Hough transform for robust segmentation of underwater multispectral images

A segmentation algorithm for underwater multispectral images based on the Hough transform (HT) is presented. The segmentation algorithm consists of three stages: The first stage consists in computing the HT of the original image and segmenting the desired object in its boundary. The HT has several known challenges such as the end point (infinite lines) and the connectivity problem, which lead to false contours. Most of these problems are canceled over the next two stages. The second stage starts by clustering the original image. Fuzzy C-means clustering segmentation technique is used to capture the local properties of the desired object. In the third stage, the edges of the clustering segmentation are extended to the closest HT detected lines. The boundary information (HT) and local properties (Fuzzy C-means) of the desired object are fused together and false contours are eliminated. The performance of the segmentation algorithm is demonstrated in underwater multispectral images generated in laboratory containing known objects of varying size and shape.

[1]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[2]  Raymond K. K. Yip,et al.  Line Patterns Hough Transform for line segment detection , 1994, Proceedings of TENCON'94 - 1994 IEEE Region 10's 9th Annual International Conference on: 'Frontiers of Computer Technology'.

[3]  V. F. F. Leavers Shape Detection in Computer Vision Using the Hough Transform , 2011 .

[4]  S. Araki,et al.  Segmentation of thermal images using the fuzzy C-means algorithm , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.

[5]  Chung-Lin Huang,et al.  Hough transform modified by line connectivity and line thickness , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Michael R. Lyu,et al.  A new approach for line recognition in large-size images using Hough transform , 2002, Object recognition supported by user interaction for service robots.

[8]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[9]  O DudaRichard,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972 .