The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Clark F Olson - One of the best experts on this subject based on the ideXlab platform.
-
a general method for geometric feature matching and Model extraction
International Journal of Computer Vision, 2001Co-Authors: Clark F OlsonAbstract:Popular algorithms for feature matching and Model extraction fall into two broad categories: generate-and-test and Hough transform variations. However, both methods suffer from problems in practical implementations. Generate-and-test methods are sensitive to noise in the data. They often fail when the generated Model fit is poor due to error in the data used to generate the Model Position. Hough transform variations are less sensitive to noise, but implementations for complex problems suffer from large time and space requirements and from the detection of false positives. This paper describes a general method for solving problems where a Model is extracted from, or fit to, data that draws benefits from both generate-and-test methods and those based on the Hough transform, yielding a method superior to both. An important component of the method is the subdivision of the problem into many subproblems. This allows efficient generate-and-test techniques to be used, including the use of randomization to limit the number of subproblems that must be examined. Each subproblem is solved using pose space analysis techniques similar to the Hough transform, which lowers the sensitivity of the method to noise. This strategy is easy to implement and results in practical algorithms that are efficient and robust. We describe case studies of the application of this method to object recognition, geometric primitive extraction, robust regression, and motion segmentation.
-
a probabilistic formulation for hausdorff matching
Computer Vision and Pattern Recognition, 1998Co-Authors: Clark F OlsonAbstract:Matching images based on a Hausdorff measure has become popular for computer vision applications. However, no probabilistic Model has been used in these applications. This limits the formal treatment of several issues, such as feature uncertainties and prior knowledge. In this paper, we develop a probabilistic formulation of image matching in terms of maximum likelihood estimation that generalizes a version of Hausdorff matching. This formulation yields several benefits with respect to previous Hausdorff matching formulations. In addition, we show that the optimal Model Position in a discretized pose space can be located efficiently in this formation and we apply these techniques to a mobile robot self-localization problem.
-
CVPR - A probabilistic formulation for Hausdorff matching
Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 1Co-Authors: Clark F OlsonAbstract:Matching images based on a Hausdorff measure has become popular for computer vision applications. However, no probabilistic Model has been used in these applications. This limits the formal treatment of several issues, such as feature uncertainties and prior knowledge. In this paper, we develop a probabilistic formulation of image matching in terms of maximum likelihood estimation that generalizes a version of Hausdorff matching. This formulation yields several benefits with respect to previous Hausdorff matching formulations. In addition, we show that the optimal Model Position in a discretized pose space can be located efficiently in this formation and we apply these techniques to a mobile robot self-localization problem.
Stefano Lise - One of the best experts on this subject based on the ideXlab platform.
-
Identification of single nucleotide variants using Position-specific error estimation in deep sequencing data.
BMC Medical Genomics, 2019Co-Authors: Dimitrios Kleftogiannis, Marco Punta, Anuradha Jayaram, Shahneen Sandhu, Stephen Q. Wong, Delila Gasi Tandefelt, Vincenza Conteduca, Daniel Wetterskog, Gerhardt Attard, Stefano LiseAbstract:Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to Model Position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson Model-based statistical framework for SNV detection. Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic Positions and compare the results to digital droplet PCR experiments. AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .
-
Identification of single nucleotide variants using Position-specific error estimation in deep sequencing data
bioRxiv, 2018Co-Authors: Dimitrios Kleftogiannis, Marco Punta, Anuradha Jayaram, Shahneen Sandhu, Stephen Q. Wong, Delila Gasi Tandefelt, Vincenza Conteduca, Daniel Wetterskog, Gerhardt Attard, Stefano LiseAbstract:Background: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). Results: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to Model Position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson Model-based statistical framework for SNV detection. Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic Positions and compare the results to digital droplet PCR experiments. Conclusions: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.
Dimitrios Kleftogiannis - One of the best experts on this subject based on the ideXlab platform.
-
Identification of single nucleotide variants using Position-specific error estimation in deep sequencing data.
BMC Medical Genomics, 2019Co-Authors: Dimitrios Kleftogiannis, Marco Punta, Anuradha Jayaram, Shahneen Sandhu, Stephen Q. Wong, Delila Gasi Tandefelt, Vincenza Conteduca, Daniel Wetterskog, Gerhardt Attard, Stefano LiseAbstract:Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to Model Position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson Model-based statistical framework for SNV detection. Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic Positions and compare the results to digital droplet PCR experiments. AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve .
