The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Hongbin Zha - One of the best experts on this subject based on the ideXlab platform.
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structure sensitive superpixels via Geodesic Distance
International Journal of Computer Vision, 2013Co-Authors: Peng Wang, Gang Zeng, Jingdong Wang, Rui Gan, Hongbin ZhaAbstract:Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the Geodesic Distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute Geodesic Distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach.
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structure sensitive superpixels via Geodesic Distance
International Conference on Computer Vision, 2011Co-Authors: Gang Zeng, Peng Wang, Jingdong Wang, Rui Gan, Hongbin ZhaAbstract:Over-segments (i.e. superpixels) have been commonly used as supporting regions for feature vectors and primitives to reduce computational complexity in various image analysis tasks. In this paper, we describe a structuresensitive over-segmentation technique by exploiting Lloyd's algorithm with a Geodesic Distance. It generates smaller superpixels to achieve lower under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute the Geodesic Distances amongst pixels, and in the segmentation procedure, the density of over-segments is automatically adjusted according to an energy functional that embeds color homogeneity, structure density and compactness constraints. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms prior arts while offering a comparable computational efficiency with fast methods, such as TurboPixels.
Peng Wang - One of the best experts on this subject based on the ideXlab platform.
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structure sensitive superpixels via Geodesic Distance
International Journal of Computer Vision, 2013Co-Authors: Peng Wang, Gang Zeng, Jingdong Wang, Rui Gan, Hongbin ZhaAbstract:Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the Geodesic Distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute Geodesic Distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach.
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structure sensitive superpixels via Geodesic Distance
International Conference on Computer Vision, 2011Co-Authors: Gang Zeng, Peng Wang, Jingdong Wang, Rui Gan, Hongbin ZhaAbstract:Over-segments (i.e. superpixels) have been commonly used as supporting regions for feature vectors and primitives to reduce computational complexity in various image analysis tasks. In this paper, we describe a structuresensitive over-segmentation technique by exploiting Lloyd's algorithm with a Geodesic Distance. It generates smaller superpixels to achieve lower under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute the Geodesic Distances amongst pixels, and in the segmentation procedure, the density of over-segments is automatically adjusted according to an energy functional that embeds color homogeneity, structure density and compactness constraints. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms prior arts while offering a comparable computational efficiency with fast methods, such as TurboPixels.
Alejandro C Frery - One of the best experts on this subject based on the ideXlab platform.
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a polsar scattering power factorization framework and novel roll invariant parameter based unsupervised classification scheme using a Geodesic Distance
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Debanshu Ratha, Avik Bhattacharya, Eric Pottier, Alejandro C FreryAbstract:We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain $N$ scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a Geodesic Distance between pairs of $4\times 4$ real Kennaugh matrices. In standard model-based decomposition schemes, the $3\times 3$ Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the Geodesic Distance ( ${GD}$ ) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter ( $\alpha _{GD}$ ), helicity parameter ( $\tau _{GD}$ ), and purity parameter ( $P_{GD}$ ). A $P_{GD}/\alpha _{GD}$ unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.
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a scattering power factorization framework using a Geodesic Distance in radar polarimetry
International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Debanshu Ratha, Avik Bhattacharya, Alejandro C FreryAbstract:This paper presents a novel scattering power factorization framework in radar polarimetry using a Geodesic Distance between the $4\times 4$ real Kennaugh matrices of the observed and the elementary targets (viz. dihedral, trihedral, dipole etc.). The framework provides both qualitative and quantitative estimates of the dominance of elementary scattering mechanisms in a pixel. It is also flexible in terms of the number of elementary models against which an observed backscattering may be compared. Under this framework, the observed scattering is evaluated in terms of scattering similarities which provide the dominance of scattering mechanisms. This is then further utilized for a convex splitting of unity to obtain the intermediate weights. This leads to the weights being the product of similarity and dissimilarity of the observed pixel with the elementary scattering models. Finally, these weights are modulated with the total power (Span) to obtain the non-negative scattering powers. The results are shown for full polarimetric single-look ALOS-2 L-band dataset and a multi-look RADARSAT-2 C-band dataset.
