The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Siva Chandra - One of the best experts on this subject based on the ideXlab platform.
-
An alternative curvature measure for topographic feature detection
Lecture Notes in Computer Science, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D Image Function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from Images. One such property, the curvature of the Image surface, can be used to detect features characterised by a sharp bend in the surface. Curvature based feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
-
ICVGIP - An alternative curvature measure for topographic feature detection
Computer Vision Graphics and Image Processing, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D Image Function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from Images. One such property, the curvature of the Image surface, can be used to detect features characterised by a sharp bend in the surface. Curvature based feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
Jayanthi Sivaswamy - One of the best experts on this subject based on the ideXlab platform.
-
An alternative curvature measure for topographic feature detection
Lecture Notes in Computer Science, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D Image Function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from Images. One such property, the curvature of the Image surface, can be used to detect features characterised by a sharp bend in the surface. Curvature based feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
-
ICVGIP - An alternative curvature measure for topographic feature detection
Computer Vision Graphics and Image Processing, 2006Co-Authors: Jayanthi Sivaswamy, Gopal Datt Joshi, Siva ChandraAbstract:The notion of topographic features like ridges, trenches, hills, etc. is formed by visualising the 2D Image Function as a surface in 3D space. Hence, properties of such a surface can be used to detect features from Images. One such property, the curvature of the Image surface, can be used to detect features characterised by a sharp bend in the surface. Curvature based feature detection requires an efficient technique to estimate/calculate the surface curvature. In this paper, we present an alternative measure for curvature and provide an analysis of the same to determine its scope. Feature detection algorithms using this measure are formulated and two applications are chosen to demonstrate their performance. The results show good potential of the proposed measure in terms of efficiency and scope.
A Kadyrov - One of the best experts on this subject based on the ideXlab platform.
-
affine invariant features from the trace transform
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004Co-Authors: Maria Petrou, A KadyrovAbstract:The trace transform is a generalization of the Radon transform that allows one to construct Image features that are invariant to a chosen group of Image transformations. In this paper, we propose a methodology and appropriate Functionals that can be computed from the Image Function and which can be used to calculate features invariant to the group of affine transforms. We demonstrate the usefulness of the constructed Image descriptors in retrieving Images from an Image database and compare it with relevant state-of-the-art object retrieval methods.
Yan Qiu Chen - One of the best experts on this subject based on the ideXlab platform.
-
Classifying Image texture with statistical landscape features
Pattern Analysis and Applications, 2005Co-Authors: Yan Qiu ChenAbstract:This paper proposes to use three-dimensional information derived from the graph of an Image Function for texture description. The graph of an Image Function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous Images, the entire Brodatz texture set, as well as the VisTex texture collection.
-
ICPR (1) - Statistical landscape features for texture classification
2004Co-Authors: Yan Qiu ChenAbstract:This paper proposes the use of information derived from the graph of a texture Image Function for texture description. The graph of an Image Function is a rumpled surface in the three-dimensional space that appears like a landscape. Four novel texture feature curves are used to characterize the texture. This method is named as statistical landscape features (SLF). SLF achieves a very high correct classification rate of 94.53% on the entire Brodatz set. Besides the very good performance, another remarkable advantage of the proposed method is that it has no parameter to tune.
Yongsheng Gao - One of the best experts on this subject based on the ideXlab platform.
-
structure integral transform versus radon transform a 2d mathematical tool for invariant shape recognition
IEEE Transactions on Image Processing, 2016Co-Authors: Bin Wang, Yongsheng GaoAbstract:In this paper, we present a novel mathematical tool, Structure Integral Transform (SIT), for invariant shape description and recognition. Different from the Radon Transform (RT), which integrates the shape Image Function over a 1D line in the Image plane, the proposed SIT builds upon two orthogonal integrals over a 2D $K$ -cross dissecting structure spanning across all rotation angles by which the shape regions are bisected in each integral. The proposed SIT brings the following advantages over the RT: 1) it has the extra Function of describing the interior structural relationship within the shape which provides a more powerful discriminative ability for shape recognition; 2) the shape regions are dissected by the $K$ -cross in a coarse to fine hierarchical order that can characterize the shape in a better spatial organization scanning from the center to the periphery; and 3) it is easier to build a completely invariant shape descriptor. The experimental results of applying SIT to shape recognition demonstrate its superior performance over the well-known Radon transform, and the well-known shape contexts and the polar harmonic transforms.