• Title/Summary/Keyword: Pixel clustering

Search Result 78, Processing Time 0.019 seconds

Enhanced ART1 Algorithm for the Recognition of Student Identification Cards of the Educational Matters Administration System on the Web (웹 환경 학사관리 시스템의 학생증 인식을 위한 개선된 ART1 알고리즘)

  • Park Hyun-Jung;Kim Kwang-Baek
    • Journal of the Korea Society of Computer and Information
    • /
    • v.10 no.5 s.37
    • /
    • pp.333-342
    • /
    • 2005
  • This paper proposes a method, which recognizes student's identification card by using image processing and recognition technology and can manage student information on the web. The presented scheme sets up an average brightness as a threshold, based on the brightest Pixel and the least bright one for the source image of the ID card. It is converting to binary image, applies a horizontal histogram, and extracts student number through its location. And, it removes the noise of the student number region by the mode smoothing with 3$\times$3 mask. After removing noise from the student number region, each number is extracted using vertical histogram and normalized. Using the enhanced ART1 algorithm recognized the extracted student number region. In this study, we propose the enhanced ART1 algorithm different from the conventional ART1 algorithm by the dynamical establishment of the vigilance parameter. which shows a tolerance limit of unbalance between voluntary and stored patterns for clustering. The Experiment results showed that the recognition rate of the proposed ART1 algorithm was improved much more than that of the conventional ART1 algorithm. So, we develop an educational matters administration system by using the proposed recognition method of the student's identification card.

  • PDF

Performance Improvement of Human Detection in Thermal Images using Principal Component Analysis and Blob Clustering (주성분 분석과 Blob 군집화를 이용한 열화상 사람 검출 시스템의 성능 향상)

  • Jo, Ahra;Park, Jeong-Sik;Seo, Yong-Ho;Jang, Gil-Jin
    • The Journal of the Institute of Internet, Broadcasting and Communication
    • /
    • v.13 no.2
    • /
    • pp.157-163
    • /
    • 2013
  • In this paper, we propose a human detection technique using thermal imaging camera. The proposed method is useful at night or rainy weather where a visible light imaging cameras is not able to detect human activities. Under the observation that a human is usually brighter than the background in the thermal images, we estimate the preliminary human regions using the statistical confidence measures in the gray-level, brightness histogram. Afterwards, we applied Gaussian filtering and blob labeling techniques to remove the unwanted noise, and gather the scattered of the pixel distributions and the center of gravities of the blobs. In the final step, we exploit the aspect ratio and the area on the unified object region as well as a number of the principal components extracted from the object region images to determine if the detected object is a human. The experimental results show that the proposed method is effective in environments where visible light cameras are not applicable.

A Study on 3D Visualization for Color Analysis of Multimedia Data (멀티미디어 데이터의 색상분포 분석을 통한 3차원 시각화 연구)

  • Seo, Sanghyun
    • Journal of Digital Contents Society
    • /
    • v.19 no.8
    • /
    • pp.1463-1469
    • /
    • 2018
  • The development of multimedia devices with built-in cameras such as smart devices and various studies using video-related multimedia data such as images and video obtained from the devices have been actively conducted. These studies deal with image data. An image can be defined as a set of color information obtained from a digital sensor called a pixel. Images contain various cognitive information such as color, lighting, objects and so on. In order to extract or process such information, it is necessary to clearly understand the composition of colors. In this paper, we introduce 3-dimensional information visualization method which can effectively express the results of image processing together with color distribution. This study visualizes the characteristics of image related multimedia data as well as the characteristics of various analytical data derived from it, so that researchers can transmit the image information more clearly and effectively.

Efficient graph-based two-stage superpixel generation method (효율적인 그래프 기반 2단계 슈퍼픽셀 생성 방법)

  • Park, Sanghyun
    • Journal of the Korea Institute of Information and Communication Engineering
    • /
    • v.23 no.12
    • /
    • pp.1520-1527
    • /
    • 2019
  • Superpixel methods are widely used in the preprocessing stage as a method to reduce computational complexity by simplifying images while maintaining the characteristics of images in the field of computer vision. It is common to generate superpixels with a regular size and form based on the pixel values rather than considering the characteristics of the image. In this paper, we propose a method to generate superpixels considering the characteristics of an image according to the application. The proposed method consists of two steps, and the first step is to oversegment an image so that the boundary information of the image is well preserved. In the second step, superpixels are merged based on similarity to produce the desired number of superpixels, where the form of superpixels are controlled by limiting the maximum size of superpixels. Experimental results show that the proposed method preserves the boundaries of an image more accurately than the existing method.