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unsupervised classification of polsar data using a scattering similarity measure derived from a Geodesic Distance
IEEE Geoscience and Remote Sensing Letters, 2018Co-Authors: Debanshu Ratha, Avik Bhattacharya, Alejandro C FreryAbstract:In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the Geodesic Distance on the unit sphere. This Geodesic Distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al . based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman–Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman–Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.
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the Geodesic Distance between mathcal g _i 0 models and its application to region discrimination
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Jose Naranjotorres, Juliana Gambini, Alejandro C FreryAbstract:The $\mathcal{G}_I^0$ distribution is able to characterize different regions in monopolarized SAR imagery. It is indexed by three parameters: the number of looks (which can be estimated in the whole image), a scale parameter and a texture parameter. This paper presents a new proposal for feature extraction and region discrimination in SAR imagery, using the Geodesic Distance as a measure of dissimilarity between $\mathcal{G}_I^0$ models. We derive Geodesic Distances between models that describe several practical situations, assuming the number of looks known, for same and different texture and for same and different scale. We then apply this new tool to the problems of (i)~identifying edges between regions with different texture, and (ii)~quantify the dissimilarity between pairs of samples in actual SAR data. We analyze the advantages of using the Geodesic Distance when compared to stochastic Distances.
Gang Zeng - One of the best experts on this subject based on the ideXlab platform.
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structure sensitive superpixels via Geodesic Distance
International Journal of Computer Vision, 2013Co-Authors: Peng Wang, Gang Zeng, Jingdong Wang, Rui Gan, Hongbin ZhaAbstract:Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the Geodesic Distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute Geodesic Distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach.
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structure sensitive superpixels via Geodesic Distance
International Conference on Computer Vision, 2011Co-Authors: Gang Zeng, Peng Wang, Jingdong Wang, Rui Gan, Hongbin ZhaAbstract:Over-segments (i.e. superpixels) have been commonly used as supporting regions for feature vectors and primitives to reduce computational complexity in various image analysis tasks. In this paper, we describe a structuresensitive over-segmentation technique by exploiting Lloyd's algorithm with a Geodesic Distance. It generates smaller superpixels to achieve lower under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute the Geodesic Distances amongst pixels, and in the segmentation procedure, the density of over-segments is automatically adjusted according to an energy functional that embeds color homogeneity, structure density and compactness constraints. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms prior arts while offering a comparable computational efficiency with fast methods, such as TurboPixels.
Debanshu Ratha - One of the best experts on this subject based on the ideXlab platform.
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a polsar scattering power factorization framework and novel roll invariant parameter based unsupervised classification scheme using a Geodesic Distance
IEEE Transactions on Geoscience and Remote Sensing, 2020Co-Authors: Debanshu Ratha, Avik Bhattacharya, Eric Pottier, Alejandro C FreryAbstract:We propose a generic scattering power factorization framework (SPFF) for polarimetric synthetic aperture radar (PolSAR) data to directly obtain $N$ scattering power components along with a residue power component for each pixel. Each scattering power component is factorized into similarity (or dissimilarity) using elementary targets and a generalized volume model. The similarity measure is derived using a Geodesic Distance between pairs of $4\times 4$ real Kennaugh matrices. In standard model-based decomposition schemes, the $3\times 3$ Hermitian-positive semi-definite covariance (or coherency) matrix is expressed as a weighted linear combination of scattering targets following a fixed hierarchical process. In contrast, under the proposed framework, a convex splitting of unity is performed to obtain the weights while preserving the dominance of the scattering components. The product of the total power (Span) with these weights provides the nonnegative scattering power components. Furthermore, the framework, along with the Geodesic Distance ( ${GD}$ ) is effectively used to obtain specific roll-invariant parameters such as scattering-type parameter ( $\alpha _{GD}$ ), helicity parameter ( $\tau _{GD}$ ), and purity parameter ( $P_{GD}$ ). A $P_{GD}/\alpha _{GD}$ unsupervised classification scheme is also proposed for PolSAR images. The SPFF, the roll invariant parameters, and the classification results are assessed using C-band RADARSAT-2 and L-band ALOS-2 images of San Francisco.
