Michael Krainin
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Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms
Sean K Bae
Yunus Emre
Jonathan Wang
Jiang Wu
Mehr Kashyap
Si-Hyuck Kang
Liwen Chen
Melissa Moran
Julie Cannon
Eric Steven Teasley
Allen Chai
Neal Wadhwa
Alejandra Maciel
Mike McConnell
Shwetak Patel
Jim Taylor
Jiening Zhan
Ming Po
Nature Communications Medicine (2022)
Preview abstract
Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment.
Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera.
Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min).
Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.
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AutoFlow: Learning a Better Training Set for Optical Flow
Daniel Vlasic
Charles Herrmann
Varun Jampani
Huiwen Chang
Ramin Zabih
Ce Liu
(2021)
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Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the performance of a model on a target dataset. AutoFlow takes a layered approach to render synthetic data, where the motion, shape, and appearance of each layer are controlled by learnable hyperparameters. Experimental results show that AutoFlow achieves state-of-the-art accuracy in pre-training both PWC-Net and RAFT. Our code and data are available at https://autoflow-google.github.io.
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SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting
Varun Jampani*
Huiwen Chang*
Kyle Gregory Sargent
Dominik Philemon Kaeser
Ce Liu
ICCV 2021 (2021)
Preview abstract
Single image 3D photography enables viewers to view a still image from novel viewpoints. Recent approaches for single-image view synthesis combine monocular depth network along with inpainting networks resulting in compelling novel view synthesis results. A drawback of these approaches is the use of hard layering making them not suitable to model intricate appearance effects such as matting. We present SLIDE, a modular and unified system for single image 3D photography that uses simple yet effective soft layering strategy to model appearance effects. In addition, we propose a novel depth-aware training of inpainting network suitable for 3D photography task. Extensive experimental analysis on 3 different view synthesis datasets in combination with user studies on in-the-wild image collections demonstrate the superior performance of our technique in comparison to existing strong baselines.
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Handheld Multi-Frame Super-Resolution
Bartlomiej Wronski
Manfred Ernst
Marc Levoy
ACM Transactions on Graphics (TOG), vol. 38 (2019), pp. 18
Preview abstract
Compared to DSLR cameras, smartphone cameras have smaller sensors, which limits their spatial resolution; smaller apertures, which limits their light gathering ability; and smaller pixels, which reduces their signal-to noise ratio. The use of color filter arrays (CFAs) requires demosaicing, which further degrades resolution.
In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images. We harness natural hand tremor, typical in handheld photography, to acquire a burst of raw frames with small offsets. These frames are then aligned and merged to form a single image with red, green, and blue values at every pixel site. This approach, which includes no explicit demosaicing step, serves to both increase image resolution and boost signal to noise ratio.
Our algorithm is robust to challenging scene conditions: local motion, occlusion, or scene changes. It runs at 100 milliseconds per 12-megapixel RAW input burst frame on mass-produced mobile phones.
Specifically, the algorithm is the basis of the Super-Res Zoom feature, as well as the default merge method in Night Sight mode (whether zooming or not) on Google's flagship phone.
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Robust image stitching using multiple registrations
Charles Herrmann
Chen Wang
Richard Strong Bowen
Emil Keyder
Ce Liu
Ramin Zabih
Springer-Verlag (2018), pp. 53-67
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Panorama creation is one of the most widely deployed techniques in computer vision. In addition to industry applications such as Google Street View, it is also used by millions of consumers in smartphones and other cameras. Traditionally, the problem is decomposed into three phases: registration, which picks a single transformation of each source image to align it to the other inputs, seam finding, which selects a source image for each pixel in the final result, and blending, which fixes minor visual artifacts. Here, we observe that the use of a single registration often leads to errors, especially in scenes with significant depth variation or object motion. We propose instead the use of multiple registrations, permitting regions of the image at different depths to be captured with greater accuracy. MRF inference techniques naturally extend to seam finding over multiple registrations, and we show here that their energy functions can be readily modified with new terms that discourage duplication and tearing, common problems that are exacerbated by the use of multiple registrations. Our techniques are closely related to layer-based stereo, and move image stitching closer to explicit scene modeling. Experimental evidence demonstrates that our techniques often generate significantly better panoramas when there is substantial motion or parallax.
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