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• We propose a method for multiscale, border-guided 360 ∘ image oversegmentation. • We propose a superpixel method for 360 ∘ images that concern borders and regularity. • We propose an annotation-free metric for assessing 360 ∘ image segmentation methods. • The novel methods are compared and assessed under reference metrics and datasets. • Our methods achieve state-of-the-art results and are faster than peering approaches. Superpixels are fundamental in many visual computing applications, and most existing algorithms are designed to work with pinhole-based images. However, immersive applications are gaining visibility with the growing number of devices for capturing and visualizing 360 ∘ media. This paper introduces two fast and accurate superpixel algorithms tailored to the spherical domain. The methods are assessed under common figures of merit, and a benchmark annotated dataset. Additionally, an annotation-free evaluation metric for exploiting larger datasets is introduced. The methods introduced in this paper perform quantitatively close to or better than state-of-the-art approaches and are faster. [ABSTRACT FROM AUTHOR]

Evaluation of image segmentation algorithms is a crucial task in the image processing field. Generally, traditional objective evaluation measures, such as ME and JS, always give the same treatment to the object pixels and the background pixels in images, which is not reasonable in practical applications. To overcome this problem, a new objective evaluation metric based on the weighted-ROC graph is proposed in this paper. Considering that pixels in different positions may gain different importance, each pixel is given a weight based on its spatial information. The ROC (receiver operating characteristic) graph with weighting strategy is constructed to evaluate the performance of segmentation algorithms quantitatively. The proposed metric focuses on the segmented objects, which is similar to human visual system. Meanwhile, it reserves the robustness of ROC against the region imbalance. The experimental results on various images show that the proposed metric gives more reasonable evaluation results than other metrics. [ABSTRACT FROM AUTHOR]