Abstract: Present disclosure discloses method and system for assessing health of a wood specimen. Method receives ultrasonic data for each of a plurality of alignments of a transmitter and associated receiver across a cross-section of one or more cross-sections along a length of the wood specimen. The ultrasonic data comprises a pulse velocity, a transit time and a distance travelled by an ultrasonic pulse between the transmitter and the associated receiver. Thereafter, method measures relative features of the wood specimen using the ultrasonic data. Subsequently, method identifies a condition of the cross-section of the wood specimen based on the relative features using a trained ML model. Upon identifying the condition of the cross-section to be defective, method determines a position of a defect in the cross-section of the wood specimen using the relative features and determines a severity of the defect using the trained ML model and the relative features.

Abstract: A method and system for task execution in dynamic heterogeneous robotic environment is disclosed. The method includes extracting data associated with a plurality of data categories and further deriving a plurality of factors from the extracted data. The method further includes determining a plurality of correlations among the plurality of factors based on the deep learning network. The method further includes deriving a plurality of sentiment parameters for a set of factors from the plurality of factors based on the plurality of correlations. The method may further includes simulating execution of the at least one current task by employing a plurality of robots. The method may further includes iteratively re-adjusting at least one of the plurality of sentiment parameters based on reinforcement learning performed on a result of the simulating. The method may further includes executing the at least one current task by employing the plurality of robots.

Abstract: A method and system for task execution in dynamic heterogeneous robotic environment is disclosed. The method includes extracting data associated with a plurality of data categories and further deriving a plurality of factors from the extracted data. The method further includes determining a plurality of correlations among the plurality of factors based on the deep learning network. The method further includes deriving a plurality of sentiment parameters for a set of factors from the plurality of factors based on the plurality of correlations. The method may further includes simulating execution of the at least one current task by employing a plurality of robots. The method may further includes iteratively re-adjusting at least one of the plurality of sentiment parameters based on reinforcement learning performed on a result of the simulating. The method may further includes executing the at least one current task by employing the plurality of robots.

Abstract: This disclosure relates generally to vehicle navigation maps, and more particularly to method and system for generating and updating vehicle navigation maps with features of navigation paths. In one embodiment, a method may include receiving a position of a vehicle and an environmental field of view (FOV) of the vehicle along a navigation path of the vehicle on a navigation map, extracting features of the navigation path from the environmental FOV, correlating the features with the navigation path on the navigation map based on the position, generating a features based navigation map based on the correlation, and transmitting the features based navigation map to a server of a navigation map service provider for storage and for subsequent use. The features based navigation map, when required to assist a navigation of another vehicle, may be accessed to assess the features of the navigation path and to provide the assessment to the other 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: The present disclosure relates to detection of obstacles by an autonomous vehicle in real-time. An obstacle detection system of an autonomous vehicle obtains point cloud data from single frame Light Detection and Ranging (LIDAR) data and camera data, of the surroundings of the vehicle. Further, the system processes the camera data to identify and extract regions comprising the obstacles. Further, the system extracts point cloud data corresponding to the obstacles and enhances the point cloud data, with the detailed information of the obstacles provided by the camera data. Further, the system processes the enhanced point cloud data to determine the obstacles along with the structure and orientation of obstacles. Upon determining the details of the obstacles, the system provides instructions to the vehicle to manoeuvre the obstacle. The disclosed obstacle detection system provides accurate data about a structure, an orientation and a location of the obstacles.

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: Disclosed herein is method and system for determining relationship among text segments in signboards for navigating autonomous vehicles. Images of signboards are captured analyzed to determine text segments in the images. Further, relationship among the identified text segments is determined based on relationship among plurality of text regions forming the text segments. Finally, information related to relationship of the text segments is provided to a navigation unit in the autonomous vehicle for facilitating navigation of the autonomous vehicle. In an embodiment, the method of present disclosure helps in eliminating prospective errors in the text identification process due to irregular arrangement of text segments in the signboards, by determining relationship among localized text segments in the images of the signboards.

Abstract: A method and system for guiding an autonomous vehicle to extract drivable road region is provided. The method involves capturing the road region ahead of the autonomous vehicle using a plurality of sensors. The road region is captured as a three-dimensional point cloud. Thereafter, a plurality of images of the road ahead the autonomous vehicle is captured using a camera. The captured road region and the plurality of images are mapped and compared. The mapping involves comparing the point cloud with plurality of pixels in the images. Based on the mapping, a training data is dynamically updated to incorporate current road conditions and a drivable road region is predicted. Finally, based on the drivable region, the autonomous vehicle is controlled and guided through the road.

Abstract: A method and device are described for controlling a vehicle using a location based dynamic dictionary. The method includes receiving, by a navigation device, an image from an image sensing device associated with the autonomous vehicle, wherein the image sensing device captures the image at a location of the autonomous vehicle. The method includes extracting, by the navigation device, navigation content from the image. The method further includes identifying, by the navigation device, the navigation content from the image by comparing the navigation content from the image with a dynamic dictionary. The dynamic dictionary is created based on the location of the autonomous vehicle. The method further includes controlling, by the navigation device, the autonomous vehicle based on the navigation content.

