O. Soldea - Academia.edu (original) (raw)

Papers by O. Soldea

Lecture Notes in Computer Science, 2010

Advances in the medical imaging technology has lead to an exponential growth in the number of dig... more Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Challenge, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation challenge. We use a direct and two ensemble classification schemes that employ local binary patterns as image descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed ensemble schemes divide the classification task into sub-problems. The first ensemble scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that ensemble annotation by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme.

Geometric Modeling and Processing, 2004. Proceedings

This paper presents a scheme to globally compute, bound, and analyze the Gaussian and mean curvat... more This paper presents a scheme to globally compute, bound, and analyze the Gaussian and mean curvatures of an entire volumetric data set, using a trivariate B-spline volumetric representation. The proposed scheme is not only precise and insensitive to aliasing, but also provides a method to globally segment the images into volumetric regions that contain convex or concave´ellipticµ iso-surfaces, planar or cylindrical´parabolicµ iso-surfaces, and volumetric regions with saddle-like´hyperbolicµ iso-surfaces, regardless of the value of the iso-surface level. This scheme, which derives a new differential scalar field for a given scalar field, could easily be adapted to other differential properties.

2011 18th IEEE International Conference on Image Processing, 2011

In this paper we propose a new method for quantification of the rate of brain tissue deformation ... more In this paper we propose a new method for quantification of the rate of brain tissue deformation of patients with neurodegenerative diseases. Our method allows for generating intermediary (virtual) MR brain volumes from real scans. In addition, we introduce two new descriptor spaces: (i) joint brain tissue deformation displacement vector magnitude and Jacobian and (ii) joint polar angles of displacement vector. In order to conduct our analysis, we built a visualization environment that illustrates images and flows, and it quantifies parameters. Through the calculation of the parameters, of the intermediary MR volumes, we create N-fold information simulating the deformation process. While our method is suitable for every neurodegenerative disease, we empirically tested our scheme on patients with diagnosed Alzheimer's disease, mild cognitive impairment and normal controls from the ADNI database. We focus on regions containing either ventricles or hippocampus. A distinction between different subjects could clearly be made.

Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range ... more Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, industrial and biomedical engineering. This work considers different computational schemes for local estimation of intrinsic curvature geometric properties. Five different algorithms and their modifications were tested on triangular meshes that represent tesselations of synthetic geometric models. The results were compared with the analytically computed values of the Gaussian and mean curvatures of the non uniform rational B-spline (NURBs) surfaces, these meshes originated from. This work manifests the best algorithms suited for total (Gaussian) and mean curvature estimation, and shows that indeed different alogrithms should be employed to compute the Gaussian and mean curvatures.

Lecture Notes in Computer Science, 2011

ABSTRACT This paper presents a new supervised learning based method for brain structure segmentat... more ABSTRACT This paper presents a new supervised learning based method for brain structure segmentation. We learn moment-based signatures of structures of interest and formulate the segmentation as a maximum a-posteriori estimation problem employing nonparametric multivariate kernel densities. For this problem, we propose a gradient flow solution. We have compared our method with state-of-the-art methods such as FSL-FIRST and Free-Surfer using volumetric 3T from IBSR. In addition, we have evaluated our algorithm on 7T MR data. We report comparative results of accuracy and significantly improved time-efficiency.

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009

We present a new active contour-based, statistical method for simultaneous volumetric segmentatio... more We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images and present a quantitative performance analysis. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy.

Lecture Notes in Computer Science, 2010

Advances in the medical imaging technology has lead to an exponential growth in the number of dig... more Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for reallife implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space.

