• Title/Summary/Keyword: Gray Level Image

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Gray-level Image Watermarking using Wavelet Transform (웨이브렛 변환을 이용한 그레이-레벨 영상 워터마킹)

  • Min, Sun-Jin;Chung, Hoon;Kim, Chung-Hwa
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2001.10a
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    • pp.487-490
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    • 2001
  • With the establishment of the optimized copyright, digital image watermarking technique is demended to identify the owner of a certain image and to avoid the unauthorized distribution of digital image copies. Also, a robust watermarking approach should survive several possible attacks, such as image processing and lossy image compession. The proposed scheme distributes the 256 gray-level signature information in discrete wavelet transform domain of the host image where is very little visible distortion. While much of the privious work used signature data that is a small fraction of th e host images the proposed approach can easily handle gray-scale Images. As the result, stable reconstruction can be obtained even when the images are transformed, JPEG lossy compression or otherwise modified by low-pass filtering operations.

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Region of Interest Heterogeneity Assessment for Image using Texture Analysis

  • Park, Yong Sung;Kang, Joo Hyun;Lim, Sang Moo;Woo, Sang-Keun
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.11
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    • pp.17-21
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    • 2016
  • Heterogeneity assessment of tumor in oncology is important for diagnosis of cancer and therapy. The aim of this study was performed assess heterogeneity tumor region in PET image using texture analysis. For assessment of heterogeneity tumor in PET image, we inserted sphere phantom in torso phantom. Cu-64 labeled radioisotope was administrated by 156.84 MBq in torso phantom. PET/CT image was acquired by PET/CT scanner (Discovery 710, GE Healthcare, Milwaukee, WI). The texture analysis of PET images was calculated using occurrence probability of gray level co-occurrence matrix. Energy and entropy is one of results of texture analysis. We performed the texture analysis in tumor, liver, and background. Assessment textural features of region-of-interest (ROI) in torso phantom used in-house software. We calculated the textural features of torso phantom in PET image using texture analysis. Calculated entropy in tumor, liver, and background were 5.322, 7.639, and 7.818. The further study will perform assessment of heterogeneity using clinical tumor PET image.

Extraordinary State Classification of Grinding Wheel Surface Based on Gray-level Run Lengths (명암도 작용 길이에 따른 연삭 숫돌면의 이상 현상 분류)

  • 유은이;김광래
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.13 no.3
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    • pp.24-29
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    • 2004
  • The grinding process plays a key role which decides the quality of a product finally. But the grinding process is very irregular, so it is very difficult to analyse the process accurately. Therefore it is very important in the aspect of precision and automation to reduce the idle time and to decide the proper dressing time by watching. In this study, we choose the method which can be observed directly by using of computer vision and then apply pattern classification technique to the method of measuring the wheel surface. Pattern classification technique is proper to analyse complicated surface image. We observe the change of the wheel surface by using of the gray level run lengths which are representative in this technique.

Scene Text Extraction in Natural Images using Hierarchical Feature Combination and Verification (계층적 특징 결합 및 검증을 이용한 자연이미지에서의 장면 텍스트 추출)

  • 최영우;김길천;송영자;배경숙;조연희;노명철;이성환;변혜란
    • Journal of KIISE:Software and Applications
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    • v.31 no.4
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    • pp.420-438
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    • 2004
  • Artificially or naturally contained texts in the natural images have significant and detailed information about the scenes. If we develop a method that can extract and recognize those texts in real-time, the method can be applied to many important applications. In this paper, we suggest a new method that extracts the text areas in the natural images using the low-level image features of color continuity. gray-level variation and color valiance and that verifies the extracted candidate regions by using the high-level text feature such as stroke. And the two level features are combined hierarchically. The color continuity is used since most of the characters in the same text lesion have the same color, and the gray-level variation is used since the text strokes are distinctive in their gray-values to the background. Also, the color variance is used since the text strokes are distinctive in their gray-values to the background, and this value is more sensitive than the gray-level variations. The text level stroke features are extracted using a multi-resolution wavelet transforms on the local image areas and the feature vectors are input to a SVM(Support Vector Machine) classifier for the verification. We have tested the proposed method using various kinds of the natural images and have confirmed that the extraction rates are very high even in complex background images.

Shape Preserving Contrast Enhancement

  • Hwang Jae Ho
    • Proceedings of the IEEK Conference
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    • 2004.08c
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    • pp.867-871
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    • 2004
  • In this paper, a new analytic approach for shape preserving contrast enhancement is presented. Contrast enhancement is achieved by means of segmental histogram stretching modification which preserves the given image shape, not distorting the original shape. After global stretching, the image is partitioned into several level-sets according to threshold condition. The image information of each level-set is represented as typical value based on grouped differential values. The basic property is modified into common local schemes, thereby introducing the enhanced effect through extreme discrimination between subsets. The scheme is based on stretching the histogram of subsets in which the intensity gray levels between connected pixels are approximately same In spite of histogram widening, stretched by local image information, it neither creates nor destroys the original image, thereby preserving image shape and enhancing the contrast. By designing local histogram stretching operations, we can preserve the original shape of level-sets of the image, and also enhance the global intensity. Thus it can hold the main properties of both global and local image schemes, which leads to versatile applications in the field of digital epigraphy.

