The Experts below are selected from a list of 279 Experts worldwide ranked by ideXlab platform

Yuanhao Zhai - One of the best experts on this subject based on the ideXlab platform.

  • ICIP - Rotation-invariant Local Radius index: A compact texture similarity feature for classification
    2014 IEEE International Conference on Image Processing (ICIP), 2014
    Co-Authors: Yuanhao Zhai, David L Neuhoff
    Abstract:

    This paper proposes a new rotation-invariant texture similarity feature, called Rotation-Invariant Local Radius Index (RI-LRI). Whereas the original LRI was designed for applications that are sensitive to rotation and aimed to penalize rotation monotonically, the new rotation-invariant LRI is well suited to texture classification. When combined with frequency domain contrast information and the well known Local Binary Patterns (LBP) feature, the proposed metric has comparable texture classification accuracy to state-of-the-art metrics, when tested on the Outex and CUReT databases. Moreover, it has an approximately ten times lower dimensional feature vector and requires substantially less computation than other state-of-the-art texture features, such as those based on LBP.

  • rotation invariant Local Radius index a compact texture similarity feature for classification
    International Conference on Image Processing, 2014
    Co-Authors: Yuanhao Zhai, David L Neuhoff
    Abstract:

    This paper proposes a new rotation-invariant texture similarity feature, called Rotation-Invariant Local Radius Index (RI-LRI). Whereas the original LRI was designed for applications that are sensitive to rotation and aimed to penalize rotation monotonically, the new rotation-invariant LRI is well suited to texture classification. When combined with frequency domain contrast information and the well known Local Binary Patterns (LBP) feature, the proposed metric has comparable texture classification accuracy to state-of-the-art metrics, when tested on the Outex and CUReT databases. Moreover, it has an approximately ten times lower dimensional feature vector and requires substantially less computation than other state-of-the-art texture features, such as those based on LBP.

  • Local Radius index a new texture similarity feature
    International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuanhao Zhai, David L Neuhoff, Thrasyvoulos N Pappas
    Abstract:

    We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.

  • ICASSP - Local Radius index - a new texture similarity feature
    2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuanhao Zhai, David L Neuhoff, Thrasyvoulos N Pappas
    Abstract:

    We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.

David L Neuhoff - One of the best experts on this subject based on the ideXlab platform.

  • ICIP - Rotation-invariant Local Radius index: A compact texture similarity feature for classification
    2014 IEEE International Conference on Image Processing (ICIP), 2014
    Co-Authors: Yuanhao Zhai, David L Neuhoff
    Abstract:

    This paper proposes a new rotation-invariant texture similarity feature, called Rotation-Invariant Local Radius Index (RI-LRI). Whereas the original LRI was designed for applications that are sensitive to rotation and aimed to penalize rotation monotonically, the new rotation-invariant LRI is well suited to texture classification. When combined with frequency domain contrast information and the well known Local Binary Patterns (LBP) feature, the proposed metric has comparable texture classification accuracy to state-of-the-art metrics, when tested on the Outex and CUReT databases. Moreover, it has an approximately ten times lower dimensional feature vector and requires substantially less computation than other state-of-the-art texture features, such as those based on LBP.

  • rotation invariant Local Radius index a compact texture similarity feature for classification
    International Conference on Image Processing, 2014
    Co-Authors: Yuanhao Zhai, David L Neuhoff
    Abstract:

    This paper proposes a new rotation-invariant texture similarity feature, called Rotation-Invariant Local Radius Index (RI-LRI). Whereas the original LRI was designed for applications that are sensitive to rotation and aimed to penalize rotation monotonically, the new rotation-invariant LRI is well suited to texture classification. When combined with frequency domain contrast information and the well known Local Binary Patterns (LBP) feature, the proposed metric has comparable texture classification accuracy to state-of-the-art metrics, when tested on the Outex and CUReT databases. Moreover, it has an approximately ten times lower dimensional feature vector and requires substantially less computation than other state-of-the-art texture features, such as those based on LBP.

  • Local Radius index a new texture similarity feature
    International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuanhao Zhai, David L Neuhoff, Thrasyvoulos N Pappas
    Abstract:

    We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.

  • ICASSP - Local Radius index - a new texture similarity feature
    2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuanhao Zhai, David L Neuhoff, Thrasyvoulos N Pappas
    Abstract:

    We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.

Thrasyvoulos N Pappas - One of the best experts on this subject based on the ideXlab platform.

  • Local Radius index a new texture similarity feature
    International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuanhao Zhai, David L Neuhoff, Thrasyvoulos N Pappas
    Abstract:

    We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.