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a scattering power factorization framework using a Geodesic Distance in radar polarimetry
International Geoscience and Remote Sensing Symposium, 2018Co-Authors: Debanshu Ratha, Avik Bhattacharya, Alejandro C FreryAbstract:This paper presents a novel scattering power factorization framework in radar polarimetry using a Geodesic Distance between the $4\times 4$ real Kennaugh matrices of the observed and the elementary targets (viz. dihedral, trihedral, dipole etc.). The framework provides both qualitative and quantitative estimates of the dominance of elementary scattering mechanisms in a pixel. It is also flexible in terms of the number of elementary models against which an observed backscattering may be compared. Under this framework, the observed scattering is evaluated in terms of scattering similarities which provide the dominance of scattering mechanisms. This is then further utilized for a convex splitting of unity to obtain the intermediate weights. This leads to the weights being the product of similarity and dissimilarity of the observed pixel with the elementary scattering models. Finally, these weights are modulated with the total power (Span) to obtain the non-negative scattering powers. The results are shown for full polarimetric single-look ALOS-2 L-band dataset and a multi-look RADARSAT-2 C-band dataset.
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unsupervised classification of polsar data using a scattering similarity measure derived from a Geodesic Distance
IEEE Geoscience and Remote Sensing Letters, 2018Co-Authors: Debanshu Ratha, Avik Bhattacharya, Alejandro C FreryAbstract:In this letter, we propose a novel technique for obtaining scattering components from polarimetric synthetic aperture radar (PolSAR) data using the Geodesic Distance on the unit sphere. This Geodesic Distance is obtained between an elementary target and the observed Kennaugh matrix, and it is further utilized to compute a similarity measure between scattering mechanisms. The normalized similarity measure for each elementary target is then modulated with the total scattering power (Span). This measure is used to categorize pixels into three categories, i.e., odd-bounce, double-bounce, and volume, depending on which of the above scattering mechanisms dominate. Then the maximum likelihood classifier of Lee et al . based on the complex Wishart distribution is iteratively used for each category. Dominant scattering mechanisms are thus preserved in this classification scheme. We show results for L-band AIRSAR and ALOS-2 data sets acquired over San Francisco and Mumbai, respectively. The scattering mechanisms are better preserved using the proposed methodology than the unsupervised classification results using the Freeman–Durden scattering powers on an orientation angle corrected PolSAR image. Furthermore: 1) the scattering similarity is a completely nonnegative quantity unlike the negative powers that might occur in double-bounce and odd-bounce scattering component under Freeman–Durden decomposition and 2) the methodology can be extended to more canonical targets as well as for bistatic scattering.
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change detection in polarimetric sar images using a Geodesic Distance between scattering mechanisms
IEEE Geoscience and Remote Sensing Letters, 2017Co-Authors: Debanshu Ratha, Turgay Celik, Avik BhattacharyaAbstract:A novel technique to generate the difference image (DI) in change detection analysis for polarimetric SAR (PolSAR) data is proposed. Unlike the standard methods, viz., band difference or intensity/amplitude ratioing, the proposed technique utilizes the full vector nature of multitemporal PolSAR data. In this data, a pixel is characterized by a $4\times 4$ Kennaugh matrix. The Geodesic Distance (GD) on an unit sphere is utilized to define the Distance between the Kennaugh matrices of the three elementary targets (trihedral, dihedral, and 45° rotated dihedral about the radar line of sight) producing canonical scattering mechanisms and the observed Kennaugh matrix. Three absolute differences of the GD from respective elementary targets are obtained for time instants $t_{1}$ and $t_{2}$ . The DI is then the maximum among the three quantities. The proposed technique is applied to two scenes obtained from the L-band UAVSAR data characterizing changes due to urbanization. The principal component analysis with $k$ -means clustering proposed by Celik is used to obtain the binary change map. The proposed differencing method performs better than the single-channel intensity band ratio and the total power ratio for time instants $t_{1}$ and $t_{2}$ . The detection rate with the proposed technique is 6% and 20% better than the ratio methods for the two data sets, respectively, with the higher $\kappa $ value as a measure of performance evaluation.