The Experts below are selected from a list of 1791 Experts worldwide ranked by ideXlab platform
Gui-song Xia - One of the best experts on this subject based on the ideXlab platform.
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SAR-based Terrain Classification using Weakly Supervised Hierarchical Markov Aspect Models
IEEE Transactions on Image Processing, 2012Co-Authors: Wen Yang, Dengxin Dai, Bill Triggs, Gui-song XiaAbstract:We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models--the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov Quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent-child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards-backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training.
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Sar-based terrain classification using weakly supervised hierarchical markov aspect models,” Image Processing
2012Co-Authors: Wen Yang, Dengxin Dai, Bill Triggs, Gui-song XiaAbstract:Abstract — We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models—the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov Quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent–child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards–backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training. Index Terms — Hierarchical Markov aspect model (HMAM), probabilistic latent semantic analysis (PLSA), scene labeling, synthetic aperture radar. I
Beatrice Pesquetpopescu - One of the best experts on this subject based on the ideXlab platform.
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initialization limitation and predictive coding of the depth and texture quadtree in 3d hevc
IEEE Transactions on Circuits and Systems for Video Technology, 2014Co-Authors: Elie Gabriel Mora, Joel Jung, Marco Cagnazzo, Beatrice PesquetpopescuAbstract:The 3D video extension of High Efficiency Video Coding (3D-HEVC) exploits texture-depth redundancies in 3D videos using intercomponent coding tools. It also inherits the same quadtree coding structure as HEVC for both components. The current software implementation of 3D-HEVC includes encoder shortcuts that speed up the quadtree construction process, but those are always accompanied by coding losses. Furthermore, since the texture and its associated depth represent the same scene, at the same time instant and view point, their Quadtrees are closely linked. In this paper, an intercomponent tool is proposed in which this link is exploited to save both runtime and bits through a joint coding of the Quadtrees. If depth is coded before the texture, the texture quadtree is initialized from the coded depth quadtree. Otherwise, the depth quadtree is limited to the coded texture quadtree. A 31% encoder runtime saving, a -0.3% gain for coded and synthesized views and a -1.8% gain for coded views are reported for the second method.
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initialization limitation and predictive coding of the depth and texture quadtree in 3d hevc
IEEE Transactions on Circuits and Systems for Video Technology, 2014Co-Authors: Elie Gabriel Mora, Joel Jung, Marco Cagnazzo, Beatrice PesquetpopescuAbstract:The 3D video extension of High Efficiency Video Coding (3D-HEVC) exploits texture-depth redundancies in 3D videos using intercomponent coding tools. It also inherits the same quadtree coding structure as HEVC for both components. The current software implementation of 3D-HEVC includes encoder shortcuts that speed up the quadtree construction process, but those are always accompanied by coding losses. Furthermore, since the texture and its associated depth represent the same scene, at the same time instant and view point, their Quadtrees are closely linked. In this paper, an intercomponent tool is proposed in which this link is exploited to save both runtime and bits through a joint coding of the Quadtrees. If depth is coded before the texture, the texture quadtree is initialized from the coded depth quadtree. Otherwise, the depth quadtree is limited to the coded texture quadtree. A 31% encoder runtime saving, a -0.3% gain for coded and synthesized views and a -1.8% gain for coded views are reported for the second method.
Martin Vetterli - One of the best experts on this subject based on the ideXlab platform.
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best wavelet packet bases in a rate distortion sense
IEEE Transactions on Image Processing, 1993Co-Authors: Kannan Ramchandran, Martin VetterliAbstract:A fast rate-distortion (R-D) optimal scheme for coding adaptive trees whose individual nodes spawn descendents forming a disjoint and complete basis cover for the space spanned by their parent nodes is presented. The scheme guarantees operation on the convex hull of the operational R-D curve and uses a fast dynamic programing pruning algorithm to markedly reduce computational complexity. Applications for this coding technique include R. Coefman et al.'s (Yale Univ., 1990) generalized multiresolution wavelet packet decomposition, iterative subband coders, and quadtree structures. Applications to image processing involving wavelet packets as well as discrete cosine transform (DCT) Quadtrees are presented. >
Meir Feder - One of the best experts on this subject based on the ideXlab platform.
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image compression via improved quadtree decomposition algorithms
IEEE Transactions on Image Processing, 1994Co-Authors: Eliahu Shusterman, Meir FederAbstract:Quadtree decomposition is a simple technique used to obtain an image representation at different resolution levels. This representation can be useful for a variety of image processing and image compression algorithms. This paper presents a simple way to get better compression performances (in MSE sense) via quadtree decomposition, by using near to optimal choice of the threshold for quadtree decomposition; and bit allocation procedure based on the equations derived from rate-distortion theory. The rate-distortion performance of the improved algorithm is calculated for some Gaussian field, and it is examined vie simulation over benchmark gray-level images. In both these cases, significant improvement in the compression performances is shown. >
Wen Yang - One of the best experts on this subject based on the ideXlab platform.
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SAR-based Terrain Classification using Weakly Supervised Hierarchical Markov Aspect Models
IEEE Transactions on Image Processing, 2012Co-Authors: Wen Yang, Dengxin Dai, Bill Triggs, Gui-song XiaAbstract:We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models--the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov Quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent-child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards-backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training.
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Sar-based terrain classification using weakly supervised hierarchical markov aspect models,” Image Processing
2012Co-Authors: Wen Yang, Dengxin Dai, Bill Triggs, Gui-song XiaAbstract:Abstract — We introduce the hierarchical Markov aspect model (HMAM), a computationally efficient graphical model for densely labeling large remote sensing images with their underlying terrain classes. HMAM resolves local ambiguities efficiently by combining the benefits of quadtree representations and aspect models—the former incorporate multiscale visual features and hierarchical smoothing to provide improved local label consistency, while the latter sharpen the labelings by focusing them on the classes that are most relevant for the broader local image context. The full HMAM model takes a grid of local hierarchical Markov Quadtrees over image patches and augments it by incorporating a probabilistic latent semantic analysis aspect model over a larger local image tile at each level of the quadtree forest. Bag-of-word visual features are extracted for each level and patch, and given these, the parent–child transition probabilities from the quadtree and the label probabilities from the tile-level aspect models, an efficient forwards–backwards inference pass allows local posteriors for the class labels to be obtained for each patch. Variational expectation-maximization is then used to train the complete model from either pixel-level or tile-keyword-level labelings. Experiments on a complete TerraSAR-X synthetic aperture radar terrain map with pixel-level ground truth show that HMAM is both accurate and efficient, providing significantly better results than comparable single-scale aspect models with only a modest increase in training and test complexity. Keyword-level training greatly reduces the cost of providing training data with little loss of accuracy relative to pixel-level training. Index Terms — Hierarchical Markov aspect model (HMAM), probabilistic latent semantic analysis (PLSA), scene labeling, synthetic aperture radar. I
