• Journal of Korea Multimedia Society
• /
• v.22 no.1
• /
• pp.18-26
• /
• 2019

In this paper we propose how to detect circular objects in the gray scale image and segment them using the discrete Morse theory, which makes it possible to analyze the topology of a digital image, when it is transformed into the data structure of some combinatorial complex. It is possible to get meaningful information about how many connected components and topologically circular shapes are in the image by computing the persistent homology of the filtration using the Morse complex. We obtain a Morse complex by modeling an image as a cubical cellular complex. Each cell in the Morse complex is the critical point at which the topological structure changes in the filtration consisting of the level sets of the image. In this paper, we implement the proposed algorithm of segmenting the circularly shaped objects with a long persistence of homology as well as computing persistent homology along the filtration of the input image and displaying in the form of a persistence diagram.

• Journal of Advanced Information Technology and Convergence
• /
• v.8 no.2
• /
• pp.47-58
• /
• 2018

In this paper, we propose a method for skin condition analysis using a camera module embedded in a smartphone without a separate skin diagnosis device. The type of skin disease detected in facial image taken by smartphone is acne, pigmentation, blemish and flush. Face features and regions were detected using Haar features, and skin regions were detected using YCbCr and HSV color models. Acne and flush were extracted by setting the range of a component image hue, and pigmentation was calculated by calculating the factor between the minimum and maximum value of the corresponding skin pixel in the component image R. Blemish was detected on the basis of adaptive thresholds in gray scale level images. As a result of the experiment, the proposed skin condition analysis showed that skin diseases of acne, pigmentation, blemish and flush were effectively detected.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.14 no.4
• /
• pp.169-176
• /
• 2018

In this study, we propose a new Fire Flame Region Extraction using Block Homogeneity Segmentation method of the Fire Image with irregular texture and various colors. It is generally assumed that fire flame extraction plays a very important role. The Color Image with fire flame is divided into blocks and edge strength for each block is computed by using modified color histogram intersection method that has been developed to differentiate object boundaries from irregular texture boundaries effectively. The block homogeneity is designed to have the higher value in the center of region with the homeogenous colors or texture while to have lower value near region boundaries. The image represented by the block homogeneity is gray scale image and watershed transformation technique is used to generate closed boundary for each region. As the watershed transform generally results in over-segmentation, region merging based on common boundary strength is followed. The proposed method can be applied quickly and effectively to the initial response of fire.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.06a
• /
• pp.59-62
• /
• 2018

In this paper, we propose a contrast enhancement algorithm using guided image filter (GIF). The GIF is used to divide an HDR image into a base layer and a detail layer. The energy scale of base layer determinate the darkness and brightness of the image. However, the detail information in the base layer is difficult to be displayed because of the high brightness and clusters of low brightness. We propose a contrast enhancement method by adjusting the gray level of base layer by subtracting the mean value of itself. It is combined with the detail layer to preserve the detail information. Experiment results show that the proposed algorithm has better performance in detail preservation and contrast enhancement.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.05a
• /
• pp.212-215
• /
• 2021

본 논문에서는 딥러닝의 CNN(Convolution Neural Network) 학습을 통하여 악성코드를 실행시키지 않고서 악성코드 변종을 패밀리 그룹으로 분류하는 방법을 연구한다. 먼저 데이터 전처리를 통해 3가지의 서로 다른 방법으로 악성코드 이미지와 메타데이터를 생성하고 이를 CNN으로 학습시킨다. 첫째, 악성코드의 byte 파일을 8비트 gray-scale 이미지로 시각화하는 방법이다. 둘째, 악성코드 asm 파일의 opcode sequence 정보를 추출하고 이를 이미지로 변환하는 방법이다. 셋째, 악성코드 이미지와 메타데이터를 결합하여 분류에 적용하는 방법이다. 이미지 특징 추출을 위해서는 본고에서 제안한 CNN을 통한 학습 방식과 더불어 3개의 Pre-trained된 CNN 모델을 (InceptionV3, Densnet, Resnet-50) 사용하여 전이학습을 진행한다. 전이학습 시에는 마지막 분류 레이어층에서 본 논문에서 선택한 데이터셋에 대해서만 학습하도록 파인튜닝하였다. 결과적으로 가공된 악성코드 데이터를 적용하여 9개의 악성코드 패밀리로 분류하고 예측 정확도를 측정해 비교 분석한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.11a
• /
• pp.347-350
• /
• 2020

