2014
DOI: 10.1109/tip.2014.2307475
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Images as Occlusions of Textures: A Framework for Segmentation

Abstract: Abstract-We propose a new mathematical and algorithmic framework for unsupervised image segmentation, which is a critical step in a wide variety of image processing applications. We have found that most existing segmentation methods are not successful on histopathology images, which prompted us to investigate segmentation of a broader class of images, namely those without clear edges between the regions to be segmented. We model these images as occlusions of random images, which we call textures, and show that… Show more

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Cited by 38 publications
(34 citation statements)
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“…In the section, a state-of-the-art image segmentation algorithm called ORT is employed. The segmentation method is proposed by McCann [25,26]. It is an unsupervised image segmentation method for our automatic extraction process.…”
Section: Building Structure Feature Extraction Methodsmentioning
confidence: 99%
“…In the section, a state-of-the-art image segmentation algorithm called ORT is employed. The segmentation method is proposed by McCann [25,26]. It is an unsupervised image segmentation method for our automatic extraction process.…”
Section: Building Structure Feature Extraction Methodsmentioning
confidence: 99%
“…For object region association, the remote sensing image segmentation method based on occlusion random texture model (ORT) is employed to extract the object areas of each image [17]. Then, sparse matching method based on SIFT feature is used to build the relationship between the same object areas of the mixed stereoscopic image pairs [18].…”
Section: Proposed Methodsmentioning
confidence: 99%
“…McCann et al [17] proposed the segmentation algorithm based on occlusion random texture model, which has an excellent performance in the unsupervised segmentation. Moreover, there are corresponding improved methods for extracting objects in remote sensing image processing.…”
Section: Object Region Associationmentioning
confidence: 99%
“…The method in [47] uses local pixel intensities as features and is able to segment bone, cartilage, and fat tissue in teratoma tumor images. We presented a segmentation method [48] inspired by the lack of edges in histology images. It uses local color histograms rather than edge-based features and outperforms generic methods for tissue segmentation.…”
Section: H Tissue Segmentationmentioning
confidence: 99%