The Experts below are selected from a list of 69735 Experts worldwide ranked by ideXlab platform
Ke Gao - One of the best experts on this subject based on the ideXlab platform.
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maximally visual Homogeneous Region detector for large scale image retrieval
International Conference on Multimedia Retrieval, 2015Co-Authors: Gang Wang, Ke GaoAbstract:Conventional local detectors often extract numerous small repeated Regions in textured areas, which easily results in false matching. In order to find representative and distinctive local invariant Regions, this paper proposes a Maximally Visual-Homogeneous Region (MVHR) detector. The main contributions can be summarized as 2 parts: (1) Being different from original MSER which employs single pixel intensity as ranking unit, we propose a novel sorting method based on visual homogeneity analysis on a local patch. (2) Identifying the observation scale has a close relationship with visual homogeneity analysis, a heuristic scale selection algorithm is developed to choose a proper scale according to the changes of visual homogeneity evaluation over a range of scales. Experiments demonstrate our detector can find less but representative Regions with high repeatability, while still perserving competitive precision compared to the state-of-art detectors for large scale image retrieval.
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ICMR - Maximally Visual-Homogeneous Region Detector for Large Scale Image Retrieval
Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15, 2015Co-Authors: Gang Wang, Ke GaoAbstract:Conventional local detectors often extract numerous small repeated Regions in textured areas, which easily results in false matching. In order to find representative and distinctive local invariant Regions, this paper proposes a Maximally Visual-Homogeneous Region (MVHR) detector. The main contributions can be summarized as 2 parts: (1) Being different from original MSER which employs single pixel intensity as ranking unit, we propose a novel sorting method based on visual homogeneity analysis on a local patch. (2) Identifying the observation scale has a close relationship with visual homogeneity analysis, a heuristic scale selection algorithm is developed to choose a proper scale according to the changes of visual homogeneity evaluation over a range of scales. Experiments demonstrate our detector can find less but representative Regions with high repeatability, while still perserving competitive precision compared to the state-of-art detectors for large scale image retrieval.
L. Pottier - One of the best experts on this subject based on the ideXlab platform.
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Micrometer scale visualization of thermal waves by photoreflectance microscopy
Applied Physics Letters, 1994Co-Authors: L. PottierAbstract:We propose a novel approach of photoreflectance microscopy that provides a direct visualization of the phase contour lines of the thermal wave. The method is applicable to (possibly heterogeneous) samples of mediocre polish. In a locally Homogeneous Region it yields the local thermal diffusivity.
Gang Wang - One of the best experts on this subject based on the ideXlab platform.
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maximally visual Homogeneous Region detector for large scale image retrieval
International Conference on Multimedia Retrieval, 2015Co-Authors: Gang Wang, Ke GaoAbstract:Conventional local detectors often extract numerous small repeated Regions in textured areas, which easily results in false matching. In order to find representative and distinctive local invariant Regions, this paper proposes a Maximally Visual-Homogeneous Region (MVHR) detector. The main contributions can be summarized as 2 parts: (1) Being different from original MSER which employs single pixel intensity as ranking unit, we propose a novel sorting method based on visual homogeneity analysis on a local patch. (2) Identifying the observation scale has a close relationship with visual homogeneity analysis, a heuristic scale selection algorithm is developed to choose a proper scale according to the changes of visual homogeneity evaluation over a range of scales. Experiments demonstrate our detector can find less but representative Regions with high repeatability, while still perserving competitive precision compared to the state-of-art detectors for large scale image retrieval.
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ICMR - Maximally Visual-Homogeneous Region Detector for Large Scale Image Retrieval
Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15, 2015Co-Authors: Gang Wang, Ke GaoAbstract:Conventional local detectors often extract numerous small repeated Regions in textured areas, which easily results in false matching. In order to find representative and distinctive local invariant Regions, this paper proposes a Maximally Visual-Homogeneous Region (MVHR) detector. The main contributions can be summarized as 2 parts: (1) Being different from original MSER which employs single pixel intensity as ranking unit, we propose a novel sorting method based on visual homogeneity analysis on a local patch. (2) Identifying the observation scale has a close relationship with visual homogeneity analysis, a heuristic scale selection algorithm is developed to choose a proper scale according to the changes of visual homogeneity evaluation over a range of scales. Experiments demonstrate our detector can find less but representative Regions with high repeatability, while still perserving competitive precision compared to the state-of-art detectors for large scale image retrieval.
Shuang Wang - One of the best experts on this subject based on the ideXlab platform.
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local maximal Homogeneous Region search for sar speckle reduction with sketch based geometrical kernel function
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Jie Wu, Licheng Jiao, Xiangrong Zhang, Shuang WangAbstract:With the flourish of the nonlocal mean method, the neighborwise similarity metric is widely applied in speckle reduction for its robust performance on the search of similar samples. In this metric, an isotropic kernel function is usually chosen to aggregate the corresponding pixels' distance between two neighborhoods. It means that the kernel function is considered as the explanation of the local spatial relationship at each pixel. However, for anisotropic features (such as edges and lines), a strong relationship exists along their directions rather than across them, so the isotropic kernel is not suitable to explain the spatial relationship around these features. Meanwhile, due to the inherent speckle in synthetic aperture radar (SAR) images, the discrimination and exploration of the geometrical properties of anisotropic features are important for the construction of adaptive kernel function. In this paper, the sketch map which is a representation of the sketch information of SAR images is extracted as the criterion for designing the kernel function. Meanwhile, due to the properties of symmetric and maximal self-similarity, a modified ratio distance is proposed and used jointly with the constructed kernel function as a similarity metric. Then, under the local stationary assumption, the local maximal Homogeneous Region of each pixel is searched by using the Region growing method with the proposed metric. Moreover, maximal likelihood rule is used within the Region for the estimation of true value. From the experiments on the synthetic and real SAR images, a promising performance in terms of speckle reduction and preservation of the details is achieved by our proposed method.
Kenneth R Sloan - One of the best experts on this subject based on the ideXlab platform.
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accelerated volume rendering using Homogeneous Region encoding
IEEE Visualization, 1997Co-Authors: Jason Freund, Kenneth R SloanAbstract:Previous accelerated volume rendering techniques have used auxiliary hierarchical datastructures to skip empty and Homogeneous Regions. Although some recent research has taken advantage of more efficient direct encoding techniques to skip empty Regions, no work has been done to directly encode Homogeneous but not empty Regions. 3D distance transforms previously used to encode empty space can be extended to preprocess Homogeneous Regions as well, and these Regions can be efficiently encoded and incorporated into volume ray-casting and back projection algorithms with a high degree of flexibility.
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IEEE Visualization - Accelerated volume rendering using Homogeneous Region encoding
Proceedings. Visualization '97 (Cat. No. 97CB36155), 1Co-Authors: Jason Freund, Kenneth R SloanAbstract:Previous accelerated volume rendering techniques have used auxiliary hierarchical datastructures to skip empty and Homogeneous Regions. Although some recent research has taken advantage of more efficient direct encoding techniques to skip empty Regions, no work has been done to directly encode Homogeneous but not empty Regions. 3D distance transforms previously used to encode empty space can be extended to preprocess Homogeneous Regions as well, and these Regions can be efficiently encoded and incorporated into volume ray-casting and back projection algorithms with a high degree of flexibility.