Segmentation of Noisy Digital Images with Stochastic Gradient Kernel

Authors: Abhishek Neogi, Jayesh Verma, Pinaki Pratim Acharjya

Abstract:

Image segmentation and edge detection is a fundamental section in image processing. In case of noisy images Edge Detection is very less effective if we use conventional Spatial Filters like Sobel, Prewitt, LOG, Laplacian etc. To overcome this problem we have proposed the use of Stochastic Gradient Mask instead of Spatial Filters for generating gradient images. The present study has shown that the resultant images obtained by applying Stochastic Gradient Masks appear to be much clearer and sharper as per Edge detection is considered.

Keywords: Image segmentation, edge Detection, noisy images, spatialfilters, stochastic gradient kernel.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1107125

References:

[1] L. Vincent, "Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms," IEEE Transactions on Image Processing, vol. 2, pp. 176-201, 1993.
[2] P. Soille, “Morphological image analysis: principles and applications”, in Springer-Verlag New York, Inc. Secaucus, NJ, USA, 1999.
[3] J. Serra, “Image analysis and mathematical morphology”: Academic Press, Inc. Orlando, FL, USA, 1983.
[4] J. Serra and L. Vincent, "An overview of morphological filtering", Circuits, Systems, and Signal Processing, vol. 11, pp. 47-108, 1992.
[5] W. J. Niessen, K. L. Vincken, J. A. Weickert,and M. A. Viergever, "Nonlinear multiscale representations for image segmentation," Computer Vision and Image Understanding, vol. 66, pp. 233-245, 1997.
[6] A.K.Jain, “Fundamentals of digital image processing”, Second Edition, Prentice Hall, 2002.
[7] Canny, J., “A Computational Approach to Edge Detection”, IEEE Trans. Pattern Analysis and Machine Intelligence, 8(6):679–698, 1986.
[8] R. Deriche, Using Canny's criteria to derive a recursively implemented optimal edge detector, Int. J. Computer Vision, Vol. 1, pp. 167–187, April 1987.
[9] C. Gonzalez, Richard E. Woods, “Digital Image Processing”, 2nd Edition, Addison Wesley Pub. Co, 2002.