-
Identification of single nucleotide variants using Position-specific error estimation in deep sequencing data
bioRxiv, 2018Co-Authors: Dimitrios Kleftogiannis, Marco Punta, Anuradha Jayaram, Shahneen Sandhu, Stephen Q. Wong, Delila Gasi Tandefelt, Vincenza Conteduca, Daniel Wetterskog, Gerhardt Attard, Stefano LiseAbstract:Background: Targeted deep sequencing is a highly effective technology to identify known and novel single nucleotide variants (SNVs) with many applications in translational medicine, disease monitoring and cancer profiling. However, identification of SNVs using deep sequencing data is a challenging computational problem as different sequencing artifacts limit the analytical sensitivity of SNV detection, especially at low variant allele frequencies (VAFs). Results: To address the problem of relatively high noise levels in amplicon-based deep sequencing data (e.g. with the Ion AmpliSeq technology) in the context of SNV calling, we have developed a new bioinformatics tool called AmpliSolve. AmpliSolve uses a set of normal samples to Model Position-specific, strand-specific and nucleotide-specific background artifacts (noise), and deploys a Poisson Model-based statistical framework for SNV detection. Our tests on both synthetic and real data indicate that AmpliSolve achieves a good trade-off between precision and sensitivity, even at VAF below 5% and as low as 1%. We further validate AmpliSolve by applying it to the detection of SNVs in 96 circulating tumor DNA samples at three clinically relevant genomic Positions and compare the results to digital droplet PCR experiments. Conclusions: AmpliSolve is a new tool for in-silico estimation of background noise and for detection of low frequency SNVs in targeted deep sequencing data. Although AmpliSolve has been specifically designed for and tested on amplicon-based libraries sequenced with the Ion Torrent platform it can, in principle, be applied to other sequencing platforms as well. AmpliSolve is freely available at https://github.com/dkleftogi/AmpliSolve.
Aurelien Borgoltz - One of the best experts on this subject based on the ideXlab platform.
-
Laser Displacement Sensors for Wind Tunnel Model Position Measurements.
Sensors, 2018Co-Authors: Matthew S. Kuester, Nanyaporn Intaratep, Aurelien BorgoltzAbstract:: Wind tunnel measurements of two-dimensional wing sections, or airfoils, are the building block of aerodynamic predictions for many aerodynamic applications. In these experiments, the forces and pitching moment on the airfoil are measured as a function of the orientation of the airfoil relative to the incoming airflow. Small changes in this angle (called the angle of attack, or α ) can create significant changes in the forces and moments, so accurately measuring the angle of attack is critical in these experiments. This work describes the implementation of laser displacement sensors in a wind tunnel; the sensors measured the distance between the wind tunnel walls and the airfoil, which was then used to calculate the Model Position. The uncertainty in the measured laser distances, based on the sensor resolution and temperature drift, is comparable to the uncertainty in traditional linear encoder measurements. Distances from multiple sensors showed small, but statistically significant, amounts of Model deflection and rotation that would otherwise not have been detected, allowing for an improved angle of attack measurement.
Yogetsu Bando - One of the best experts on this subject based on the ideXlab platform.
-
Effect on thickness of a single-layer mouthguard of Positional relationship between suction port of the vacuum forming device and the Model.
Dental traumatology : official publication of International Association for Dental Traumatology, 2021Co-Authors: Mutsumi Takahashi, Yogetsu BandoAbstract:BACKGROUND/AIM Wearing a mouthguard reduces the risk of sport-related injuries, but the thickness has a large effect on its efficacy and safety. The aim of this study was to investigate the effect on the thickness of a single-layer mouthguard of the Positional relationship between the suction port of the vacuum forming device and the Model. MATERIALS AND METHODS Ethylene-vinyl-acetate sheets of 4.0-mm-thickness and a vacuum forming machine were used. Two hard plaster Models were prepared: Model A was 25-mm at the anterior teeth and 20-mm at the molar, and Model B was trimmed so the bucco-lingual width was half that of Model A. Three Model Positions on the forming table were examined: (a) P20, where the Model anterior rim was located in front of the suction port, (b) P30, where the Model anterior rim and front edge of the suction port were close, and (c) P43, where the Model anterior rim and palatal rim were located on the suction port. Six mouthguards were fabricated for each condition. Thickness differences due to Model form and Model Position were analyzed. RESULTS Thickness differences due to Model form were observed at the incisal edge and labial surface, and Model A was significantly thicker than Model B in P43 (P
-
Effect of acute angle Model on mouthguard thickness with the thermoforming method and moving the Model Position just before fabrication.