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006

We propose a novel scheme for fusion between two types of modalities to support function-based cl... more We propose a novel scheme for fusion between two types of modalities to support function-based classification. While the first modality targets functional classification from sounds registered at impact, the second one aims classification of objects in 3D images. Using audio one can answer functional questions such as what is the material the analyzed objects are built of, if the objects are full or hollow, if they are heavy, and if they are rigidly linked to their supports. Audio based signatures are used to label parts of the object under analysis. Different parts of any object can be partitioned in generic multi-level hierarchical descriptions of functional components. Functionality, in the visual modality reasoning scheme, is derived from a large set of geometric attributes and relationships between object parts. These geometric properties represent labeling signatures to the primitive and functional parts of the analyzed classes. The fusion between both of the modalities relies on a shared cooperation among audio and visual signatures of the functional and primitive parts. The scheme does not require a-priori knowledge about any class. We tested the proposed scheme on a database of about one thousand different 3D objects. The results show high accuracy in classification.

2010 20th International Conference on Pattern Recognition, 2010

Segmentation of brain structures from MR images is crucial in understanding the disease progress,... more Segmentation of brain structures from MR images is crucial in understanding the disease progress, diagnosis, and treatment monitoring. Atlases, showing the expected locations of the structures, are commonly used to start and guide the segmentation process. In many cases, the quality of the atlas may have a significant effect in the final result. In the literature, commonly used atlases may be obtained from one subject's data, only from the healthy, or depict only certain structures that limit their accuracy. Anatomical variations, pathologies, imaging artifacts all could aggravate the problems related to application of atlases. In this paper, we propose to use multiple atlases that are sufficiently different from each other as much as possible to handle such problems. To this effect, we have built a library of atlases and computed their similarity values to each other. Our study showed that the existing atlases have varying levels of similarity for different structures.

SPIE Proceedings, 2009

We present a new method for multi-object segmentation in a maximum a posteriori estimation framew... more We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multivariate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

Jewels are Forever, 1999

ABSTRACT Some algorithms for performing arithmetical operations, on line, and fit for parallel an... more ABSTRACT Some algorithms for performing arithmetical operations, on line, and fit for parallel and concurrent computation are described and investigated. The algorithms are based on the continued fractions representation of numbers and the continued fraction representation is generalized so as to allow rational quotients (instead of integer quotients). This generalization is intended to minimize the delay in the output stream of quotients provided by the units when a continuous stream of quotients is received by them at input.

2010 20th International Conference on Pattern Recognition, 2010

This paper develops a recursive method for computing moments of 2D objects described by elliptic ... more This paper develops a recursive method for computing moments of 2D objects described by elliptic Fourier descriptors (EFD). Green's theorem is utilized to transform 2D surface integrals into 1D line integrals and EFD description is employed to derive recursions for moments computations. Experiments are performed to quantify the accuracy of our proposed method. Comparison with Bernstein-Bézier representations is also provided.

Pattern Analysis and Applications, 2010

This paper presents a new method for recognizing 3D objects based on the comparison of invariants... more This paper presents a new method for recognizing 3D objects based on the comparison of invariants of their 2D projection curves. We show that Euclidean equivalent 3D surfaces imply affine equivalent 2D projection curves that are obtained from the projection of cross section curves of the surfaces onto the coordinate planes. Planes used to extract cross section curves are chosen to be orthogonal to the principal axes of the defining surfaces. Projection curves are represented using implicit polynomial (algebraic) equations. Affine algebraic and geometric invariants of projection curves are constructed and compared under a variety of distance measures. Results are verified by several experiments with objects from different classes and objects within the same class.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

This paper presents a method to globally segment volumetric images into regions that contain conv... more This paper presents a method to globally segment volumetric images into regions that contain convex or concave (elliptic) iso-surfaces, planar or cylindrical (parabolic) iso-surfaces, and volumetric regions with saddlelike (hyperbolic) iso-surfaces, regardless of the value of the iso-surface level. The proposed scheme relies on a novel approach to globally compute, bound, and analyze the Gaussian and mean curvatures of an entire volumetric data set, using a trivariate B-spline volumetric representation. This scheme derives a new differential scalar field for a given volumetric scalar field, which could easily be adapted to other differential properties. Moreover, this scheme can set the basis for more precise and accurate segmentation of data sets targeting the identification of primitive parts. Since the proposed scheme employs piecewise continuous functions, it is precise and insensitive to aliasing.

