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Ioannis A Kakadiaris - One of the best experts on this subject based on the ideXlab platform.
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3d facial landmark detection under large yaw and expression variations
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Panagiotis Perakis, Georgios Passalis, Theoharis Theoharis, Ioannis A KakadiarisAbstract:A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of Principal Curvature values of a 3D object's surface, and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.
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3d facial landmark detection face registration a 3d facial landmark model 3d local shape descriptors approach
2010Co-Authors: Panagiotis Perakis, Georgios Passalis, Theoharis Theoharis, Ioannis A KakadiarisAbstract:In this Technical Report a novel method for 3D landmark detec- tion and pose estimation suitable for both frontal and side 3D facial scans is presented. It utilizes 3D information by using 3D local shape descriptors to extract candidate interest points that are subsequently identied and labeled as anatomical landmarks. The shape descriptors include the shape index, a continuous map of Principal Curvature values of 3D objects, the extrusion map, a measure of the extruded areas of a 3D object and the spin images, local descriptors of the object's 3D point distribution. However, feature detection methods which use general purpose shape de- scriptors cannot identify and label the detected candidate land- marks. Therefore, the topological properties of the human face need to be taken into consideration. To this end, we use a Facial Landmark Model (FLM) of facial anatomical landmarks. Candi- date landmarks, irrespectively of the way they are generated, can be identied and labeled by matching them with the correspond- ing FLM. The proposed method is evaluated using an extensive 3D facial database, and achieves high accuracy even in challenging scenarios.
Cecil, Thomas E. - One of the best experts on this subject based on the ideXlab platform.
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Compact Dupin Hypersurfaces
2021Co-Authors: Cecil, Thomas E.Abstract:A hypersurface $M$ in ${\bf R}^n$ is said to be Dupin if along each Curvature surface, the corresponding Principal Curvature is constant. A Dupin hypersurface is said to be proper Dupin if the number of distinct Principal Curvatures is constant on $M$, i.e., each continuous Principal Curvature function has constant multiplicity on $M$. These conditions are preserved by stereographic projection, so this theory is essentially the same for hypersurfaces in ${\bf R}^n$ or $S^n$. The theory of compact proper Dupin hypersurfaces in $S^n$ is closely related to the theory of isoparametric hypersurfaces in $S^n$, and many important results in this field concern relations between these two classes of hypersurfaces. In 1985, Cecil and Ryan conjectured on p. 184 of the book, "Tight and Taut Immersions of Manifolds," that every compact, connected proper Dupin hypersurface $M \subset S^n$ is equivalent to an isoparametric hypersurface in $S^n$ by a Lie sphere transformation. This paper gives a survey of progress on this conjecture and related developments.Comment: 29 page
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Compact Dupin Hypersurfaces
CrossWorks, 2021Co-Authors: Cecil, Thomas E.Abstract:A hypersurface M in Rn is said to be Dupin if along each Curvature surface, the corresponding Principal Curvature is constant. A Dupin hypersurface is said to be proper Dupin if the number of distinct Principal Curvatures is constant on M, i.e., each continuous Principal Curvature function has constant multiplicity on M. These conditions are preserved by stereographic projection, so this theory is essentially the same for hypersurfaces in Rn or Sn . The theory of compact proper Dupin hypersurfaces in Sn is closely related to the theory of isoparametric hypersurfaces in Sn, and many important results in this field concern relations between these two classes of hypersurfaces. In 1985, Cecil and Ryan [18, p. 184] conjectured that every compact, connected proper Dupin hypersurface M ⊂ Sn is equivalent to an isoparametric hypersurface in Sn by a Lie sphere transformation. This paper gives a survey of progress on this conjecture and related developments
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Using Lie Sphere Geometry to Study Dupin Hypersurfaces in ${\bf R}^n$
2021Co-Authors: Cecil, Thomas E.Abstract:A hypersurface $M$ in ${\bf R}^n$ or $S^n$ is said to be Dupin if along each Curvature surface, the corresponding Principal Curvature is constant. A Dupin hypersurface is said to be proper Dupin if each Principal Curvature has constant multiplicity on $M$, i.e., the number of distinct Principal Curvatures is constant on $M$. The notions of Dupin and proper Dupin hypersurfaces in ${\bf R}^n$ or $S^n$ can be generalized to the setting of Lie sphere geometry, and these properties are easily seen to be invariant under Lie sphere transformations. This makes Lie sphere geometry an effective setting for the study of Dupin hypersurfaces, and many classifications of proper Dupin hypersurfaces have been obtained up to Lie sphere transformations. In these notes, we give a detailed introduction to this method for studying Dupin hypersurfaces in ${\bf R}^n$ or $S^n$, including proofs of several fundamental results.Comment: 59 pages. arXiv admin note: text overlap with arXiv:1607.08153 by other author
