Literature DB >> 27875214

Visual Vibrometry: Estimating Material Properties from Small Motions in Video.

Abe Davis, Katherine L Bouman, Justin G Chen, Michael Rubinstein, Oral Buyukozturk, Fredo Durand, William T Freeman.   

Abstract

The estimation of material properties is important for scene understanding, with many applications in vision, robotics, and structural engineering. This paper connects fundamentals of vibration mechanics with computer vision techniques in order to infer material properties from small, often imperceptible motions in video. Objects tend to vibrate in a set of preferred modes. The frequencies of these modes depend on the structure and material properties of an object. We show that by extracting these frequencies from video of a vibrating object, we can often make inferences about that object's material properties. We demonstrate our approach by estimating material properties for a variety of objects by observing their motion in high-speed and regular frame rate video.

Year:  2016        PMID: 27875214     DOI: 10.1109/TPAMI.2016.2622271

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  5 in total

1.  Material characterization of thin planar structures using full-field harmonic vibration response measured with stroboscopic holography.

Authors:  Arash Ebrahimian; Haimi Tang; Cosme Furlong; Jeffrey Tao Cheng; Nima Maftoon
Journal:  Int J Mech Sci       Date:  2021-03-14       Impact factor: 5.329

2.  Non-Contact Smartphone-Based Monitoring of Thermally Stressed Structures.

Authors:  Mehmet Sefa Orak; Amir Nasrollahi; Turgut Ozturk; David Mas; Belen Ferrer; Piervincenzo Rizzo
Journal:  Sensors (Basel)       Date:  2018-04-18       Impact factor: 3.576

3.  Operational Deflection Shapes Magnification and Visualization Using Optical-Flow-Based Image Processing.

Authors:  Adam Machynia; Ziemowit Dworakowski; Kajetan Dziedziech; Paweł Zdziebko; Jarosław Konieczny; Krzysztof Holak
Journal:  Sensors (Basel)       Date:  2021-12-14       Impact factor: 3.576

4.  Bayesian-Inference Embedded Spline-Kerneled Chirplet Transform for Spectrum-Aware Motion Magnification.

Authors:  Enjian Cai; Dongsheng Li; Jianyuan Lin; Hongnan Li
Journal:  Sensors (Basel)       Date:  2022-04-06       Impact factor: 3.576

5.  Amplitude-Based Filtering for Video Magnification in Presence of Large Motion.

Authors:  Xiu Wu; Xuezhi Yang; Jing Jin; Zhao Yang
Journal:  Sensors (Basel)       Date:  2018-07-17       Impact factor: 3.576

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.