Abstract
Automatic image segmentation is a challenging task in computer vision applications, especially in the presence of occluded objects, varying color, and different lighting conditions. The advancement of depth-sensing technologies has introduced RGB-Depth cameras which are capable to generate RGB-Depth images and brought significant changes in computer vision applications. However, the segmentation of RGB-Depth images is a difficult task. Therefore, in this paper, a new segmentation method for RGB-Depth images has been introduced and named as random Henry gas solubility optimization-fuzzy clustering method. Firstly, a random Henry gas solubility optimization algorithm has been developed. Next, the proposed optimization algorithm has been employed to obtain optimal fuzzy clusters which are finally merged through segmentation by aggregating superpixels. The standard NYU depth V2 RGB-Depth indoor image dataset is used for performance evaluation. The proposed segmentation approach has been compared with five different methods namely, kmeans, fuzzy c-means, Henry gas solubility optimization algorithm, chaotic gravitational search algorithm, and J-Segmentation in terms of qualitative and quantitative measures. The result analysis shows that the proposed RGB-D segmentation method outperforms the other considered methods.
Published Version
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