Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectu...
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Most existing salient object detection algorithms face the problem of either under-or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation...
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image segmentation is a fundamental process in many image, video, and computer vision applications. It is very essential and critical to imageprocessing and patternrecognition, and determines the quality of final re...
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ISBN:
(纸本)9781479927654
image segmentation is a fundamental process in many image, video, and computer vision applications. It is very essential and critical to imageprocessing and patternrecognition, and determines the quality of final result of analysis and recognition. This paper presents a semi-supervised strategy to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized cut criterion is utilized to segment images into meaningful units. The experimental results conducted on Berkeley image databases and MSRC image databases demonstrate the effectiveness of the proposed strategy.
Automatic detection of PCB defects become a difficult work in electronics industry, with the rapid development of Integrated Circuit. A good detection method can effectively improve production efficiency and reduce th...
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Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key pr...
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ISBN:
(纸本)9781728176055;9781728176062
Optical flow estimation is an essential step for many real-world computer vision tasks. Existing deep networks have achieved satisfactory results by mostly employing a pyramidal coarse-to-fine paradigm, where a key process is to adopt warped target feature based on previous flow prediction to correlate with source feature for building 3D matching cost volume. However, the warping operation can lead to troublesome ghosting problem that results in ambiguity. Moreover, occluded areas are treated equally with non occluded regions in most existing works, which may cause performance degradation. To deal with these challenges, we propose a lightweight yet efficient optical flow network, named OAS-Net (occlusion aware sampling network) for accurate optical flow. First, a new sampling based correlation layer is employed without noisy warping operation. Second, a novel occlusion aware module is presented to make raw cost volume conscious of occluded regions. Third, a shared flow and occlusion awareness decoder is adopted for structure compactness. Experiments on Sintel and KITTI datasets demonstrate the effectiveness of proposed approaches.
Diagnostic ultrasound is one of useful and noninvasive tools for clinical medicine. However, due to its qualitative, subjective and experience-based nature, ultrasound images can be influenced by image conditions such...
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This paper presents sparse slow feature analysis (SFA) for efficient process monitoring and fault isolation, which is a new latent variable model for time series data. We first recast sparse SFA in terms of a novel re...
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A new calibration algorithm for multi-camera systems using the 1D calibration objects is proposed. The algorithm integrates the rank-4 factorization with Zhangpsilas method. The intrinsic parameters as well as the ext...
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A new calibration algorithm for multi-camera systems using the 1D calibration objects is proposed. The algorithm integrates the rank-4 factorization with Zhangpsilas method. The intrinsic parameters as well as the extrinsic parameters are recovered only by capturing with cameras the 1D objectpsilas rotations around a fixed point. The algorithm is based on factorization of the scaled measurement matrix, the projective depths of which is estimated in an analytic equation instead of a recursive form. For the conditions that there are more than 3 points on the 1D object, our algorithm may solve them by extending the scaled measurement matrix. The obtained parameters are finally refined through the maximum likelihood inference. The validity of the proposed technique was verified through simulation and experiments with real images.
The paper presents a novel fast matching algorithm between airborne and satellite-borne SAR images so as to efficiently integrate SAR images into GPSISINS navigation system. Because the character such as gray level an...
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To overcome the drawbacks of print measurements, a novel measurement approach, namely dot structure based measurement, is proposed. In addition, the structure feature extraction of microscopic dot is discussed. The pr...
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ISBN:
(纸本)0780382730
To overcome the drawbacks of print measurements, a novel measurement approach, namely dot structure based measurement, is proposed. In addition, the structure feature extraction of microscopic dot is discussed. The proposed scheme provides a precondition for such further research as print quality surveillance, fault diagnosis, color design and printing theories as well.
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