The Experts below are selected from a list of 276 Experts worldwide ranked by ideXlab platform
Anna Mikaelyan - One of the best experts on this subject based on the ideXlab platform.
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Frequency Map by Structure Tensor in Logarithmic Scale Space and Forensic Fingerprints
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016Co-Authors: Josef Bigun, Anna MikaelyanAbstract:Increasingly, absolute frequency and orientation maps are needed, e.g. for forensics. We introduce a non-linear Scale space via the logarithm of trace of the Structure Tensor. Therein, frequency estimation becomes an orientation estimation problem. We show that this offers significant advantages, including construction of efficient isotropic estimations of dense maps of frequency. In fingerprints, both maps are shown to improve each other in an enhancement scheme via Gabor filtering. We suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. Furthermore, we present new evidence that frequency maps are useful attributes of minutiae. We verify that the suggested method compares favorably with state of the art using forensic fingerprints as test bed, and test images where the ground truth is known. In evaluations, we use public data sets and published methods only.
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dense frequency maps by structure tensor and Logarithmic Scale space application to forensic fingerprints
2015Co-Authors: Josef Bigun, Anna MikaelyanAbstract:Automatic feature extraction still remains a relevant image and signal processing problem even tough both the field and technologies are developing rapidly. Images of low quality, where it is extremely difficult to reliably process image information automatically, are of special interest. To such images we can refer forensic fingerprints, which are left unintentionally on different surfaces andare contaminated by several of the most difficult noise types. For this reason, identification of fingerprints is mainly based on the visual skills of forensic examiners. We address the problem caused by low quality in fingerprints by connecting different sources of information together, yielding dense frequency and orientation maps in an iterative scheme. This scheme comprises smoothing ofthe original, but only along, ideally never across, the ridges. Reliable estimation of dense maps allows to introduce a continuous fingerprint ridge counting technique. In fingerprint scenario the collection of irrefutable tiny details, e.g. bifurcation of ridges, called minutiae, is used to tie the pattern of such points and their tangential directions to the finger producing the pattern. This limited feature set, location and direction of minutiae, is used in current AFIS systems, while fingerprint examiners use the extended set of features, including the image information between the points. With reasonably accurate estimationsof dense frequency and orientation maps at hand, we have been able to propose a novel compact feature descriptor of arbitrary points. We have used these descriptors to show that the image information between minutiae can be extracted automatically and be valuable for identity establishment of forensic images even if the underlying images are noisy. We collect and compress the image information in the neighborhoods of the fine details, such as minutiae, to vectors, one per minutia, and use the vectors to "color" the minutiae. When matching two patterns (of minutiae) even the color of the minutia must match to conclude that they come from the same identity. This feature development has been concentrated and tested on forensic fingerprint images. However, we have also studied an extension of its application area to other biometrics, periocular regions of faces. This allowed us to test the persistence of automatically extracted features across different types of imagesand image qualities, supporting its generalizability.
Salvatore Tabbone - One of the best experts on this subject based on the ideXlab platform.
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ICDAR - A Shape Descriptor Combining Logarithmic-Scale Histogram of Radon Transform and Phase-Only Correlation Function
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Makoto Hasegawa, Salvatore TabboneAbstract:A shape descriptor combining the histogram of the Radon transform, the Logarithmic-Scale histogram, and the phase-only correlation function is proposed. Applying a Logarithmic-Scale to the Radon transform, the shape scaling and rotation become two-dimensional translation in our descriptor without any normalization. The geometric invariance to translation, when we match two shapes, are kept using the phase-only correlation function. In addition, we can determine with this function the rotation angle and the Scale parameter between two shapes. Our descriptor is robust to shape occlusion and noise also.
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A Shape Descriptor Combining Logarithmic-Scale Histogram of Radon Transform and Phase-Only Correlation Function
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Makoto Hasegawa, Salvatore TabboneAbstract:A shape descriptor combining the histogram of the Radon transform, the Logarithmic-Scale histogram, and the phase-only correlation function is proposed. Applying a Logarithmic-Scale to the Radon transform, the shape scaling and rotation become two-dimensional translation in our descriptor without any normalization. The geometric invariance to translation, when we match two shapes, are kept using the phase-only correlation function. In addition, we can determine with this function the rotation angle and the Scale parameter between two shapes. Our descriptor is robust to shape occlusion and noise also.
Josef Bigun - One of the best experts on this subject based on the ideXlab platform.
