A Probabilistic Approach for Color Correction in Image Mosaicking Applications

  • November 19, 2015
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A Probabilistic Approach for Color Correction in Image Mosaicking Applications

ABSTRACT:

Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.

EXISTING SYSTEM:

  • Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. Several previous works on color correction have shown the advantages of using a local approach when compared to a global one.
  • Local methods presented in and have shown better performance compared with the global approach presented in.

DISADVANTAGES OF EXISTING SYSTEM:

  • The photometrical correspondence between images deals with the photometrical alignment of image capturing devices. The same object, under the same lighting conditions, should be represented by the same color in two different images. However, even in sets of images taken from the same camera, the colors representing an object may differ from picture to picture.
  • This poses a problem to the fusion of information from several images. Hence, the problem of how to balance the color of one picture so that it matches the color of another must be tackled.

PROPOSED SYSTEM:

  • The proposed one of the first parametric methods for color correction. Single Gaussians are used to model the color distributions of the target and source images.
  • The color distribution of the S image is then transferred to the T image by scaling and offsetting according to the mean and standard deviations of the precomputed Gaussians.
  • With this in mind, some subsequent works have proposed to use more complex models, in an attempt to achieve more accurate models of the color distributions, and as a result, more effective color corrections.
  • The usage of Gaussian Mixture Models (GMMs) was proposed in . The GMM was fitted to the data using a Expectation Maximization (EM) methodology. The GMM could not only model the color distribution more accurately, but also assist in the probabilistic segmentation of the image into regions. For each segmented region, a color transfer methodology similar to the one proposed in was proposed. One other problem that may hamper the performance of color correction methodologies is that each color channel in the image is modelled and corrected independently.
  • Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure.
  • Then, local color palette mapping functions are computed using these sets of Gaussians.
  • The color correction is performed by applying those functions to all the regions of the image.
  • An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets.
  • Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.

ADVANTAGES OF PROPOSED SYSTEM:

  • The proposed approach is well suited for handling realistic scenarios.
  • The usage of truncated Gaussians as well as the indirect modelling of the joint image histogram also improves the effectiveness of the color correction algorithm.

SYSTEM ARCHITECTURE:

3

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

  • System : Pentium IV 2.4 GHz.
  • Hard Disk : 40 GB.
  • Floppy Drive : 44 Mb.
  • Monitor : 15 VGA Colour.
  • Mouse :
  • Ram : 512 Mb.

SOFTWARE REQUIREMENTS:

  • Operating system : Windows XP/7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

REFERENCE:

Miguel Oliveira, Angel Domingo Sappa, Senior Member, IEEE, and Vitor Santos, Member, IEEE, “A Probabilistic Approach for Color Correction in Image Mosaicking Applications”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 2, FEBRUARY 2015.

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