The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform
Eckehard Steinbach - One of the best experts on this subject based on the ideXlab platform.
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A Tactile Computer Mouse for the Display of Surface Material Properties
IEEE transactions on haptics, 2018Co-Authors: Matti Strese, Rania Hassen, Andreas Noll, Eckehard SteinbachAbstract:We present a novel input/output device to display the tactile properties of Surface Materials. The proposed Tactile Computer Mouse (TCM) is equipped with a series of actuators that can create perceptually relevant tactile cues to a user. The display capabilities of our TCM match the major tactile dimensions in human Surface Material perception, namely, hardness, friction, warmth, microscopic roughness, and macroscopic roughness. The TCM also preserves necessary interaction capabilities of a typical computer mouse. In addition to the TCM design, we introduce data acquisition procedures and concepts that are necessary to derive a parametric representation of a Surface Material and further demonstrate the corresponding rendering approach on the TCM. We conducted subjective experiments to determine tactile property ratings of real Materials, perceived property ratings using the TCM, and how precisely subjects match the real Materials to corresponding virtual Material representations using the TCM in the absence of visual and audible clues. Our experimental results show that our TCM successfully displays the five fundamental tactile dimensions and that the twenty participants were able to perceive the TCM-produced virtual Surface Material tactile sensations with a recognition rate of 89.6 percent for ten different Materials.
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multimodal feature based Surface Material classification
IEEE Transactions on Haptics, 2017Co-Authors: Matti Strese, Clemens Schuwerk, Albert Iepure, Eckehard SteinbachAbstract:When a tool is tapped on or dragged over an object Surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-Surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the Surfaces. We present an approach for tool-mediated Surface classification that combines these signals and demonstrate that the proposed method is robust against variable scan-time parameters. We examine freehand recordings of 69 textured Surfaces recorded by different users and propose a classification system that uses perception-related features, such as hardness, roughness, and friction; selected features adapted from speech recognition, such as modified cepstral coefficients applied to our acceleration signals; and Surface texture-related image features. We focus on mitigating the effect of variable contact force and exploration velocity conditions on these features as a prerequisite for a robust machine-learning-based approach for Surface classification. The proposed system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of textured Surfaces under variable freehand movement conditions, exerted by different human operators. The proposed subset of six features, selected from the described sound, image, friction force, and acceleration features, leads to a classification accuracy of 74 percent in our experiments when combined with a Naive Bayes classifier.
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WHC - Content-based Surface Material retrieval
2017 IEEE World Haptics Conference (WHC), 2017Co-Authors: Matti Strese, Yannik Boeck, Eckehard SteinbachAbstract:We present a content-based Surface Material retrieval (CBSMR) system for tool-mediated freehand Surface exploration that relies on features motivated by the main psychophysical dimensions of tactile Surface texture perception. The proposed approach does not require explicit scan force and scan velocity measurements. The perceptual features used in our CBSMR engine cover the tactile dimensions of friction, hardness, macroscopic roughness, microscopic roughness and warmth. We examine 108 Surface Materials recorded by different users and present the results of a free-sorting grouping experiment with 30 subjects which we conducted to determine the perceptual similarity of the Surface Materials in our database, providing a ground truth data set for perceived tactile similarity. The outcome of this experiment is used to demonstrate that the proposed CBSMR engine is able to determine the perceptually most similar Surface Materials for a test query. The proposed set of 8 features leads to a classification precision of 86% and a similarity precision-at-one of 30% when combined with a Euclidean Distance-based classifier.
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Deep Learning for Surface Material Classification Using Haptic and Visual Information
IEEE Transactions on Multimedia, 2016Co-Authors: Haitian Zheng, Lu Fang, Matti Strese, Yigitcan Ozer, Eckehard SteinbachAbstract:When a user scratches a hand-held rigid tool across an object Surface, an acceleration signal can be captured, which carries relevant information about the Surface Material properties. More importantly, such haptic acceleration signals can be used together with Surface images to jointly recognize the Surface Material. In this paper, we present a novel deep learning method dealing with the Surface Material classification problem based on a fully convolutional network, which takes the aforementioned acceleration signal and a corresponding image of the Surface texture as inputs. Compared to the existing Surface Material classification solutions which rely on a careful design of hand-crafted features, our method automatically extracts discriminative features utilizing advanced deep learning methodologies. Experiments performed on the TUM Surface Material database demonstrate that our method achieves state-of-the-art classification accuracy robustly and efficiently.
Janine R Hutchison - One of the best experts on this subject based on the ideXlab platform.