Comparison of Algorithms for Generating Parametric Image of Cerebral Blood Flow Using ${H_2}^{15}O$ PET Positron Emission Tomography (${H_2}^{15}O$ PET을 이용한 뇌혈류 파라메트릭 영상 구성을 위한 알고리즘 비교)

  • Lee, Jae-Sung;Lee, Dong-Soo;Park, Kwang-Suk;Chung, June-Key;Lee, Myung-Chul
    • The Korean Journal of Nuclear Medicine
    • /
    • v.37 no.5
    • /
    • pp.288-300
    • /
    • 2003
  • Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.

A Study on the Accuracy of Calculating Slopes for Mountainous Landform in Korea Using GIS Software - Focused on the Contour Interval of Source Data and the Resolution - (GIS Software를 이용한 한국 산악 지형의 경사도 산출 정확도에 관한 연구 -원자료의 등고선 간격과 해상력을 중심으로-)

  • 신진민;이규석
    • Spatial Information Research
    • /
    • v.7 no.1
    • /
    • pp.1-12
    • /
    • 1999
  • The DTM(Digital Terrain Model) in GIS(Geographical Information System) shows the elevation from interpolation using data points surveyed. In panoramic flat landform, pixel size, resolution of source data may not be the problem in using DTM However, in mountainous landform like Korea, appropriate resolution accuracy of source data are important factors to represent the topography concerned. In this study, the difference in contour interval of source data, the resolution after interpolation, and different data structures were compared to figure out the accuracy of slope calculation using DTM from the topographic maps of Togyusan National Park Two types of GIS softwares, Idrisi(grid) ver. 2.0 using the altitude matrices and ArcView(TIN) ver. 3.0a using TIN were used for this purpose. After the analysis the conclusions are as follows: 1) The coarser resolution, the more smoothing effect inrepresenting the topography. 2) The coarser resolution the more difference between the grid-based Idrisi and the TIN-based ArcView. 3) Based on the comparison analysis of error for 30 points from clustering, there is not much difference among 10, 20, 30 m resolution in TIM-based Airview ranging from 4.9 to 6.2n However, the coarser resolution the more error for elevation and slope in the grid-based Idrisi. ranging from 6.3 to 10.9m. 4) Both Idrisi and ArcView could net consider breaklines of lanform like hilltops, valley bottoms.

  • PDF

Statistical Analysis of Projection-Based Face Recognition Algorithms (투사에 기초한 얼굴 인식 알고리즘들의 통계적 분석)

  • 문현준;백순화;전병민
    • The Journal of Korean Institute of Communications and Information Sciences
    • /
    • v.25 no.5A
    • /
    • pp.717-725
    • /
    • 2000
  • Within the last several years, there has been a large number of algorithms developed for face recognition. The majority of these algorithms have been view- and projection-based algorithms. Our definition of projection is not restricted to projecting the image onto an orthogonal basis the definition is expansive and includes a general class of linear transformation of the image pixel values. The class includes correlation, principal component analysis, clustering, gray scale projection, and matching pursuit filters. In this paper, we perform a detailed analysis of this class of algorithms by evaluating them on the FERET database of facial images. In our experiments, a projection-based algorithms consists of three steps. The first step is done off-line and determines the new basis for the images. The bases is either set by the algorithm designer or is learned from a training set. The last two steps are on-line and perform the recognition. The second step projects an image onto the new basis and the third step recognizes a face in an with a nearest neighbor classifier. The classification is performed in the projection space. Most evaluation methods report algorithm performance on a single gallery. This does not fully capture algorithm performance. In our study, we construct set of independent galleries. This allows us to see how individual algorithm performance varies over different galleries. In addition, we report on the relative performance of the algorithms over the different galleries.

  • PDF

A Novel Method for Automated Honeycomb Segmentation in HRCT Using Pathology-specific Morphological Analysis (병리특이적 형태분석 기법을 이용한 HRCT 영상에서의 새로운 봉와양폐 자동 분할 방법)

  • Kim, Young Jae;Kim, Tae Yun;Lee, Seung Hyun;Kim, Kwang Gi;Kim, Jong Hyo
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.1 no.2
    • /
    • pp.109-114
    • /
    • 2012
  • Honeycombs are dense structures that small cysts, which generally have about 2~10 mm in diameter, are surrounded by the wall of fibrosis. When honeycomb is found in the patients, the incidence of acute exacerbation is generally very high. Thus, the observation and quantitative measurement of honeycomb are considered as a significant marker for clinical diagnosis. In this point of view, we propose an automatic segmentation method using morphological image processing and assessment of the degree of clustering techniques. Firstly, image noises were removed by the Gaussian filtering and then a morphological dilation method was applied to segment lung regions. Secondly, honeycomb cyst candidates were detected through the 8-neighborhood pixel exploration, and then non-cyst regions were removed using the region growing method and wall pattern testing. Lastly, final honeycomb regions were segmented through the extraction of dense regions which are consisted of two or more cysts using cluster analysis. The proposed method applied to 80 High resolution computed tomography (HRCT) images and achieved a sensitivity of 89.4% and PPV (Positive Predictive Value) of 72.2%.