Images as Occlusions of Textures: A Framework for Segmentation

McCann, Michael T.; Mixon, Dustin G.; Fickus, Matthew; Castro, Carlos; Ozolek, John A.; Kovačević, Jelena

doi:10.1109/tip.2014.2307475

Cited by 38 publications

(34 citation statements)

References 46 publications

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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%

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

Yan

Qiu

et al. 2018

Sensors

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In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods.

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Section: Building Structure Feature Extraction Methodsmentioning

confidence: 99%

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

Yan

Qiu

et al. 2018

Sensors

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“…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%

Object-Oriented Hierarchy Radiation Consistency for Different Temporal and Different Sensor Images

Yan

Zhao

et al. 2018

Sensors

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In the paper, we propose a novel object-oriented hierarchy radiation consistency method for dense matching of different temporal and different sensor data in the 3D reconstruction. For different temporal images, our illumination consistency method is proposed to solve both the illumination uniformity for a single image and the relative illumination normalization for image pairs. Especially in the relative illumination normalization step, singular value equalization and linear relationship of the invariant pixels is combined used for the initial global illumination normalization and the object-oriented refined illumination normalization in detail, respectively. For different sensor images, we propose the union group sparse method, which is based on improving the original group sparse model. The different sensor images are set to a similar smoothness level by the same threshold of singular value from the union group matrix. Our method comprehensively considered the influence factors on the dense matching of the different temporal and different sensor stereoscopic image pairs to simultaneously improve the illumination consistency and the smoothness consistency. The radiation consistency experimental results verify the effectiveness and superiority of the proposed method by comparing two other methods. Moreover, in the dense matching experiment of the mixed stereoscopic image pairs, our method has more advantages for objects in the urban area.

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“…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%