Computer Vision and Image Understanding, 2007

Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range ... more Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, and industrial and biomedical engineering. This work considers different computational schemes for local estimation of intrinsic curvature geometric properties. Four different algorithms and their modifications were tested on triangular meshes that represent tessellations of synthetic geometric models. The results were compared with the analytically computed values of the Gaussian and mean curvatures of the non-uniform rational B-spline (NURBS) surfaces from which these meshes originated. The algorithms were also tested on range images of geometric objects. The results were compared with the analytic values of the Gaussian and mean curvatures of the scanned geometric objects. This work manifests the best algorithms suited for Gaussian and mean curvature estimation, and shows that different algorithms should be employed to compute the Gaussian and mean curvatures.

Computer Vision and Image Understanding, 2007

In this work we propose a probabilistic model for generic object classification from raw range im... more In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about 150 real raw range images of object instances from 10 classes.

Computer-Aided Design, 2002

Two schemes for computing moments of free-form objects are developed and analyzed. In the ®rst sc... more Two schemes for computing moments of free-form objects are developed and analyzed. In the ®rst scheme, we assume that the boundary of the analyzed object is represented using parametric surfaces. In the second scheme, we represent the boundary of the object as a constant set of a trivariate function. These schemes rely on a pre-computation step which allows fast re-evaluation of the moments when the analyzed object is modi®ed. Both schemes take advantage of a representation that is based on the B-spline blending functions.

Automatic reconstruction of archeological fragmented objects is an invaluable tool for restoratio... more Automatic reconstruction of archeological fragmented objects is an invaluable tool for restoration purposes and personnel. In this paper, we assume that broken pieces resemble similar characteristics on their common boundaries, when they are correctly combined, of course. Bearing in mind that common boundaries preserve texture and geometry, we analyze features that allow the transport of characteristics over the common boundaries. We present a quantitative and qualitative comparison over a large set of features and over a large set of synthetic and real archeological fragmented objects. To the best of our knowledge, this is the first work that provides evidences for the most utile features.

Segmentation of brain structures is an important component in the field of medical imaging becaus... more Segmentation of brain structures is an important component in the field of medical imaging because it provides support for diagnosis and treatment as well as therapy evaluation guidance. Modern technology allows acquisition of MR images with very high resolution up to 0.3 mm of spacing between voxels. In this context, we propose a fully automatic segmentation method focused on substructures of the Hippocampal Formation. For this purpose, we present the development and implementation of three relevant contributions. First, we introduce a fast registration method based on moments. Second, we present a multi-level initialization scheme for obtaining initial contours required for segmentation; this procedure is fully automatic and it requires a training set of images with manual annotations of the substructures of interest. Third, we introduce a new segmentation algorithm, which is based on active contours driven by moments prior. We minimize an energy cost function in order to get optimal segmentation employing signatures based on moments. We compared our results to state-of-the-art tools and show significantly improvement in time performance. In addition, we tested our method with patients with Alzheimer Disease.

Lecture Notes in Computer Science, 2010

Advances in the medical imaging technology has lead to an exponential growth in the number of dig... more Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Challenge, the proposed solutions are still far from being sufficiently accurate for real-life implementations. In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation challenge. We use a direct and two ensemble classification schemes that employ local binary patterns as image descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed ensemble schemes divide the classification task into sub-problems. The first ensemble scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that ensemble annotation by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme.