  • ICASSP - Local Radius index - a new texture similarity feature
    2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013
    Co-Authors: Yuanhao Zhai, David L Neuhoff, Thrasyvoulos N Pappas
    Abstract:

    We develop a new type of statistical texture image feature, called a Local Radius Index (LRI), which can be used to quantify texture similarity based on human perception. Image similarity metrics based on LRI can be applied to image compression, identical texture retrieval and other related applications. LRI extracts texture features by using simple pixel value comparisons in space domain. Better performance can be achieved when LRI is combined with complementary texture features, e.g., Local Binary Patterns (LBP) and the proposed Subband Contrast Distribution. Compared with Structural Texture Similarity Metrics (STSIM), the LRI-based metrics achieve better retrieval performance with much less computation. Applied to the recently developed structurally lossless image coder, Matched Texture Coding, LRI enables similar performance while significantly accelerating the encoding.

Pinaki Prasad Guha Neogi - One of the best experts on this subject based on the ideXlab platform.

  • edge texture based characteristic attribute on Local Radius of gyration face for human face recognition
    2020
    Co-Authors: Pinaki Prasad Guha Neogi
    Abstract:

    It is a well-known fact that most of the edges in an image can be spotted on the facial segments and they have a place on the image’s high-frequency components. Besides edges, another crucial feature for face matching is the texture. Therefore, both edges and texture can have significant contribution in extracting facial attributes and thus in human face recognition. This paper puts forward a novel edge–texture characteristic attribute for human face recognition based on the concept of Radius of gyration face, which is invariant to changes in illumination, rotation and noise. The supremacy of the proposed approach in human face recognition is exhibited when its recognition accuracy is compared with other recent state-of-the-art techniques over challenging databases like CMU-PIE database, Extended Yale B database, AR database and CUFS database under varying conditions of illumination, noise, rotation and face sketch recognition.

  • Triangular coil pattern of Local Radius of gyration face for heterogeneous face recognition
    Applied Intelligence, 2019
    Co-Authors: Arindam Kar, Pinaki Prasad Guha Neogi
    Abstract:

    This paper puts forward a novel methodology for Heterogeneous Face Recognition (HFR), where we present a new-fangled image representation technique called the Local Radius of Gyration Face (LRGF), which has been theoretically proved to be invariant to changes in illumination, rotation and noise. Finally, a novel Local Triangular Coil Binary Pattern (LTCBP) is presented so as to apprehend the Local variations of the LRGF attributes, and the method has been entitled as the Triangular Coil Pattern of Local Radius of Gyration Face (TCPLRGF). The proposed algorithm has been tested on a number of challenging databases to study the precision of the TCPLRGF method under varying condition of illumination, rotation, noise and also the recognition accuracy of sketch-photo and NIR-VIS image. The Rank-1 recognition accuracy of 98.27% on CMU-PIE Database, 98.09% on Extended Yale B Database, 96.35% on AR Face Database, 100% on CUHK Face Sketch (CUFS) Database, 89.01% on LFW Database and 98.74% on the CASIA-HFB NIR-VIS Database exhibits the supremacy of the proposed strategy in Heterogeneous Face Recognition (HFR) under various conditions, compared to other recent state-of-the-art methods. For reckoning the similarity measure between images, a hybridized approach amalgamating the Jaccard Similarity method and the standardized L_1 norm approach has been taken into account.

ştefan Măruster - One of the best experts on this subject based on the ideXlab platform.

  • Local convergence of generalized Mann iteration
    Numerical Algorithms, 2017
    Co-Authors: ştefan Măruster, L. Maruster
    Abstract:

    The Local convergence of generalized Mann iteration is investigated in the setting of a real Hilbert space. As application, we obtain an algorithm for estimating the Local Radius of convergence for some known iterative methods. Numerical experiments are presented showing the performances of the proposed algorithm. For a particular case of the Ezquerro-Hernandez method (Ezquerro and Hernandez, J. Complex., 25 :343–361: 2009 ), the proposed procedure gives radii which are very close to or even identical with the best possible ones.

  • Local Convergence and Radius of Convergence for Modified Newton Method
    Annals of the West University of Timisoara: Mathematics and Computer Science, 2017
    Co-Authors: ştefan Măruster
    Abstract:

    AbstractWe investigate the Local convergence of modified Newton method, i.e., the classical Newton method in which the derivative is periodically re-evaluated. Based on the convergence properties of Picard iteration for demicontractive mappings, we give an algorithm to estimate the Local Radius of convergence for considered method. Numerical experiments show that the proposed algorithm gives estimated radii which are very close to or even equal with the best ones.

  • estimating the Local Radius of convergence for picard iteration
    Algorithms, 2017
    Co-Authors: ştefan Măruster
    Abstract:

    In this paper, we propose an algorithm to estimate the Radius of convergence for the Picard iteration in the setting of a real Hilbert space. Numerical experiments show that the proposed algorithm provides convergence balls close to or even identical to the best ones. As the algorithm does not require to evaluate the norm of derivatives, the computing effort is relatively low.