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false negative rate limit of detection and recovery efficiency performance of a validated macrofoam swab sampling method for low Surface concentrations of bacillus anthracis sterne and bacillus atrophaeus spores
Journal of Applied Microbiology, 2016Co-Authors: Gregory F Piepel, Brett G Amidan, Christopher A Barrett, B Deatherage L Kaiser, Michael A. Sydor, Janine R HutchisonAbstract:AIMS: We sought to evaluate the effects of Bacillus species, low Surface concentrations, and Surface Material on recovery efficiency (RE), false-negative rate (FNR) and limit of detection for recovering Bacillus spores using a validated macrofoam-swab sampling procedure. METHODS AND RESULTS: The performance of a macrofoam-swab sampling method was evaluated using Bacillus anthracis Sterne (BAS) and Bacillus atrophaeus Nakamura (BG) spores applied at nine low target Surface concentrations (2 to 500 CFU per plate or coupon) to positive-control plates and test coupons (25·8064 cm(2) ) of four Surface Materials (glass, stainless steel, vinyl tile and plastic). The Bacillus species and Surface Material had statistically significant effects on RE, but Surface concentration did not. Mean REs were the lowest for vinyl tile (50·8% with BAS and 40·2% with BG) and the highest for glass (92·8% with BAS and 71·4% with BG). FNR values (which ranged from 0 to 0·833 for BAS and from 0 to 0·806 for BG) increased as Surface concentration decreased in the range tested. Surface Material also had a statistically significant effect on FNR, with FNR the lowest for glass and highest for vinyl tile. Finally, FNR tended to be higher for BG than for BAS at lower Surface concentrations, especially for glass. CONCLUSIONS: Concentration and Surface Material had significant effects on FNR, with Bacillus species having a small effect. Species and Surface Material had significant effects on RE, with Surface concentration having a nonsignificant effect. SIGNIFICANCE AND IMPACT OF THE STUDY: The results provide valuable information on the performance of the macrofoam-swab method for low Surface concentrations of Bacillus spores, which can be adapted to assess the likelihood that there is no contamination when all macrofoam-swab samples fail to detect B. anthracis.
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recovery efficiency false negative rate and limit of detection performance of a validated macrofoam swab sampling method with low Surface concentrations of two bacillus anthracis surrogates
2015Co-Authors: Gregory F Piepel, Brett G Amidan, Brooke Deatherage L Kaiser, Michael A. Sydor, Janine R Hutchison, Christopher A BarrettAbstract:The performance of a macrofoam-swab sampling method was evaluated using Bacillus anthracis Sterne (BAS) and Bacillus atrophaeus Nakamura (BG) spores applied at nine low target amounts (2-500 spores) to positive-control plates and test coupons (2 in. × 2 in.) of four Surface Materials (glass, stainless steel, vinyl tile, and plastic). Test results from cultured samples were used to evaluate the effects of surrogate, Surface concentration, and Surface Material on recovery efficiency (RE), false negative rate (FNR), and limit of detection. For RE, surrogate and Surface Material had statistically significant effects, but concentration did not. Mean REs were the lowest for vinyl tile (50.8% with BAS, 40.2% with BG) and the highest for glass (92.8% with BAS, 71.4% with BG). FNR values ranged from 0 to 0.833 for BAS and 0 to 0.806 for BG, with values increasing as concentration decreased in the range tested (0.078 to 19.375 CFU/cm2, where CFU denotes ‘colony forming units’). Surface Material also had a statistically significant effect. A FNR-concentration curve was fit for each combination of surrogate and Surface Material. For both surrogates, the FNR curves tended to be the lowest for glass and highest for vinyl title. The FNR curves for BG tended tomore » be higher than for BAS at lower concentrations, especially for glass. Results using a modified Rapid Viability-Polymerase Chain Reaction (mRV-PCR) analysis method were also obtained. The mRV-PCR results and comparisons to the culture results will be discussed in a subsequent report.« less
Gregory F Piepel - One of the best experts on this subject based on the ideXlab platform.
