公开(公告)号：US20200311413A1

公开(公告)日：2020-10-01

申请号：US16368304

申请日：2019-03-28

Applicant: Konica Minolta Laboratory U.S.A., Inc.

Inventor： Yongmian ZHANG ,  Shubham AGARWAL

IPC: G06K9/00 ,  G06K9/62

Abstract: Image processing is performed on an input image generated from scanning a filled-in document form. The input image is evaluated against a blank version of various document forms in order to identify the form type of the filled-in document form. The evaluation results in identifying one of the blank document forms as a match to the filled-in document form. Each document form has a set of keywords. The evaluation uses a vector of keyword matches in the filled-in document form. Once a blank document form is identified to be match, the filled-in document form may be categorized according to that document form and/or data extracted from the filled-in document may be stored in association with keywords of that document form.

公开(公告)号：US20190205758A1

公开(公告)日：2019-07-04

申请号：US16326091

申请日：2017-12-13

Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

Inventor： Jingwen ZHU ,  Yongmian ZHANG

IPC: G06N3/08 ,  G06N3/04 ,  G16H30/40 ,  G06K9/62 ,  G06T7/11 ,  G06T7/13

CPC classification number: G06N3/08 ,  A61B5/72 ,  G06K9/6232 ,  G06K9/6256 ,  G06K9/6277 ,  G06K9/628 ,  G06K2209/05 ,  G06N3/0454 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/13 ,  G06T2207/10056 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G16H30/40

Abstract: Pathological analysis needs instance-level labeling on a histologic image with high accurate boundaries required. To this end, embodiments of the present invention provide a deep model that employs the DeepLab basis and the multi-layer deconvolution network basis in a unified model. The model is a deeply supervised network that allows to represent multi-scale and multi-level features. It achieved segmentation on the benchmark dataset at a level of accuracy which is significantly beyond all top ranking methods in the 2015 MICCAI Gland Segmentation Challenge. Moreover, the overall performance of the model surpasses the state-of-the-art Deep Multi-channel Neural Networks published most recently, and the model is structurally much simpler, more computational efficient and weight-lighted to learn.

公开(公告)号：US20170201717A1

公开(公告)日：2017-07-13

申请号：US15471386

申请日：2017-03-28

Applicant: KONICA MINOLTA LABORATORY U.S.A., INC.

Inventor： Foram Manish PARADKAR ,  Yongmian ZHANG ,  Haisong GU

IPC: H04N7/15 ,  G06K9/62 ,  G06K9/00

CPC classification number: H04N7/15 ,  G06F17/30516 ,  G06F17/3053 ,  G06F17/30598 ,  G06K9/00315 ,  G06K9/00335 ,  G06K9/00342 ,  G06K9/44 ,  G06K9/4604 ,  G06K9/469 ,  G06K9/6202 ,  G06K9/6267 ,  G06K9/6277

Abstract: A method, computer readable storage medium, and system are disclosed for improving communication productivity in a conference between two or more subjects, wherein at least one of the two or more subjects participates in the conference from a first location and one or more of the two or more subjects participate in the meeting from a second location. The method includes capturing, at least one first three-dimensional (3D) stream of data and at least one second three-dimensional (3D) stream of data on each of the two or more subjects participating in the conference; generating a synchrony score for the two or more subjects, wherein the synchrony score is calculated by comparing time series of skeletal data of each of the two or more subjects to one another for a defined period of time; and using the synchrony score to generate an engagement index between the two or more subjects.

公开(公告)号：US20170091948A1

公开(公告)日：2017-03-30

申请号：US15253324

申请日：2016-08-31

Applicant: Konica Minolta Laboratory U.S.A., Inc.

Inventor： Foram Manish PARADKAR ,  Yongmian ZHANG ,  Jingwen ZHU ,  Haisong GU

IPC: G06T7/00 ,  G06K9/52 ,  G06K9/62 ,  G06T7/60 ,  G06K9/66 ,  G06K9/46

CPC classification number: G06K9/4604 ,  G06K9/4671 ,  G06K9/52 ,  G06K9/6267 ,  G06K9/66 ,  G06K2009/4666 ,  G06T7/0002 ,  G06T7/11 ,  G06T7/13 ,  G06T7/194 ,  G06T7/60 ,  G06T7/73 ,  G06T2207/30024

Abstract: A method, a computer readable medium, and a system are disclosed for cell segmentation. The method including generating a binary mask from an input image of a plurality of cells, wherein the binary mask separates foreground cells from a background; classifying each of the cell regions of the binary mask into single cell regions, small cluster regions, and large cluster regions; performing, on each of the small cluster regions, a segmentation based on a contour shape of the small cluster region; performing, on each of the large cluster regions, a segmentation based on a texture in the large cluster regions; and outputting an image with cell boundaries.