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New Carotid Artery Stenosis Measurement Method Using MRA Images (경동맥 MRA 영상을 이용한 새로운 내경 측정 방법)

  • 김도연;박종원
    • Journal of KIISE:Software and Applications
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    • v.30 no.12
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    • pp.1247-1254
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    • 2003
  • Currently. the north american symptomatic carotid endarterectomy trial, european carotid surgery trial, and common carotid method are used to measure the carotid stenosis for determining candidate for carotid endarterectomy using the projection angiography from different modalities such as digital subtraction angiography. rotational angiography, computed tomography angiography and magnetic resonance angiography. A new computerized carotid stenosis measuring system was developed using MR angiography axial image to overcome the drawbacks of conventional carotid stenosis measuring methods, to reduce the variability of inter-observer and intra-observer. The gray-level thresholding is one of the most popular and efficient method for image segmentation. We segmented the carotid artery and lumen from three-dimensional time-of-flight MRA axial image using gray-level thresholding technique. Using the measured intima-media thickness value of common carotid artery for each cases, we separated carotid artery wall from the segmented carotid artery region. After that, the regions of segmented carotid without artery wall were divided into region of blood flow and plaque. The calculation of carotid stenosis degree was performed as the following; carotid stenosis grading is(area measure of plaque/area measure of blood flow region and plaque) * 100%.

Adaptive Image Labeling Algorithm Using Non-recursive Flood-Fill Algorithm (비재귀 Flood-Fill 알고리즘을 이용한 적응적 이미지 Labeling 알고리즘)

  • Kim, Do-Hyeon;Gang, Dong-Gu;Cha, Ui-Yeong
    • The KIPS Transactions:PartB
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    • v.9B no.3
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    • pp.337-342
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    • 2002
  • This paper proposes a new adaptive image labeling algorithm fur object analysis of the binary images. The proposed labeling algorithm need not merge/order of complex equivalent labels like classical labeling algorithm and the processing is done during only 1 Pass. In addition, this algorithm can be extended for gray-level image easily. Experiment result with HIPR image library shows that the proposed algorithm process more than 2 times laster than compared algorithm.

Small Target Detection Method under Complex FLIR Imagery (복잡한 FLIR 영상에서의 소형 표적 탐지 기법)

  • Lee, Seung-Ik;Kim, Ju-Young;Kim, Ki-Hong;Koo, Bon-Ho
    • Journal of Korea Multimedia Society
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    • v.10 no.4
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    • pp.432-440
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    • 2007
  • In this paper, we propose a small target detection algorithm for FLIR image with complex background. First, we compute the motion information of target from the difference between the current frame and the created background image. However, the slow speed of target cause that it has the very low gray level value in the difference image. To improve the gray level value, we perform the local gamma correction for difference image. So, the detection index is computed by using statistical characteristics in the improved image and then we chose the lowest detection index a true target. Experimental results show that the proposed method has significantly the good detection performance.

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Improvement of Image Processing Algorithm for Particle Size Measurement Using Hough Transform (Hough 변환을 이용한 입경 측정을 위한 영상처리 알고리즘의 개선)

  • Kim, Yu-Dong;Lee, Sang-Yong
    • Journal of ILASS-Korea
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    • v.6 no.1
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    • pp.35-43
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    • 2001
  • Previous studies on image processing techniques for panicle size measurement usually have focused on a single panicle or weakly overlapped particles. In the present work, the image processing algorithm for particle size measurement has been improved to process heavily-overlapped spherical-particle images. The algorithm consists of two steps; detection of boundaries which separate the images of the overlapped panicles from the background and the panicle identification process. For the first step, Sobel operator (using gray-level gradient) and the thinning process was adopted, and compared with the gray-level thresholding method that has been widely adopted. In the second, Hough transform was used. Hough transform is the detection algorithm of parametric curves such as straight lines or circles which can be described by several parameters. To reduce the measurement error, the process of finding the true center was added. The improved algorithm was tested by processing an image frame which contains heavily overlapped spherical panicles. The results showed that both the performances of detecting the overlapped images and separating the panicle from them were improved.

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Binarization Based on the Spatial Correlation of Gray Levles (그레이 레벨의 공간적 상관관계 기반 이진화)

  • Seo, Suk-T.;Son, Seo-H.;Lee, In-K.;Jeong, Hye-C.;Kwon, Soon-H.
    • Journal of the Korean Institute of Intelligent Systems
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    • v.17 no.4
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    • pp.466-471
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    • 2007
  • Conventional thresholding methods including Otsu's thresholding method are based on the gray levels frequency histogram. But the gray levels frequency histogram is obtained by recomposing only frequency information from an input image, where frequency histogram dose not contain any other informations such as the distribution of gray levels and relation between gray levels. Therefore the methods using the gray levels frequency histogram occasionally present inappropriate threshold values because it cannot reflect informations of the given image sufficiently. In this paper, we define a correlation function of gray levels and propose a novel thresholding method using the gray levels frequency histogram and the spatial correlation information. The effectiveness of the proposed method will be shown through comparison with Otsu's thresholding method.