Abstract: There is provided an apparatus which can include a first module and a second module coupled to the first module. The first module can be configured to receive at least one input signal and/or generate at least one input signal. The input signal can be associated with at least one operating parameter and/or at least one target variable, associable with a structure (e.g., a pipeline). The second module can be configured to process the input signal by manner of data cleaning, data wrangling and/or data merging, to produce at least one output signal communicable for further processing (e.g., Machine-Learning based processing).

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure discloses method and a road region determination system for determining drivable road regions for safe navigation of an autonomous vehicle. The road region determination system receives real-time images of road in which autonomous vehicle is travelling, from image sensors. Each of real-time images of road is segmented into polygon regions and trend lines to obtain plurality of features using pre-trained road segmentation model. An orientation of the road is identified in real-time images as one of linear orientation and non-linear orientation based on slope measured between successive intermediate points distributed evenly on trend lines. The road region determination system manages redistribution of intermediate points on trend lines based on orientation of the road. Thereafter, paired points from intermediate points redistributed on the trend lines is identified, where paired points are connected using a horizontal line to determine drivable road regions for autonomous vehicle.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.