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230 records (took 0.031 seconds)

    Joint rolling shutter correction and image deblurring
    145.
    发明授权
    Joint rolling shutter correction and image deblurring 有权

    公开(公告)号：US11599974B2

    公开(公告)日：2023-03-07

    申请号：US17090508

    申请日：2020-11-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Quoc-Huy Tran ,  Bingbing Zhuang ,  Pan Ji ,  Manmohan Chandraker

    IPC: G06T5/50 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06T5/00

    Abstract: A method for jointly removing rolling shutter (RS) distortions and blur artifacts in a single input RS and blurred image is presented. The method includes generating a plurality of RS blurred images from a camera, synthesizing RS blurred images from a set of GS sharp images, corresponding GS sharp depth maps, and synthesized RS camera motions by employing a structure-and-motion-aware RS distortion and blur rendering module to generate training data to train a single-view joint RS correction and deblurring convolutional neural network (CNN), and predicting an RS rectified and deblurred image from the single input RS and blurred image by employing the single-view joint RS correction and deblurring CNN.

    Construction zone segmentation
    146.
    发明授权
    Construction zone segmentation 有权

    公开(公告)号：US11580334B2

    公开(公告)日：2023-02-14

    申请号：US17128612

    申请日：2020-12-21

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yi-Hsuan Tsai ,  Kihyuk Sohn ,  Buyu Liu ,  Manmohan Chandraker ,  Jong-Chyi Su

    IPC: G06K9/00 ,  G06K9/62 ,  G06T7/11 ,  G06N3/08 ,  G06V10/25 ,  G06V20/00

    Abstract: Systems and methods for construction zone segmentation are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes construction zones scenes having various objects. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

    Privacy-preserving visual recognition via adversarial learning
    147.
    发明授权
    Privacy-preserving visual recognition via adversarial learning 有权

    公开(公告)号：US11520923B2

    公开(公告)日：2022-12-06

    申请号：US16674425

    申请日：2019-11-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kihyuk Sohn ,  Manmohan Chandraker ,  Yi-Hsuan Tsai

    IPC: G06F21/62 ,  G06N3/04 ,  G06N3/08

    Abstract: A method for protecting visual private data by preventing data reconstruction from latent representations of deep networks is presented. The method includes obtaining latent features from an input image and learning, via an adversarial reconstruction learning framework, privacy-preserving feature representations to maintain utility performance and prevent the data reconstruction by simulating a black-box model inversion attack by training a decoder to reconstruct the input image from the latent features and training an encoder to maximize a reconstruction error to prevent the decoder from inverting the latent features while minimizing the task loss.

    LEARNING PRIVACY-PRESERVING OPTICS VIA ADVERSARIAL TRAINING
    150.
    发明申请
    LEARNING PRIVACY-PRESERVING OPTICS VIA ADVERSARIAL TRAINING 有权

    公开(公告)号：US20220067457A1

    公开(公告)日：2022-03-03

    申请号：US17412704

    申请日：2021-08-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Francesco Pittaluga ,  Giovanni Milione ,  Xiang Yu ,  Manmohan Chandraker ,  Yi-Hsuan Tsai ,  Zaid Tasneem

    IPC: G06K9/62 ,  G06N3/04 ,  G06F21/60

    Abstract: A method for acquiring privacy-enhancing encodings in an optical domain before image capture is presented. The method includes feeding a differentiable sensing model with a plurality of images to obtain encoded images, the differentiable sensing model including parameters for sensor optics, integrating the differentiable sensing model into an adversarial learning framework where parameters of attack networks, parameters of utility networks, and the parameters of the sensor optics are concurrently updated, and, once adversarial training is complete, validating efficacy of a learned sensor design by fixing the parameters of the sensor optics and training the attack networks and the utility networks to learn to estimate private and public attributes, respectively, from a set of the encoded images.

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