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: The present disclosure relates to detection of obstacles by an autonomous vehicle in real-time. An obstacle detection system of an autonomous vehicle obtains point cloud data from single frame Light Detection and Ranging (LIDAR) data and camera data, of the surroundings of the vehicle. Further, the system processes the camera data to identify and extract regions comprising the obstacles. Further, the system extracts point cloud data corresponding to the obstacles and enhances the point cloud data, with the detailed information of the obstacles provided by the camera data. Further, the system processes the enhanced point cloud data to determine the obstacles along with the structure and orientation of obstacles. Upon determining the details of the obstacles, the system provides instructions to the vehicle to manoeuvre the obstacle. The disclosed obstacle detection system provides accurate data about a structure, an orientation and a location of the obstacles.

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: This disclosure relates generally to vehicle navigation maps, and more particularly to method and system for generating and updating vehicle navigation maps with features of navigation paths. In one embodiment, a method may include receiving a position of a vehicle and an environmental field of view (FOV) of the vehicle along a navigation path of the vehicle on a navigation map, extracting features of the navigation path from the environmental FOV, correlating the features with the navigation path on the navigation map based on the position, generating a features based navigation map based on the correlation, and transmitting the features based navigation map to a server of a navigation map service provider for storage and for subsequent use. The features based navigation map, when required to assist a navigation of another vehicle, may be accessed to assess the features of the navigation path and to provide the assessment to the other vehicle.

Abstract: The present disclosure is related in general to image processing and a method and system for generating a multi-level classifier for image processing. An image processing system may analyze an input image of a predetermined image type to extract unique key feature descriptors associated with the input image. Further, the unique key feature descriptors are resized into a predefined standard template format which is utilized to develop an image type classifier. Furthermore, the unique key feature descriptors are resized into each of one or more template classifiers of the predetermined image type. Further, signal quality value of each of the template classifiers is determined by validating each of the unique key feature descriptors resized based on each of the template classifiers and an image prediction classifier is developed based on the signal quality value.

Abstract: Disclosed herein is method and system for determining relationship among text segments in signboards for navigating autonomous vehicles. Images of signboards are captured analyzed to determine text segments in the images. Further, relationship among the identified text segments is determined based on relationship among plurality of text regions forming the text segments. Finally, information related to relationship of the text segments is provided to a navigation unit in the autonomous vehicle for facilitating navigation of the autonomous vehicle. In an embodiment, the method of present disclosure helps in eliminating prospective errors in the text identification process due to irregular arrangement of text segments in the signboards, by determining relationship among localized text segments in the images of the signboards.

Abstract: A method and device are described for controlling a vehicle using a location based dynamic dictionary. The method includes receiving, by a navigation device, an image from an image sensing device associated with the autonomous vehicle, wherein the image sensing device captures the image at a location of the autonomous vehicle. The method includes extracting, by the navigation device, navigation content from the image. The method further includes identifying, by the navigation device, the navigation content from the image by comparing the navigation content from the image with a dynamic dictionary. The dynamic dictionary is created based on the location of the autonomous vehicle. The method further includes controlling, by the navigation device, the autonomous vehicle based on the navigation content.

Abstract: A method and system for guiding an autonomous vehicle to extract drivable road region is provided. The method involves capturing the road region ahead of the autonomous vehicle using a plurality of sensors. The road region is captured as a three-dimensional point cloud. Thereafter, a plurality of images of the road ahead the autonomous vehicle is captured using a camera. The captured road region and the plurality of images are mapped and compared. The mapping involves comparing the point cloud with plurality of pixels in the images. Based on the mapping, a training data is dynamically updated to incorporate current road conditions and a drivable road region is predicted. Finally, based on the drivable region, the autonomous vehicle is controlled and guided through the road.

Abstract: Method and device for identifying path boundary for vehicle navigation are disclosed. The method includes capturing a plurality of images of a path being traversed by a vehicle, through a plurality of cameras placed to meet predefined placement criteria. The method further includes processing shadowed regions within each of the plurality of images based on an associated Hue Saturation and Value (HSV) color space to generate a plurality of shadow processed images. The method includes identifying boundaries of the path within each of the plurality of shadow processed images based on a histogram of each of the plurality of shadow processed images. The method further includes estimating a distance between the boundaries of the path identified in the plurality of shadow processed images, based on a disparity map created for the plurality of shadow processed images and parameters associated with the plurality of cameras.

Abstract: The present disclosure is related in general to image processing and a method and system for generating a multi-level classifier for image processing. An image processing system may analyse an input image of a predetermined image type to extract unique key feature descriptors associated with the input image. Further, the unique key feature descriptors are resized into a predefined standard template format which is utilized to develop an image type classifier. Furthermore, the unique key feature descriptors are resized into each of one or more template classifiers of the predetermined image type. Further, signal quality value of each of the template classifiers is determined by validating each of the unique key feature descriptors resized based on each of the template classifiers and an image prediction classifier is developed based on the signal quality value.