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false negative rate limit of detection and recovery efficiency performance of a validated macrofoam swab sampling method for low Surface concentrations of bacillus anthracis sterne and bacillus atrophaeus spores
Journal of Applied Microbiology, 2016Co-Authors: Gregory F Piepel, Brett G Amidan, Christopher A Barrett, B Deatherage L Kaiser, Michael A. Sydor, Janine R HutchisonAbstract:AIMS: We sought to evaluate the effects of Bacillus species, low Surface concentrations, and Surface Material on recovery efficiency (RE), false-negative rate (FNR) and limit of detection for recovering Bacillus spores using a validated macrofoam-swab sampling procedure. METHODS AND RESULTS: The performance of a macrofoam-swab sampling method was evaluated using Bacillus anthracis Sterne (BAS) and Bacillus atrophaeus Nakamura (BG) spores applied at nine low target Surface concentrations (2 to 500 CFU per plate or coupon) to positive-control plates and test coupons (25·8064 cm(2) ) of four Surface Materials (glass, stainless steel, vinyl tile and plastic). The Bacillus species and Surface Material had statistically significant effects on RE, but Surface concentration did not. Mean REs were the lowest for vinyl tile (50·8% with BAS and 40·2% with BG) and the highest for glass (92·8% with BAS and 71·4% with BG). FNR values (which ranged from 0 to 0·833 for BAS and from 0 to 0·806 for BG) increased as Surface concentration decreased in the range tested. Surface Material also had a statistically significant effect on FNR, with FNR the lowest for glass and highest for vinyl tile. Finally, FNR tended to be higher for BG than for BAS at lower Surface concentrations, especially for glass. CONCLUSIONS: Concentration and Surface Material had significant effects on FNR, with Bacillus species having a small effect. Species and Surface Material had significant effects on RE, with Surface concentration having a nonsignificant effect. SIGNIFICANCE AND IMPACT OF THE STUDY: The results provide valuable information on the performance of the macrofoam-swab method for low Surface concentrations of Bacillus spores, which can be adapted to assess the likelihood that there is no contamination when all macrofoam-swab samples fail to detect B. anthracis.
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recovery efficiency false negative rate and limit of detection performance of a validated macrofoam swab sampling method with low Surface concentrations of two bacillus anthracis surrogates
2015Co-Authors: Gregory F Piepel, Brett G Amidan, Brooke Deatherage L Kaiser, Michael A. Sydor, Janine R Hutchison, Christopher A BarrettAbstract:The performance of a macrofoam-swab sampling method was evaluated using Bacillus anthracis Sterne (BAS) and Bacillus atrophaeus Nakamura (BG) spores applied at nine low target amounts (2-500 spores) to positive-control plates and test coupons (2 in. × 2 in.) of four Surface Materials (glass, stainless steel, vinyl tile, and plastic). Test results from cultured samples were used to evaluate the effects of surrogate, Surface concentration, and Surface Material on recovery efficiency (RE), false negative rate (FNR), and limit of detection. For RE, surrogate and Surface Material had statistically significant effects, but concentration did not. Mean REs were the lowest for vinyl tile (50.8% with BAS, 40.2% with BG) and the highest for glass (92.8% with BAS, 71.4% with BG). FNR values ranged from 0 to 0.833 for BAS and 0 to 0.806 for BG, with values increasing as concentration decreased in the range tested (0.078 to 19.375 CFU/cm2, where CFU denotes ‘colony forming units’). Surface Material also had a statistically significant effect. A FNR-concentration curve was fit for each combination of surrogate and Surface Material. For both surrogates, the FNR curves tended to be the lowest for glass and highest for vinyl title. The FNR curves for BG tended tomore » be higher than for BAS at lower concentrations, especially for glass. Results using a modified Rapid Viability-Polymerase Chain Reaction (mRV-PCR) analysis method were also obtained. The mRV-PCR results and comparisons to the culture results will be discussed in a subsequent report.« less
Daniel Kersten - One of the best experts on this subject based on the ideXlab platform.
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visual motion and the perception of Surface Material
Current Biology, 2011Co-Authors: Katja Doerschner, Roland W Fleming, Ozgur Yilmaz, Paul Schrater, Bruce Hartung, Daniel KerstenAbstract:Many critical perceptual judgments, from telling whether fruit is ripe to determining whether the ground is slippery, involve estimating the Material properties of Surfaces. Very little is known about how the brain recognizes Materials, even though the problem is likely as important for survival as navigating or recognizing objects. Though previous research has focused nearly exclusively on the properties of static images, recent evidence suggests that motion may affect the appearance of Surface Material. However, what kind of information motion conveys and how this information may be used by the brain is still unknown. Here, we identify three motion cues that the brain could rely on to distinguish between matte and shiny Surfaces. We show that these motion measurements can override static cues, leading to dramatic changes in perceived Material depending on the image motion characteristics. A classifier algorithm based on these cues correctly predicts both successes and some striking failures of human Material perception. Together these results reveal a previously unknown use for optic flow in the perception of Surface Material properties.
Matti Strese - One of the best experts on this subject based on the ideXlab platform.