Dental traumatology : official publication of International Association for Dental Traumatology, 2020Co-Authors: Mutsumi Takahashi, Yogetsu BandoAbstract:BACKGROUND/AIM The effectiveness and safety of mouthguards are affected by their thickness. The aim of this study was to investigate the effect of an acute angle Model on the mouthguard thickness with the thermoforming method in which the Model Position was moved just before fabrication. MATERIALS AND METHODS Mouthguards were thermoformed using 4.0 mm thick ethylene vinyl acetate sheets and a vacuum forming machine. Three hard plaster Models were prepared: 1) the angle of the labial surface to the Model base was 90°, and the anterior height was 25 mm (Model A); 2) the angle was 90°, and the anterior height was 30 mm (Model B); and 3) the angle was 80°, and the anterior height was 30 mm (Model C). The sheet was softened until it sagged 15 mm, after which the sheet frame was lowered to cover the Model. The Model was then pushed from behind to move it forward, and the vacuum was switched on (MP). The Model was moved 20 mm whereas a control Model was not moved. Mouthguard thickness was measured using a specialized caliper. The differences in mouthguard thicknesses due to Model forms and forming conditions were analyzed by two-way ANOVA and Bonferroni's multiple comparison tests. RESULTS The MP tended to be thicker than the control in all Models. In the controls, Model C was significantly thicker than Models A and B at the labial and buccal surfaces. In MP, Model A was significantly thicker than Models B and C on the labial surface. On the labial and buccal surfaces in MP, Model C was significantly thicker than Model B. CONCLUSIONS This study suggested that in the thermoforming method in which the Model Position was moved just before fabrication, reducing the height was more effective than changing the angle of the Model to ensure the appropriate thickness.
-
Movement of Model Position just before vacuum forming to ensure mouthguard thickness: Part 2 Effect of Model moving distance.
Dental traumatology : official publication of International Association for Dental Traumatology, 2019Co-Authors: Mutsumi Takahashi, Yogetsu BandoAbstract:BACKGROUND/AIM Wearing a mouthguard during sports reduces the risk of dental injury via absorbing impact forces, and the effectiveness and safety of the mouthguard are closely linked to the mouthguard material and thickness. The aim of this study was to clarify the suppression effect of the thickness reduction of the mouthguard when changing the moving distance of the Model forward in a stepwise manner. MATERIALS AND METHODS Ethylene-vinyl acetate sheets of 4.0 mm thick and a vacuum forming machine were used. The working Model was placed at a Position 40 mm from the front of the forming unit. The sheet was softened until it sagged 15 mm, and the sheet frame was lowered and covered the Model. The Model was then pushed from the back to move it forward, and the vacuum was switched on. The Model was moved 10 (MP-10), 20 (MP-20), or 30 mm (MP-30). The control Model was not moved. The thickness after formation was measured with a specialized caliper. Differences in the mouthguard thickness caused by the forming conditions were analyzed by one-way ANOVA and Bonferroni's multiple comparison tests. RESULTS Significant differences were observed between the control and each MP condition (P
-
Thermoforming method to effectively maintain mouthguard thickness: Effect of moving the Model Position just before vacuum formation.
Dental traumatology : official publication of International Association for Dental Traumatology, 2018Co-Authors: Mutsumi Takahashi, Yogetsu BandoAbstract:BACKGROUND/AIMS Mouthguards can reduce the risk of sports-related injuries but the sheet material and thickness have a large effect on their efficacy and safety. The aim of this study was to investigate the effect of the thermoforming technique that moves the Model Position just before vacuum formation. MATERIALS AND METHODS Ethylene vinyl acetate sheets of 4.0-mm thickness and a vacuum forming machine were used. The working Model was placed with its anterior rim Positioned 40 mm from the front of the forming table. Three forming conditions were compared: (a) The sheet was formed when it sagged 15 mm at the top of the post under normal conditions (control); (b) the sheet frame was lowered to and heated at 50 mm from the level of ordinary use, and the sheet was formed when it sagged 15 mm (LH); and (c) the sheet frame at the top of the post was lowered and covered on the Model when it sagged 15 mm. Subsequently, the rear side of the Model was pushed to move it forward 20 mm, and it was then formed (MP). Sheet thickness after fabrication was determined for the incisal edge, labial surface, and buccal surface using a specialized caliper accurate to 0.1 mm. Thickness differences among forming conditions were analyzed by one-way ANOVA and Bonferroni's multiple comparison tests. RESULTS A significant difference was observed for all measurement points, and the thickness after formation increased in the order of control, LH, and MP. Particularly on the labial surface, MP was able to yield about 1.7 times the thickness (about 3.1 mm) of the control. CONCLUSION The forming method of moving the Model forward just before vacuum formation was effective for suppressing the mouthguard thickness reduction, which is capable of securing the labial thickness at 3 mm or more with a single layer.