Measuring distinct regions of grayscale image using pixel values

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Grayscale is a series of shades of gray without apparent color. The total absence of transmitted or reflected light, which is the darkest shade, black. The total reflection or transmission of light at all observable wavelengths, which is nothing but lightest possible shade i.e., white. Intermediate shades of gray are characterized by equal brightness levels of the primary colors (red, green and blue) for transmitting light, or equal amounts of the three primary pigments (magenta,cyan, and yellow) for reflected light. This paper focuses mainly on measuring the properties of objects in a grayscale image using Regionprops to calculate the standard Deviation. This is achieved by segmenting a grayscale image to get objects of a binary image. Although, the common problem of including chromatic values to a grayscale image has objective solution,not exact, the present approach tries to provide an approach to help minimize the amount of human labor required for this task. We transfer the source’s whole color “mood” to the target image by matching texture information and luminance between the images rather than selecting RGB colors from a group of colors to an individual color components. We pick out to transfer only chromatic information and retain the target image’s original luminance values. Further, the procedure is improved by permitting the user to match areas of the two images with rectangular swatches. It is essential to develop grayscale image pixel value, resultant to each object in the binary image to inspect the original grayscale image.Based on the original grayscale image pixel values, the pixel value properties in grayscale image are used to do routine calculations.

  • Keywords

    Grayscale image, regionprops, binary image, pixel value.

  • References

      [1] Mondal S & Mukherjee J, “Image Similarity Measurement using Region Props, Color and Texture: An Approach”, International Journal of Computer Applications, Vol.121, No.22, (2015).

      [2] Sharma N, Chawla G & Khurana M, “Weighted centroid range free localization algorithm based on IOT”, International journal of Computer Application, Vol.83, No.9, pp.27-30, (2013).

      [3] Fazal-e-malik, “Mean and standard deviation features of color histogram using laplacian filter for content based image retrieval”, Journal of theoretical and applied information technology, Vol.34, No.1, pp.1-7, (2011).

      [4] Jassim FA, “Semi-optimal edge detector based on simple standard deviation with adjusted thresholding”, International Journal computer Application, Vol.68, No.2, pp.43-48, (2013).

      [5] Joseph Zacharias, Jayakrishnan SB & Vijayakumar Narayanan, “82 GHz Millimeter-Wave Transmission Over OFDM ROF System”, (2016).

      [6] Semwal A, Arya MC, Chamoli A & Bhatt U, “A Survey: On Image Segmentation And Its Various Techniques”, International Research Journal of Engineering and Technology, Vol.03, No.12, pp.1565-1568, (2016).

      [7] Vartak AP & Mankar V, “Colour image segmentation-A survey”, International Journal of Emerging Technology and Advanced Engineering, Vol.3, No.2, pp.681-688, (2013).

      [8] Preeti Rani & Raghuvinder Bhardwaj, “An Approach of Colour Based Image Segmentation Technique for Differentiate Objects using MATLAB Simulation”, International Journal of Advanced Research in Computer and Communication Engineering, Vol.5, No.7, pp.553-556, (2016).

      [9] Sharma P & Suji J, “A Review on Image Segmentation with its Clustering Techniques”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.9, No.5, pp.209-218, (2016).

      [10] Parandekar AB, Dhande SS & Vhyawhare HR, “A Review on Changing Image from Grayscale to Color”, International Journal of Advanced Research in Computer Engineering & Technology, Vol.3, No.1, pp.143-146, (2014).

      [11] Kumar K, Li JP & Shaikh RA, “Content Based Image Retrieval Using Gray Scale Weighted Average Method”, International Journal of Advanced Computer Science and Applications, Vol.7, No.1, (2016).

      [12] Das S & Sarkar TS, “A new method of linear displacement measurement utilizing a grayscale image”, International Journal of Electronics and Electrical Engineering, Vol.1, No.3, pp.176-181, (2013).

      [13] Zhang, X & Brainard DH, “Estimation of saturated pixel values in digital color imaging”, JOSA A, Vol.21, No.12, pp.2301-2310, (2004).

      [14] Bin L, Zheng D, Yu N & Yun L, “An Improved Weighted Centroid Localization Algorithm”, International Journal of Future Generation Communication & Networks, Vol.6, No.5, (2013).

      [15] Borgmann LAK, Ries J, Ewers H, Ulbrich MH & Graumann PL, “The bacterial SMC complex displays two distinct modes of interaction with the chromosome”, Cell reports, Vol.3, No.5, pp.1483-1492, (2013).

      [16] Sharadqeh AAM, “Linear model of resolution and quality of digital images”, Contemporary Engineering Sciences, Vol.5, No.6, pp.273–279, (2012).

      [17] Kumar BV & Karpagam GR, “An empirical analysis of requantization errors for recompressed JPEG images”, IJEST, Vol.3, No.12, (2011).

      [18] Sekeh MA, Maarof MA, Rohani MF & Mahdian B, “Efficient image duplicated region detection model using sequential block clustering”, Digit Investig, Vol.10, pp.73–84, (2013).

      [19] Murugan V, Avudaiappan TR & Balasubramanian B, “A Comparative Analysis of Impulse Noise Removal Techniques on Gray Scale Images”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol.7, No.5, pp.239-248, (2014).




Article ID: 9210
DOI: 10.14419/ijet.v7i1.1.9210

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.