公开(公告)号：US11113838B2

公开(公告)日：2021-09-07

申请号：US16814248

申请日：2020-03-10

Applicant: NEC Laboratories America, Inc.

Inventor： Yi Yang ,  Srimat Chakradhar ,  Tarang Chugh

IPC: G06T7/70 ,  G06F16/58 ,  G06N3/08 ,  G06N3/04 ,  G06T1/20 ,  G06T3/40 ,  G06T7/90 ,  G06K9/62

Abstract: A computer-implemented method executed by at least one processor for detecting tattoos on a human body is presented. The method includes inputting a plurality of images into a tattoo detection module, selecting one or more images of the plurality of images including tattoos with at least three keypoints, the at least three keypoints having auxiliary information related to the tattoos, manually labeling tattoo locations in the plurality of images including tattoos to create labeled tattoo images, increasing a size of the labeled tattoo images identified to be below a predetermined threshold by padding a width and height of the labeled tattoo images, training two different tattoo detection deep learning models with the labeled tattoo images defining tattoo training data, and executing either the first tattoo detection deep learning model or the second tattoo detection deep learning model based on a performance of a general-purpose graphical processing unit.

公开(公告)号：US20200311962A1

公开(公告)日：2020-10-01

申请号：US16814248

申请日：2020-03-10

Applicant: NEC Laboratories America, Inc.

Inventor： Yi Yang ,  Srimat Chakradhar ,  Tarang Chugh

IPC: G06T7/70 ,  G06F16/58 ,  G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06T1/20 ,  G06T3/40 ,  G06T7/90

Abstract: A computer-implemented method executed by at least one processor for detecting tattoos on a human body is presented. The method includes inputting a plurality of images into a tattoo detection module, selecting one or more images of the plurality of images including tattoos with at least three keypoints, the at least three keypoints having auxiliary information related to the tattoos, manually labeling tattoo locations in the plurality of images including tattoos to create labeled tattoo images, increasing a size of the labeled tattoo images identified to be below a predetermined threshold by padding a width and height of the labeled tattoo images, training two different tattoo detection deep learning models with the labeled tattoo images defining tattoo training data, and executing either the first tattoo detection deep learning model or the second tattoo detection deep learning model based on a performance of a general-purpose graphical processing unit.