본 논문에서는 영상 변환 기술인 이산웨이블릿변환(Discrete Wavelet Transform, DWT)를 딥러닝 기반의 네트워크로 구현한다. 딥러닝 기술 중에도 CNN 기반으로 네트워크를 설계하였으며, 본 DWT 네트워크는 해상도에 의존적이지 않은 계층들로만 구성된다. 데이터세트를 구성할 때 파이썬의 라이브러리를 사용하여 레이블 데이터세트를 구성한다. 128×128크기의 gray-scale 영상을 입력으로 사용하고 이에 대응하는 레이블 데이터세트를 구성하여 1-level DWT를 수행하는 네트워크의 학습을 진행한다. 역방향 변환도 네트워크 설계 후 데이터세트를 구성하여 학습을 진행한다. 학습이 완료된 1-level DWT 네트워크를 반복적으로 사용하여 Multi-level DWT 네트워크를 구성한다. 또한 양자화에 의한 간단한 영상압축 실험을 진행하여 DWT 네트워크의 성능과 압축 등의 응용분야에 활용할 수 있음을 보인다. 설계한 DWT 네트워크의 1-level 순방향 변환 성능은 42.18dB의 PSNR을 보였고, 1-level 역방향 변환 성능은 50.13dB의 PSNR을 보였다.

• Korean Journal of Radiology
• /
• v.21 no.7
• /
• pp.779-792
• /
• 2020

Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.

• The KIPS Transactions:PartB
• /
• v.15B no.4
• /
• pp.259-268
• /
• 2008

In an effort to increase the data capacity of one-dimensional symbology, 2D barcodes have been proposed a decade ago. In this paper, we present an effective 2D barcode detection algorithm from gray-level images, especially for the handheld 2D barcode recognition system. To locate the symbol inside the image, a criteria based on the block contrast is adopted, and a gray-scale projection with sub-pixel operation is utilized to segment the symbol precisely from the region of interest(ROI). Finally, the segmented ROI is normalized using the inverse perspective transformation for the following decoding processes. We also introduce the post-processing steps for decoding the QR-code. The proposed method ensures high performances under various lighting/printing conditions and strong perspective deformations. Experiments shows that our method is very robust and efficient in detecting the code area for the various types of 2D barcodes in real time.

• The KIPS Transactions:PartB
• /
• v.13B no.4 s.107
• /
• pp.401-408
• /
• 2006

In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.

• 제어로봇시스템학회:학술대회논문집
• /
• 1988.10b
• /
• pp.826-831
• /
• 1988

This paper presents an approach to segment an image into areas of surfaces, and to compute the surface properties from a gray-scale image in order to describe the surfaces for reconstruction of the 3-D shape of the objects. In general, an rigid body has several surfaces and many edges. But if it is not polyhedoron, it is necessary not only to describe the relation between surfaces, i.e. its line drawings but also to represent the surfaces' equations itself. In order to compute the surfaces' equation we use a probability of edge distribution. At first it is extracted edges from a gray-level image as much as possible. These are not only the points that maximize the change of an image intensuty but candidates which can be seemed to be edges. Next, other character of a surface (color, coordinates and image intensity) are extracted. In our study, we call the all feature of a surface as "texture", for example color, intensity level, orientation of an edge, shape of a surface and so on. These features of a surface on a pixel of an image plane are mapped to a point of the feature space, and segmented to each groups by cluster analysis on this space. These groups are considered to represent object surface in an image plane. Finally, the states of object surface in 3-D space are computed from distributional probability of local and overall statistical features of a surface, and from shape of a surface.a surface.