Geometric Modeling and Processing, 2004. Proceedings

This paper presents a scheme to globally compute, bound, and analyze the Gaussian and mean curvat... more This paper presents a scheme to globally compute, bound, and analyze the Gaussian and mean curvatures of an entire volumetric data set, using a trivariate B-spline volumetric representation. The proposed scheme is not only precise and insensitive to aliasing, but also provides a method to globally segment the images into volumetric regions that contain convex or concave´ellipticµ iso-surfaces, planar or cylindrical´parabolicµ iso-surfaces, and volumetric regions with saddle-like´hyperbolicµ iso-surfaces, regardless of the value of the iso-surface level. This scheme, which derives a new differential scalar field for a given scalar field, could easily be adapted to other differential properties.

2011 18th IEEE International Conference on Image Processing, 2011

In this paper we propose a new method for quantification of the rate of brain tissue deformation ... more In this paper we propose a new method for quantification of the rate of brain tissue deformation of patients with neurodegenerative diseases. Our method allows for generating intermediary (virtual) MR brain volumes from real scans. In addition, we introduce two new descriptor spaces: (i) joint brain tissue deformation displacement vector magnitude and Jacobian and (ii) joint polar angles of displacement vector. In order to conduct our analysis, we built a visualization environment that illustrates images and flows, and it quantifies parameters. Through the calculation of the parameters, of the intermediary MR volumes, we create N-fold information simulating the deformation process. While our method is suitable for every neurodegenerative disease, we empirically tested our scheme on patients with diagnosed Alzheimer's disease, mild cognitive impairment and normal controls from the ADNI database. We focus on regions containing either ventricles or hippocampus. A distinction between different subjects could clearly be made.

Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range ... more Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, industrial and biomedical engineering. This work considers different computational schemes for local estimation of intrinsic curvature geometric properties. Five different algorithms and their modifications were tested on triangular meshes that represent tesselations of synthetic geometric models. The results were compared with the analytically computed values of the Gaussian and mean curvatures of the non uniform rational B-spline (NURBs) surfaces, these meshes originated from. This work manifests the best algorithms suited for total (Gaussian) and mean curvature estimation, and shows that indeed different alogrithms should be employed to compute the Gaussian and mean curvatures.

Lecture Notes in Computer Science, 2011

ABSTRACT This paper presents a new supervised learning based method for brain structure segmentat... more ABSTRACT This paper presents a new supervised learning based method for brain structure segmentation. We learn moment-based signatures of structures of interest and formulate the segmentation as a maximum a-posteriori estimation problem employing nonparametric multivariate kernel densities. For this problem, we propose a gradient flow solution. We have compared our method with state-of-the-art methods such as FSL-FIRST and Free-Surfer using volumetric 3T from IBSR. In addition, we have evaluated our algorithm on 7T MR data. We report comparative results of accuracy and significantly improved time-efficiency.

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009

We present a new active contour-based, statistical method for simultaneous volumetric segmentatio... more We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images and present a quantitative performance analysis. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy.

Lecture Notes in Computer Science, 2010

Advances in the medical imaging technology has lead to an exponential growth in the number of dig... more Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that need to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, the proposed solutions are still far from being sufficiently accurate for reallife implementations. In a previous work, performance of different feature types were investigated in a SVM-based learning framework for classification of X-Ray images into classes corresponding to body parts and local binary patterns were observed to outperform others. In this paper, we extend that work by exploring the effect of attribute selection on the classification performance. Our experiments show that principal component analysis based attribute selection manifests prediction values that are comparable to the baseline (all-features case) with considerably smaller subsets of original features, inducing lower processing times and reduced storage space.

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006

We propose a novel scheme for fusion between two types of modalities to support function-based cl... more We propose a novel scheme for fusion between two types of modalities to support function-based classification. While the first modality targets functional classification from sounds registered at impact, the second one aims classification of objects in 3D images. Using audio one can answer functional questions such as what is the material the analyzed objects are built of, if the objects are full or hollow, if they are heavy, and if they are rigidly linked to their supports. Audio based signatures are used to label parts of the object under analysis. Different parts of any object can be partitioned in generic multi-level hierarchical descriptions of functional components. Functionality, in the visual modality reasoning scheme, is derived from a large set of geometric attributes and relationships between object parts. These geometric properties represent labeling signatures to the primitive and functional parts of the analyzed classes. The fusion between both of the modalities relies on a shared cooperation among audio and visual signatures of the functional and primitive parts. The scheme does not require a-priori knowledge about any class. We tested the proposed scheme on a database of about one thousand different 3D objects. The results show high accuracy in classification.