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Compact Dupin Hypersurfaces
2021Co-Authors: Cecil, Thomas E.Abstract:A hypersurface $M$ in ${\bf R}^n$ is said to be Dupin if along each Curvature surface, the corresponding Principal Curvature is constant. A Dupin hypersurface is said to be proper Dupin if the number of distinct Principal Curvatures is constant on $M$, i.e., each continuous Principal Curvature function has constant multiplicity on $M$. These conditions are preserved by stereographic projection, so this theory is essentially the same for hypersurfaces in ${\bf R}^n$ or $S^n$. The theory of compact proper Dupin hypersurfaces in $S^n$ is closely related to the theory of isoparametric hypersurfaces in $S^n$, and many important results in this field concern relations between these two classes of hypersurfaces. In 1985, Cecil and Ryan conjectured on p. 184 of the book, "Tight and Taut Immersions of Manifolds," that every compact, connected proper Dupin hypersurface $M \subset S^n$ is equivalent to an isoparametric hypersurface in $S^n$ by a Lie sphere transformation. This paper gives a survey of progress on this conjecture and related developments.Comment: 29 pages. arXiv admin note: substantial text overlap with arXiv:2011.1143
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Using Lie Sphere Geometry to Study Dupin Hypersurfaces in R^n
CrossWorks, 2021Co-Authors: Cecil, Thomas E.Abstract:A hypersurface M in Rn or Sn is said to be Dupin if along each Curvature surface, the corresponding Principal Curvature is constant. A Dupin hypersurface is said to be proper Dupin if each Principal Curvature has constant multiplicity on M, i.e., the number of distinct Principal Curvatures is constant on M. The notions of Dupin and proper Dupin hypersurfaces in Rn or Sn can be generalized to the setting of Lie sphere geometry, and these properties are easily seen to be invariant under Lie sphere transformations. This makes Lie sphere geometry an effective setting for the study of Dupin hypersurfaces, and many classifications of proper Dupin hypersurfaces have been obtained up to Lie sphere transformations. In these notes, we give a detailed introduction to this method for studying Dupin hypersurfaces in Rn or Sn , including proofs of several fundamental results. NOTE: This paper is a revised version of Notes on Lie Sphere Geometry and the Cyclides of Dupin and is published as such despite having a different title than the original paper
Koen Jacques Ferdinand Blanckaert - One of the best experts on this subject based on the ideXlab platform.
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saturation of Curvature induced secondary flow energy losses and turbulence in sharp open channel bends laboratory experiments analysis and modeling
Journal of Geophysical Research, 2009Co-Authors: Koen Jacques Ferdinand BlanckaertAbstract:The paper investigates the influence of relative bend Curvature on secondary flow, energy losses, and turbulence in sharp open-channel bends. These processes are important in natural streams with respect to sediment transport, the bathymetry and planimetry, mixing and spreading of pollutants, heat, oxygen, nutrients and biological species, and the conveyance capacity. Laboratory experiments were carried out in a configuration with rectangular cross section, consisting of a 193° bend of constant radius of Curvature, preceded and followed by straight reaches. This somewhat unnatural configuration allows investigating the adaptation of mean flow and turbulence to Curvature changes in open-channel bends, without contamination by other effects such as a mobile bed topography. Experiments were carried out for three different values of the Curvature ratio, defined as the ratio of centerline radius of Curvature over flow depth, which is the Principal Curvature parameter for hydrodynamic processes. Commonly used so-called linear models predict secondary flow to increase linearly with the Curvature ratio. The reported experiments show that the secondary flow hardly increases in the investigated very sharp bends when the Curvature ratio is further increased. This phenomenon is called saturation. Similar saturation is observed for the energy losses and the turbulence. This paper focuses on the analysis and modeling of the saturation of energy losses and turbulence. Secondary flow is found to be the dominant contribution to the Curvature-induced increase in turbulence production, which leads to increased energy losses. The Curvature-induced turbulence is explained by the fact that the turbulence dissipation lags behind the turbulence production, in agreement with the concept of the turbulence energy cascade. A 1-D model is proposed for the Curvature-induced energy losses and turbulence. It could extend 1-D or depth-averaged 2-D models that are commonly used in long-term (scale of a flood event to geological scales) or large-scale (scale of a river basin) investigations on flood propagation, hazard mapping, water quality modeling, and planimetric river evolution.
