segmentation is a demanding research area and not a trivial problem of image processing and computer vision. Tremendous segmentation algorithms were addressed on gray-scale (or biomedical) images that rely on numerous image features as well as their strategies. These works in practice cannot apply to natural color images because of their negative effects to color values due to the use of gray-scale gradient information. In this paper, we proposed a new approach for color object segmentation by modifying a geometric active contour model named distance regularized level set evolution (DRLSE). Its speed function will be designed to exploit as much as possible color gradient information of images. Finally, we provide experiments to show performance of our method with respect to its accuracy and time efficiency using various color images.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1925-1931
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• 2011

This paper suggests an approach to the subtle concept, "good", through the fuzzy logic and the design of the Canny edge detector of Gray scale images based on the rules of fuzzy anisotropic diffusion. The Canny edge detection algorithms design is to divide the gray levels into pixels and then calculate the diffusion coefficients at each pixel of non-edgy regions. Based on this processing, we present the Canny edge detector implementing fuzzy logic and comparing the results to other existing methods. The proposed approach is the narrow dynamic range of the gray-level image Sharpening the edge detection and has the advantage.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.53-56
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• 2002

In this paper, we propose the method of iris lacuna extraction using watershed transform. Lacuna is salient feature of iris. It has three dimensional structure formed by leak of pigmentation and loss of fiber tissues. Lacuna can be used for iris recognition system, and generally used in health diagnosis and character analysis with its shape and position. The main idea of the proposed method is applying the watershed transform to radial gray scale profile of iris image. The result shows that the lacuna can be extracted automatically from eye image.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.520-522
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• 2002

오늘날 CT나 MR등을 통한 의학 영상 기술과 컴퓨터 성능의 향상으로 인체 내부 장기의 영상을 비교적 용이하게 얻을 수 있으며 얻어진 영상 정보는 컴퓨터로 수치화 되므로 데이터의 조작 및 가공이 용이하다. 그러나, 이렇게 얻어진 의학 영상들은 보통 2차원적 슬라이스 image 형태로 얻어진다. 일반적으로 슬라이스 사이의 간격은 조사량 등 여러 문제 때문에, 항상 동일한 간격을 유지하고 있지 않은 경우가 많으며 슬라이스 사이 간격이 슬라이스 내의 픽셀 간격보다 큰 경우가 대부분이다. 이러한 image로부터 3차원적 디스플레이나, 조작, 분석을 하기 위해서는 같은 간격의 image를 얻어야 한다. 이러한 이유로 인하여 보간(Interpolation) 기법이 의학 영상 분야에서 많이 사용된다. 본 논문은 명태-기반 보간 방법을 gray-scale image에 적용이 가능하도록 확장한 그레이 수준 형태 기반 보간 알고리즘을 구현하였다. 그리하여, 본 논문이 제안한 알고리즘을 슬라이스 간격이 큰 2차원 복부 CT 영상에 적용시켜 다른 보간법들보다 향상된 결과를 확인할 수 있었다.