• Title, Summary, Keyword: Histogram

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Image Contrast Enhancement based on Histogram Decomposition and Weighting (히스토그램 분할과 가중치에 기반한 영상 콘트라스트 향상 방법)

  • Kim, Ma-Ry;Chung, Min-Gyo
    • Journal of Internet Computing and Services
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    • v.10 no.3
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    • pp.173-185
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    • 2009
  • This paper proposes two new image contrast enhancement methods, RSWHE (Recursively Separated and Weighted Histogram Equalization) and RSWHS (Recursively Separated and Weighted Histogram Specification). RSWHE is a histogram equalization method based on histogram decomposition and weighting, whereas RSWHS is a histogram specification method based on histogram decomposition and weighting. The two proposed methods work as follows: 1) decompose an input histogram based on the image's mean brightness, 2) compute the probability for the area corresponding to each sub-histogram, 3) modify the sub-histogram by weighting it with the computed probability value, 4) lastly, perform histogram equalization (in the case of RSWHE) or histogram specification (in the case of RSWHS) on the modified sub-histograms independently. Experimental results show that RSWHE and RSWHS outperform other methods in terms of contrast enhancement and mean brightness preservation as well.

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DWTHE: Decomposable Weighted and Thresholded Histogram Equalization (DWTHE: 분할 기반의 히스토그램 평활화)

  • Kim, Mary;Chung, Min-Gyo
    • Journal of KIISE:Computing Practices and Letters
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    • v.15 no.11
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    • pp.856-860
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    • 2009
  • Wang and Ward developed an image contrast enhancement method called WTHE (Weighted and Thresholded Histogram Equalization). In this paper, we propose an improved variant of WTHE called DWTHE(Decomposable WTHE) that enhances WTHE through the use of histogram decomposition. Specifically, DWTHE divides an input histogram by using image's mean brightness or equally-spaced brightness points, computes a probability value for each sub-histogram, modifies the sub-histograms by using those probability values as weights, and then performs histogram equalization on the modified input histogram. As opposed to WTHE that uses a single weight, DWTHE uses multiple weights derived from histogram decomposition. Experimental results show that the proposed method outperforms the previous histogram equalization based methods.

Shifted Histogram Matching Algorithm for Image Retrieval (영상 검색을 위한 Shifted 히스토그램 정합 알고리즘)

  • Yoo, Gi-Hyoung;Yoo, Seung-Sun;Youk, Sang-Jo;Park, Gil-Cheol
    • Convergence Security Journal
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    • v.7 no.1
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    • pp.107-113
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    • 2007
  • This paper proposes the shifted histogram method (SHM), for histogram-based image retrieval based on the dominant colors in images. The histogram-based method is very suitable for color image retrieval because retrievals are unaffected by geometrical changes in images, such as translation and rotation. Images with the same visual information, but with shifted color intensity, may significantly degrade if the conventional histogram intersection method (HIM) is used. To solve this problem, we use the shifted histogram method (SHM). Our experimental results show that the shifted histogram method has significant higher retrieval performance than the standard histogram method.

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Hadoop Based Wavelet Histogram for Big Data in Cloud

  • Kim, Jeong-Joon
    • Journal of Information Processing Systems
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    • v.13 no.4
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    • pp.668-676
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    • 2017
  • Recently, the importance of big data has been emphasized with the development of smartphone, web/SNS. As a result, MapReduce, which can efficiently process big data, is receiving worldwide attention because of its excellent scalability and stability. Since big data has a large amount, fast creation speed, and various properties, it is more efficient to process big data summary information than big data itself. Wavelet histogram, which is a typical data summary information generation technique, can generate optimal data summary information that does not cause loss of information of original data. Therefore, a system applying a wavelet histogram generation technique based on MapReduce has been actively studied. However, existing research has a disadvantage in that the generation speed is slow because the wavelet histogram is generated through one or more MapReduce Jobs. And there is a high possibility that the error of the data restored by the wavelet histogram becomes large. However, since the wavelet histogram generation system based on the MapReduce developed in this paper generates the wavelet histogram through one MapReduce Job, the generation speed can be greatly increased. In addition, since the wavelet histogram is generated by adjusting the error boundary specified by the user, the error of the restored data can be adjusted from the wavelet histogram. Finally, we verified the efficiency of the wavelet histogram generation system developed in this paper through performance evaluation.