Panagiotis Perakis - One of the best experts on this subject based on the ideXlab platform.
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3d facial landmark detection under large yaw and expression variations
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013Co-Authors: Panagiotis Perakis, Georgios Passalis, Theoharis Theoharis, Ioannis A KakadiarisAbstract:A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of Principal Curvature values of a 3D object's surface, and spin images, local descriptors of the object's 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data.
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3d facial landmark detection face registration a 3d facial landmark model 3d local shape descriptors approach
2010Co-Authors: Panagiotis Perakis, Georgios Passalis, Theoharis Theoharis, Ioannis A KakadiarisAbstract:In this Technical Report a novel method for 3D landmark detec- tion and pose estimation suitable for both frontal and side 3D facial scans is presented. It utilizes 3D information by using 3D local shape descriptors to extract candidate interest points that are subsequently identied and labeled as anatomical landmarks. The shape descriptors include the shape index, a continuous map of Principal Curvature values of 3D objects, the extrusion map, a measure of the extruded areas of a 3D object and the spin images, local descriptors of the object's 3D point distribution. However, feature detection methods which use general purpose shape de- scriptors cannot identify and label the detected candidate land- marks. Therefore, the topological properties of the human face need to be taken into consideration. To this end, we use a Facial Landmark Model (FLM) of facial anatomical landmarks. Candi- date landmarks, irrespectively of the way they are generated, can be identied and labeled by matching them with the correspond- ing FLM. The proposed method is evaluated using an extensive 3D facial database, and achieves high accuracy even in challenging scenarios.
Eric N. Mortensen - One of the best experts on this subject based on the ideXlab platform.
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Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects
Machine Vision and Applications, 2008Co-Authors: Natalia Larios, Hongli Deng, Matt Sarpola, Jenny Yuen, Andrew Moldenke, David A. Lytle, Salvador Ruiz Correa, Robert Paasch, Wei Zhang, Eric N. MortensenAbstract:This paper describes a computer vision approach to automated rapid-throughput taxonomic identification of stonefly larvae. The long-term objective of this research is to develop a cost-effective method for environmental monitoring based on automated identification of indicator species. Recognition of stonefly larvae is challenging because they are highly articulated, they exhibit a high degree of intraspecies variation in size and color, and some species are difficult to distinguish visually, despite prominent dorsal patterning. The stoneflies are imaged via an apparatus that manipulates the specimens into the field of view of a microscope so that images are obtained under highly repeatable conditions. The images are then classified through a process that involves (a) identification of regions of interest, (b) representation of those regions as SIFT vectors (Lowe, in Int J Comput Vis 60(2):91–110, 2004) (c) classification of the SIFT vectors into learned “features” to form a histogram of detected features, and (d) classification of the feature histogram via state-of-the-art ensemble classification algorithms. The steps (a) to (c) compose the concatenated feature histogram (CFH) method. We apply three region detectors for part (a) above, including a newly developed Principal Curvature-based region (PCBR) detector. This detector finds stable regions of high Curvature via a watershed segmentation algorithm. We compute a separate dictionary of learned features for each region detector, and then concatenate the histograms prior to the final classification step. We evaluate this classification methodology on a task of discriminating among four stonefly taxa, two of which, Calineuria and Doroneuria , are difficult even for experts to discriminate. The results show that the combination of all three detectors gives four-class accuracy of 82% and three-class accuracy (pooling Calineuria and Doro-neuria ) of 95%. Each region detector makes a valuable contribution. In particular, our new PCBR detector is able to discriminate Calineuria and Doroneuria much better than the other detectors.
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automated insect identification through concatenated histograms of local appearance features
Workshop on Applications of Computer Vision, 2007Co-Authors: Natalia Larios, Hongli Deng, Matt Sarpola, Jenny Yuen, Andrew Moldenke, David A. Lytle, Salvador Ruiz Correa, Robert Paasch, Wei Zhang, Eric N. MortensenAbstract:This paper describes a fully automated stone fly-larvae classification system using a local features approach. It compares the three region detectors employed by the system: the Hessian-affine detector, the Kadir entropy detector and a new detector we have developed called the Principal Curvature based region detector (PCBR). It introduces a concatenated feature histogram (CFH) methodology that uses histograms of local region descriptors as feature vectors for classification and compares the results using this methodology to that of Opelt [Opelt, A, et.al., 2006.] on three stonefly identification tasks. Our results indicate that the PCBR detector outperforms the other two detectors on the most difficult discrimination task and that the use of all three detectors outperforms any other configuration. The CFH methodology also outperforms the Opelt methodology in these tasks