• Title/Summary/Keyword: color image segmentation

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Mobile Palmprint Segmentation Based on Improved Active Shape Model

  • Gao, Fumeng;Cao, Kuishun;Leng, Lu;Yuan, Yue
    • Journal of Multimedia Information System
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    • v.5 no.4
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    • pp.221-228
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    • 2018
  • Skin-color information is not sufficient for palmprint segmentation in complex scenes, including mobile environments. Traditional active shape model (ASM) combines gray information and shape information, but its performance is not good in complex scenes. An improved ASM method is developed for palmprint segmentation, in which Perux method normalizes the shape of the palm. Then the shape model of the palm is calculated with principal component analysis. Finally, the color likelihood degree is used to replace the gray information for target fitting. The improved ASM method reduces the complexity, while improves the accuracy and robustness.

A Study on the Fire Flame Region Extraction Using Block Homogeneity Segmentation (블록 동질성 분할을 이용한 화재불꽃 영역 추출에 관한 연구)

  • Park, Changmin
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.14 no.4
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    • pp.169-176
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    • 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.

Optimal Combination of Component Images for Segmentation of Color Codes (칼라 코드의 영역 분할을 위한 성분 영상들의 최적 조합)

  • Kwon B. H;Yoo H-J.;Kim T. W.;Kim K D.
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.42 no.1
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    • pp.33-42
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    • 2005
  • Identifying color codes needs precise color information of their constituents, and is far from trivial because colors usually suffer severe distortions throughout the entire procedures from printing to acquiring image data. To accomplish accurate identification of colors, we need a reliable segmentation method to separate different color regions from each other, which would enable us to process the whole pixels in the region of a color statistically, instead of a subset of pixels in the region. Color image segmentation can be accomplished by performing edge detection on component image(s). In this paper, we separately detected edges on component images from RGB, HSI, and YIQ color models, and performed mathematical analyses and experiments to find out a pair of component images that provided the best edge image when combined. The best result was obtained by combining Y- and R-component edge images.

Fish Injured Rate Measurement Using Color Image Segmentation Method Based on K-Means Clustering Algorithm and Otsu's Threshold Algorithm

  • Sheng, Dong-Bo;Kim, Sang-Bong;Nguyen, Trong-Hai;Kim, Dae-Hwan;Gao, Tian-Shui;Kim, Hak-Kyeong
    • Journal of Power System Engineering
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    • v.20 no.4
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    • pp.32-37
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    • 2016
  • This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu's threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu's threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu's threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu' threshold algorithm.

Region-Based Gradient and Its Application to Image Segmentation

  • Kim, Hyoung Seok
    • International journal of advanced smart convergence
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    • v.7 no.4
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    • pp.108-113
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    • 2018
  • In this study, we introduce a new image gradient computation based on understanding of image generation. Most images consist of groups of pixels with similar color information because the images are generally obtained by taking a picture of the real world. The general gradient operator for an image compares only the neighboring pixels and cannot obtain information about a wide area, and there is a risk of falling into a local minimum problem. Therefore, it is necessary to attempt to introduce the gradient operator of the interval concept. We present a bow-tie gradient by color values of pixels on bow-tie region of a given pixel. To confirm the superiority of our study, we applied our bow-tie gradient to image segmentation algorithms for various images.

A Study on Color Fuzzy Decision Algorithm in Video Object Segmentation

  • Byun, Oh-Sung;Moon, Sung-Ryong
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.2
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    • pp.142-148
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    • 2004
  • In this paper, we propose the color fuzzy decision algorithm to face segmentation in a color image. Our algorithm can segment without the user's interaction by fuzzy decision marking. And it removes small parts such as a noise using wavelet morphology in the image obtained by applying the fuzzy decision algorithm. Also, it merges and chooses the face region in each quantization image through rough sets. This video object division algorithm is shown to be superior to a conventional algorithm.

Segmentation of Color Image Using the Deterministic Anneanling EM Algorithm (결정적 어닐링 EM 알고리즘을 이용한 칼라 영상의 분할)

  • 박종현;박순영;조완현
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.569-572
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    • 1999
  • In this paper we present a color image segmentation algorithm based on statistical models. A novel deterministic annealing Expectation Maximization(EM) formula is derived to estimate the parameters of the Gaussian Mixture Model(GMM) which represents the multi-colored objects statistically. The experimental results show that the proposed deterministic annealing EM is a global optimal solution for the ML parameter estimation and the image field is segmented efficiently by using the parameter estimates.

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Video image segmentation based on color histogram and change detector (칼라 히스토그램과 변화 검출기에 기반한 비디오 영상 분할)

  • 박진우;정의윤;김희수;송근원;하영호
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.1093-1096
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    • 1999
  • In this paper, video image segmentation algorithm based on color histogram and change detector is proposed. Color histograms are calculated from both changed region which is detected in the previous and current frame and unchanged region. With each histogram, modes and valleys are detected. Then, color vectors are calculated by averaging pixels in modes. Markers are extracted by labeling color vectors that represent modes, the watershed algorithm is applied to determine uncertain region. In growing region, the root mean square(RMS) of the distance between average pixel in marker region and adjacent pixel is used as a measure. The proposed algorithm based on color histogram and change detector segments video image fastly and effectively. And simulation results show that the proposed method determines the exact boundary between background and foreground.

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Region-based Content Retrieval Algorithm Using Image Segmentation (영상 분할을 이용한 영역기반 내용 검색 알고리즘)

  • Rhee, Kang-Hyeon
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.44 no.5
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    • pp.1-11
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    • 2007
  • As the availability of an image information has been significantly increasing, necessity of system that can manage an image information is increasing. Accordingly, we proposed the region-based content retrieval(CBIR) algorithm based on an efficient combination of an image segmentation, an image texture, a color feature and an image's shape and position information. As a color feature, a HSI color histogram is chosen which is known to measure spatial of colors well. We used active contour and CWT(complex wavelet transform) to perform an image segmentation and extracting an image texture. And shape and position information are obtained using Hu invariant moments in the luminance of HSI model. For efficient similarity computation, the extracted features(color histogram, Hu invariant moments, and complex wavelet transform) are combined and then precision and recall are measured. As a experimental result using DB that was supported by www.freefoto.com. the proposed image retrieval engine have 94.8% precision, 82.7% recall and can apply successfully image retrieval system.