2010 20th International Conference on Pattern Recognition, 2010

Segmentation of brain structures from MR images is crucial in understanding the disease progress,... more Segmentation of brain structures from MR images is crucial in understanding the disease progress, diagnosis, and treatment monitoring. Atlases, showing the expected locations of the structures, are commonly used to start and guide the segmentation process. In many cases, the quality of the atlas may have a significant effect in the final result. In the literature, commonly used atlases may be obtained from one subject's data, only from the healthy, or depict only certain structures that limit their accuracy. Anatomical variations, pathologies, imaging artifacts all could aggravate the problems related to application of atlases. In this paper, we propose to use multiple atlases that are sufficiently different from each other as much as possible to handle such problems. To this effect, we have built a library of atlases and computed their similarity values to each other. Our study showed that the existing atlases have varying levels of similarity for different structures.

SPIE Proceedings, 2009

We present a new method for multi-object segmentation in a maximum a posteriori estimation framew... more We present a new method for multi-object segmentation in a maximum a posteriori estimation framework. Our method is motivated by the observation that neighboring or coupling objects in images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our approach employs coupled shape and inter-shape pose priors that are computed using training images in a nonparametric multivariate kernel density estimation framework. The coupled shape prior is obtained by estimating the joint shape distribution of multiple objects and the inter-shape pose priors are modeled via standard moments. Based on such statistical models, we formulate an optimization problem for segmentation, which we solve by an algorithm based on active contours. Our technique provides significant improvements in the segmentation of weakly contrasted objects in a number of applications. In particular for medical image analysis, we use our method to extract brain Basal Ganglia structures, which are members of a complex multi-object system posing a challenging segmentation problem. We also apply our technique to the problem of handwritten character segmentation. Finally, we use our method to segment cars in urban scenes.

Jewels are Forever, 1999

ABSTRACT Some algorithms for performing arithmetical operations, on line, and fit for parallel an... more ABSTRACT Some algorithms for performing arithmetical operations, on line, and fit for parallel and concurrent computation are described and investigated. The algorithms are based on the continued fractions representation of numbers and the continued fraction representation is generalized so as to allow rational quotients (instead of integer quotients). This generalization is intended to minimize the delay in the output stream of quotients provided by the units when a continuous stream of quotients is received by them at input.

2010 20th International Conference on Pattern Recognition, 2010

This paper develops a recursive method for computing moments of 2D objects described by elliptic ... more This paper develops a recursive method for computing moments of 2D objects described by elliptic Fourier descriptors (EFD). Green's theorem is utilized to transform 2D surface integrals into 1D line integrals and EFD description is employed to derive recursions for moments computations. Experiments are performed to quantify the accuracy of our proposed method. Comparison with Bernstein-Bézier representations is also provided.

Pattern Analysis and Applications, 2010

This paper presents a new method for recognizing 3D objects based on the comparison of invariants... more This paper presents a new method for recognizing 3D objects based on the comparison of invariants of their 2D projection curves. We show that Euclidean equivalent 3D surfaces imply affine equivalent 2D projection curves that are obtained from the projection of cross section curves of the surfaces onto the coordinate planes. Planes used to extract cross section curves are chosen to be orthogonal to the principal axes of the defining surfaces. Projection curves are represented using implicit polynomial (algebraic) equations. Affine algebraic and geometric invariants of projection curves are constructed and compared under a variety of distance measures. Results are verified by several experiments with objects from different classes and objects within the same class.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006

