The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 30, 2020

Filed:

Oct. 03, 2017
Applicant:

Sigtuple Technologies Private Limited, Bangalore, IN;

Inventors:

Bharath Cheluvaraju, Bangalore, IN;

Apurv Anand, Bangalore, IN;

Rohit Kumar Pandey, Bangalore, IN;

Tathagato Rai Dastidar, Bangalore, IN;

Abdul Aziz, Kolkata, IN;

Apoorva Jakalannanavar Halappa Manjula, Sirsi, IN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/194 (2017.01); G02B 21/36 (2006.01); G06T 1/00 (2006.01); G06T 5/00 (2006.01); G06T 7/136 (2017.01); G06T 7/11 (2017.01); G06T 7/70 (2017.01); G06T 7/50 (2017.01); G06T 5/50 (2006.01);
U.S. Cl.
CPC ...
G06T 7/194 (2017.01); G02B 21/367 (2013.01); G06K 9/0014 (2013.01); G06T 1/0007 (2013.01); G06T 5/003 (2013.01); G06T 5/50 (2013.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/50 (2017.01); G06T 7/70 (2017.01); G06T 2207/10028 (2013.01); G06T 2207/10056 (2013.01);
Abstract

Embodiments of present disclosure discloses system and method for acquisition of optimal images of object in multi-layer sample. Initially, images for FOV of multi-layer sample comprising objects are retrieved. Each of images are captured by varying focal depth of image capturing unit associated with system. Further, objects associated with multi-layer sample in FOV are identified. For identification, cumulative foreground mask of FOV is obtained based on adaptive thresholding performed on foreground image of FOV. Based on contour detection performed on cumulative foreground mask of FOV, object masks, corresponding to objects, is obtained for identifying objects. Further, sharpness of each of images associated with each of object masks is computed. Based on sharpness, optimal images from images for each of objects is selected for acquisition of optimal images of objects in multi-layer sample.


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