Statistical region merging

By Richard Nock, Frank Nielsen. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

902 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2bdf9b643748b28b5d891f320908c817acce4e9d

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In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-graph-view Learning for Graph Classification (2014, methodology): “The original images [18] from Corel dataset1 are preprocessed and segmented using Statistical Region Merging (SRM) [19], which has achieved a better performance on the Berkeley Segmentation Database compared to state-of-the-art techniques.”
Border detection in dermoscopy images using statistical region merging (2008, methodology): “In this paper, we present an unsupervised approach to border detection in dermoscopy images based on the statistical region merging (SRM) algorithm (15).”
Benchmarking modern multiprocessors (2011, methodology): “This function uses statistical region merging (SRM) [67] to segment the image.”
The PARSEC benchmark suite: Characterization and architectural implications (2008, methodology): “This function uses statistical region merging (SRM)[33] to segment the image.”
Image Segmentation Using Deep Learning: A Survey (2020, methodology): “Numerous image segmentation algorithms have been developed in the literature, from the earliest methods, such as thresholding [4], histogram-based bundling, regiongrowing [5], k-means clustering [6], watershed methods [7], to more advanced algorithms such as active contours [8], graph cuts [9], cond”
photoQuad: A dedicated seabed image processing software, and a comparative error analysis of four photoquadrat methods (2012, methodology): “…et al. (2011) demonstrated the effectiveness of segmentation-based methods in seabed image analysis. photoQuad is equipped with a statistical region merging algorithm developed by Nock and Nielsen (2004), selected upon its efficiency in the successful partitioning of highly complex benthic images.”
Seven-Point Checklist and Skin Lesion Classification Using Multitask Multimodal Neural Nets (2019, methodology): “To detect irregular dots and globules, they applied statistical region merging [15] to find candidate dark segments, extracted morphological features, and applied thresholds (set experimentally) to detect rounded areas.”
Recent progress in semantic image segmentation (2018, neutral): “Besides, edge-based approaches and region-growing methods (Nock and Nielsen 2004) are also other branches.”