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A Tactile Computer Mouse for the Display of Surface Material Properties
IEEE transactions on haptics, 2018Co-Authors: Matti Strese, Rania Hassen, Andreas Noll, Eckehard SteinbachAbstract:We present a novel input/output device to display the tactile properties of Surface Materials. The proposed Tactile Computer Mouse (TCM) is equipped with a series of actuators that can create perceptually relevant tactile cues to a user. The display capabilities of our TCM match the major tactile dimensions in human Surface Material perception, namely, hardness, friction, warmth, microscopic roughness, and macroscopic roughness. The TCM also preserves necessary interaction capabilities of a typical computer mouse. In addition to the TCM design, we introduce data acquisition procedures and concepts that are necessary to derive a parametric representation of a Surface Material and further demonstrate the corresponding rendering approach on the TCM. We conducted subjective experiments to determine tactile property ratings of real Materials, perceived property ratings using the TCM, and how precisely subjects match the real Materials to corresponding virtual Material representations using the TCM in the absence of visual and audible clues. Our experimental results show that our TCM successfully displays the five fundamental tactile dimensions and that the twenty participants were able to perceive the TCM-produced virtual Surface Material tactile sensations with a recognition rate of 89.6 percent for ten different Materials.
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multimodal feature based Surface Material classification
IEEE Transactions on Haptics, 2017Co-Authors: Matti Strese, Clemens Schuwerk, Albert Iepure, Eckehard SteinbachAbstract:When a tool is tapped on or dragged over an object Surface, vibrations are induced in the tool, which can be captured using acceleration sensors. The tool-Surface interaction additionally creates audible sound waves, which can be recorded using microphones. Features extracted from camera images provide additional information about the Surfaces. We present an approach for tool-mediated Surface classification that combines these signals and demonstrate that the proposed method is robust against variable scan-time parameters. We examine freehand recordings of 69 textured Surfaces recorded by different users and propose a classification system that uses perception-related features, such as hardness, roughness, and friction; selected features adapted from speech recognition, such as modified cepstral coefficients applied to our acceleration signals; and Surface texture-related image features. We focus on mitigating the effect of variable contact force and exploration velocity conditions on these features as a prerequisite for a robust machine-learning-based approach for Surface classification. The proposed system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of textured Surfaces under variable freehand movement conditions, exerted by different human operators. The proposed subset of six features, selected from the described sound, image, friction force, and acceleration features, leads to a classification accuracy of 74 percent in our experiments when combined with a Naive Bayes classifier.
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WHC - Content-based Surface Material retrieval
2017 IEEE World Haptics Conference (WHC), 2017Co-Authors: Matti Strese, Yannik Boeck, Eckehard SteinbachAbstract:We present a content-based Surface Material retrieval (CBSMR) system for tool-mediated freehand Surface exploration that relies on features motivated by the main psychophysical dimensions of tactile Surface texture perception. The proposed approach does not require explicit scan force and scan velocity measurements. The perceptual features used in our CBSMR engine cover the tactile dimensions of friction, hardness, macroscopic roughness, microscopic roughness and warmth. We examine 108 Surface Materials recorded by different users and present the results of a free-sorting grouping experiment with 30 subjects which we conducted to determine the perceptual similarity of the Surface Materials in our database, providing a ground truth data set for perceived tactile similarity. The outcome of this experiment is used to demonstrate that the proposed CBSMR engine is able to determine the perceptually most similar Surface Materials for a test query. The proposed set of 8 features leads to a classification precision of 86% and a similarity precision-at-one of 30% when combined with a Euclidean Distance-based classifier.
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Deep Learning for Surface Material Classification Using Haptic and Visual Information
IEEE Transactions on Multimedia, 2016Co-Authors: Haitian Zheng, Lu Fang, Matti Strese, Yigitcan Ozer, Eckehard SteinbachAbstract:When a user scratches a hand-held rigid tool across an object Surface, an acceleration signal can be captured, which carries relevant information about the Surface Material properties. More importantly, such haptic acceleration signals can be used together with Surface images to jointly recognize the Surface Material. In this paper, we present a novel deep learning method dealing with the Surface Material classification problem based on a fully convolutional network, which takes the aforementioned acceleration signal and a corresponding image of the Surface texture as inputs. Compared to the existing Surface Material classification solutions which rely on a careful design of hand-crafted features, our method automatically extracts discriminative features utilizing advanced deep learning methodologies. Experiments performed on the TUM Surface Material database demonstrate that our method achieves state-of-the-art classification accuracy robustly and efficiently.