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Frequency Map by Structure Tensor in Logarithmic Scale Space and Forensic Fingerprints
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016Co-Authors: Josef Bigun, Anna MikaelyanAbstract:Increasingly, absolute frequency and orientation maps are needed, e.g. for forensics. We introduce a non-linear Scale space via the logarithm of trace of the Structure Tensor. Therein, frequency estimation becomes an orientation estimation problem. We show that this offers significant advantages, including construction of efficient isotropic estimations of dense maps of frequency. In fingerprints, both maps are shown to improve each other in an enhancement scheme via Gabor filtering. We suggest a novel continuous ridge counting method, relying only on dense absolute frequency and orientation maps, without ridge detection, thinning, etc. Furthermore, we present new evidence that frequency maps are useful attributes of minutiae. We verify that the suggested method compares favorably with state of the art using forensic fingerprints as test bed, and test images where the ground truth is known. In evaluations, we use public data sets and published methods only.
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dense frequency maps by structure tensor and Logarithmic Scale space application to forensic fingerprints
2015Co-Authors: Josef Bigun, Anna MikaelyanAbstract:Automatic feature extraction still remains a relevant image and signal processing problem even tough both the field and technologies are developing rapidly. Images of low quality, where it is extremely difficult to reliably process image information automatically, are of special interest. To such images we can refer forensic fingerprints, which are left unintentionally on different surfaces andare contaminated by several of the most difficult noise types. For this reason, identification of fingerprints is mainly based on the visual skills of forensic examiners. We address the problem caused by low quality in fingerprints by connecting different sources of information together, yielding dense frequency and orientation maps in an iterative scheme. This scheme comprises smoothing ofthe original, but only along, ideally never across, the ridges. Reliable estimation of dense maps allows to introduce a continuous fingerprint ridge counting technique. In fingerprint scenario the collection of irrefutable tiny details, e.g. bifurcation of ridges, called minutiae, is used to tie the pattern of such points and their tangential directions to the finger producing the pattern. This limited feature set, location and direction of minutiae, is used in current AFIS systems, while fingerprint examiners use the extended set of features, including the image information between the points. With reasonably accurate estimationsof dense frequency and orientation maps at hand, we have been able to propose a novel compact feature descriptor of arbitrary points. We have used these descriptors to show that the image information between minutiae can be extracted automatically and be valuable for identity establishment of forensic images even if the underlying images are noisy. We collect and compress the image information in the neighborhoods of the fine details, such as minutiae, to vectors, one per minutia, and use the vectors to "color" the minutiae. When matching two patterns (of minutiae) even the color of the minutia must match to conclude that they come from the same identity. This feature development has been concentrated and tested on forensic fingerprint images. However, we have also studied an extension of its application area to other biometrics, periocular regions of faces. This allowed us to test the persistence of automatically extracted features across different types of imagesand image qualities, supporting its generalizability.
Makoto Hasegawa - One of the best experts on this subject based on the ideXlab platform.
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ICDAR - A Shape Descriptor Combining Logarithmic-Scale Histogram of Radon Transform and Phase-Only Correlation Function
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Makoto Hasegawa, Salvatore TabboneAbstract:A shape descriptor combining the histogram of the Radon transform, the Logarithmic-Scale histogram, and the phase-only correlation function is proposed. Applying a Logarithmic-Scale to the Radon transform, the shape scaling and rotation become two-dimensional translation in our descriptor without any normalization. The geometric invariance to translation, when we match two shapes, are kept using the phase-only correlation function. In addition, we can determine with this function the rotation angle and the Scale parameter between two shapes. Our descriptor is robust to shape occlusion and noise also.
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A Shape Descriptor Combining Logarithmic-Scale Histogram of Radon Transform and Phase-Only Correlation Function
2011 International Conference on Document Analysis and Recognition, 2011Co-Authors: Makoto Hasegawa, Salvatore TabboneAbstract:A shape descriptor combining the histogram of the Radon transform, the Logarithmic-Scale histogram, and the phase-only correlation function is proposed. Applying a Logarithmic-Scale to the Radon transform, the shape scaling and rotation become two-dimensional translation in our descriptor without any normalization. The geometric invariance to translation, when we match two shapes, are kept using the phase-only correlation function. In addition, we can determine with this function the rotation angle and the Scale parameter between two shapes. Our descriptor is robust to shape occlusion and noise also.
Torsten Moller - One of the best experts on this subject based on the ideXlab platform.
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transfer functions on a Logarithmic Scale for volume rendering
Graphics Interface, 2004Co-Authors: Simeon Potts, Torsten MollerAbstract:Manual opacity transfer function editing for volume rendering can be a difficult and counter-intuitive process. This paper proposes a Logarithmically Scaled editor, and argues that such a Scale relates the height of the transfer function to the rendered intensity of a region of particular density in the volume almost directly, resulting in much improved, simpler manual transfer function editing.
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Graphics Interface - Transfer functions on a Logarithmic Scale for volume rendering
2004Co-Authors: Simeon Potts, Torsten MollerAbstract:Manual opacity transfer function editing for volume rendering can be a difficult and counter-intuitive process. This paper proposes a Logarithmically Scaled editor, and argues that such a Scale relates the height of the transfer function to the rendered intensity of a region of particular density in the volume almost directly, resulting in much improved, simpler manual transfer function editing.