This paper presents a method to globally segment volumetric images into regions that contain conv... more This paper presents a method to globally segment volumetric images into regions that contain convex or concave (elliptic) iso-surfaces, planar or cylindrical (parabolic) iso-surfaces, and volumetric regions with saddlelike (hyperbolic) iso-surfaces, regardless of the value of the iso-surface level. The proposed scheme relies on a novel approach to globally compute, bound, and analyze the Gaussian and mean curvatures of an entire volumetric data set, using a trivariate B-spline volumetric representation. This scheme derives a new differential scalar field for a given volumetric scalar field, which could easily be adapted to other differential properties. Moreover, this scheme can set the basis for more precise and accurate segmentation of data sets targeting the identification of primitive parts. Since the proposed scheme employs piecewise continuous functions, it is precise and insensitive to aliasing.

Computer Vision and Image Understanding, 2007

Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range ... more Estimating intrinsic geometric properties of a surface from a polygonal mesh obtained from range data is an important stage of numerous algorithms in computer and robot vision, computer graphics, geometric modeling, and industrial and biomedical engineering. This work considers different computational schemes for local estimation of intrinsic curvature geometric properties. Four different algorithms and their modifications were tested on triangular meshes that represent tessellations of synthetic geometric models. The results were compared with the analytically computed values of the Gaussian and mean curvatures of the non-uniform rational B-spline (NURBS) surfaces from which these meshes originated. The algorithms were also tested on range images of geometric objects. The results were compared with the analytic values of the Gaussian and mean curvatures of the scanned geometric objects. This work manifests the best algorithms suited for Gaussian and mean curvature estimation, and shows that different algorithms should be employed to compute the Gaussian and mean curvatures.

Computer Vision and Image Understanding, 2007

In this work we propose a probabilistic model for generic object classification from raw range im... more In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about 150 real raw range images of object instances from 10 classes.

Computer-Aided Design, 2002

Two schemes for computing moments of free-form objects are developed and analyzed. In the ®rst sc... more Two schemes for computing moments of free-form objects are developed and analyzed. In the ®rst scheme, we assume that the boundary of the analyzed object is represented using parametric surfaces. In the second scheme, we represent the boundary of the object as a constant set of a trivariate function. These schemes rely on a pre-computation step which allows fast re-evaluation of the moments when the analyzed object is modi®ed. Both schemes take advantage of a representation that is based on the B-spline blending functions.

Automatic reconstruction of archeological fragmented objects is an invaluable tool for restoratio... more Automatic reconstruction of archeological fragmented objects is an invaluable tool for restoration purposes and personnel. In this paper, we assume that broken pieces resemble similar characteristics on their common boundaries, when they are correctly combined, of course. Bearing in mind that common boundaries preserve texture and geometry, we analyze features that allow the transport of characteristics over the common boundaries. We present a quantitative and qualitative comparison over a large set of features and over a large set of synthetic and real archeological fragmented objects. To the best of our knowledge, this is the first work that provides evidences for the most utile features.

Segmentation of brain structures is an important component in the field of medical imaging becaus... more Segmentation of brain structures is an important component in the field of medical imaging because it provides support for diagnosis and treatment as well as therapy evaluation guidance. Modern technology allows acquisition of MR images with very high resolution up to 0.3 mm of spacing between voxels. In this context, we propose a fully automatic segmentation method focused on substructures of the Hippocampal Formation. For this purpose, we present the development and implementation of three relevant contributions. First, we introduce a fast registration method based on moments. Second, we present a multi-level initialization scheme for obtaining initial contours required for segmentation; this procedure is fully automatic and it requires a training set of images with manual annotations of the substructures of interest. Third, we introduce a new segmentation algorithm, which is based on active contours driven by moments prior. We minimize an energy cost function in order to get optimal segmentation employing signatures based on moments. We compared our results to state-of-the-art tools and show significantly improvement in time performance. In addition, we tested our method with patients with Alzheimer Disease.