Wipro Limited

Mohd Zaid

Sujatha Jagannath

Balaji Govindaraj

Raghottam Mannopantar

Amrit Paul

The present disclosure relates to a method of stitching images captured by a vehicle. A first image and a second image are received. The first image and the second image are segmented based on characteristics of pixels. Groups of pixels having similar characteristics are identified to form clusters in a predetermined portion of overlap of the first image and the second image. A confidence score is generated for the first image and the second image. A difference in the confidence score is computed. At least one of, the first image capturing unit and the second image capturing unit is aligned to capture at least one of, a first aligned image and a second aligned image based on the difference in the confidence score. The first aligned image and the second aligned image are stitched.

Method of stitching images captured by a vehicle, and a system thereof

The present disclosure relates to a method and system for recognition of data in one or more images. The method receives and segments the one or more images to identify segmented objects. Further, the method generates an object relationship data for each of the segmented objects and determines a knowledge base representation of the object relationship data based on defined features. Furthermore, a Recurrent Neural Network (RNN) is trained based on the knowledge base representation to determine an appropriate Neural Network (NN) having optimum confidence score. Based on the appropriate NN selected, the objects in the input image is predicted and transmitted to external systems for decision making. Thus, enabling accurate text/object identification for the images having different background variations using the dynamic selection of NN, thereby facilitating more effective decision making.

Method and a system for recognition of data in one or more images

A method and device for detecting objects from scene images by using dynamic knowledge base is disclosed. The method includes segmenting a scene image captured by at least one camera into a plurality of image segments. Each of the plurality of image segments include a plurality of pixels. The method further includes detecting at least one object in each of the plurality of image segments. The method includes finding a plurality of similar images from an online database based on the at least one object. The method further includes identifying using a knowledge base engine, at least one similar object in the plurality of similar images. The method includes updating the knowledge base engine based on the at least one similar object identified from the plurality of similar images. The method further includes training a neural network to detect objects from scene images, based on the updated knowledge base engine.

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Delhi, India

Mohd Zaid