Content-Based Image Retrieval using Color Feature of Region and Adaptive Color Histogram Bin Matching Method (영역의 컬러특징과 적응적 컬러 히스토그램 빈 매칭 방법을 이용한 내용기반 영상검색)

  • Park, Jung-Man;Yoo, Gi-Hyoung;Jang, Se-Young;Han, Deuk-Su;Kwak, Hoon-Sung
    • Proceedings of the KIEE Conference
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    • pp.364-366
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    • 2005
  • From the 90's, the image information retrieval methods have been on progress. As good examples of the methods, Conventional histogram method and merged-color histogram method were introduced. They could get good result in image retrieval. However, Conventional histogram method has disadvantages if the histogram is shifted as a result of intensity change. Merged-color histogram, also, causes more process so, it needs more time to retrieve images. In this paper, we propose an improved new method using Adaptive Color Histogram Bin Matching(AHB) in image retrieval. The proposed method has been tested and verified through a number of simulations using hundreds of images in a database. The simulation results have Quickly yielded the highly accurate candidate images in comparison to other retrieval methods. We show that AHB's can give superior results to color histograms for image retrieval.

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An Improved Histogram Specification using Multiresolution in the Spatial Domain for Image Enhancement (이미지 향상을 위해 공간영역에서 다중해상도를 이용한 개선된 히스토그램 특정화 방법)

  • Huh, Kyung-Moo
    • Journal of Institute of Control, Robotics and Systems
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    • v.20 no.6
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    • pp.657-662
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    • 2014
  • Usually, spatial information can be incorporated into histograms by taking histograms of a multiresolution image. For these reasons, many researchers are interested in multiresolution histogram processing. If the relation and sensitivity of the multiresolution images are well combined without loss of information, we can obtain satisfactory results in several fields of image processing including histogram equalization, specification and pattern matching. In this paper, we propose a multiresolution histogram specification method that improves the accuracy of histogram specification. The multiresolution decomposition technique is used in order to overcome the unique feature of a histogram specification affected by a quantization error of a digitalized image. The histogram specification is processed after the reduction of image resolution in order to enhance the accuracy of the results by histogram specification methods. The experimental results show that the proposed method enhances the accuracy of specification compared to conventional methods.

Piecewise Continuous Linear Density Estimator

  • Jang, Dae-Heung
    • Journal of the Korean Data and Information Science Society
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    • v.16 no.4
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    • pp.959-968
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    • 2005
  • The piecewise linear histogram can be used as a simple and efficient tool for the density estimator. But, this piecewise linear histogram is discontinuous function. We suppose the piecewise continuous linear histogram as a simple and efficient tool for the density estimator and the alternative of the piecewise linear histogram.

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An Improved Histogram-Based Image Hash (Histogram에 기반한 Image Hash 개선)

  • Kim, So-Young;Kim, Hyoung-Joong
    • 한국정보통신설비학회:학술대회논문집
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    • pp.531-534
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    • 2008
  • Image Hash specifies as a descriptor that can be used to measure similarity in images. Among all image Hash methods, histogram based image Hash has robustness to common noise-like operation and various geometric except histogram _equalization. In this_paper an improved histogram based Image Hash that is using "Imadjust" filter I together is proposed. This paper has achieved a satisfactory performance level on histogram equalization as well as geometric deformation.

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Efficient Contrast Enhancement Algorithm using Histogram Stretching (히스토그램 스트레칭을 이용한 효율적인 명암 향상 알고리즘)

  • Kim, Young Ro;Chung, Ji Yung
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.6 no.2
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    • pp.193-198
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    • 2010
  • In this paper, an efficient contrast enhancement algorithm using histogram stretching is proposed. Histogram equalization (HE) and histogram stretching (HS) are effective techniques for contrast enhancement. However, HE and HS result often in excessive contrast enhancement. Proposed technique not only produces better results than those of conventional contrast enhancement techniques, but is also adaptively adjusted to image contents.

Obstacle avoidance using Vector Field Histogram in simulation (Vector Field Histogram를 이용한 장애물 회피 시뮬레이션)

  • 정현룡;김영배
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.1076-1079
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    • 2003
  • The vector field histogram(VFH) uses a two-dimensional Cartesian histogram grid as a world model. The VFH method subsequently employs a two-stage data-reduction process in order to compute the desired control commands for the vehicle. In the first stage the histogram grid is reduced to a one dimensional polar histogram that is constructed around the robot's momentary location. Each sector in the polar histogram contains a value representing the polar obstacle density in that direction. In the second stage, the algorithm selects the most suitable sector from among all polar histogram sectors with a low polar obstacle density, and the steering of the robot is aligned with that direction. We applied this algorithm to our simulation program and tested..

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