Abstract: The present disclosure herein provides methods and systems that solves the technical problems of generating an efficient, accurate and light-weight 3-Dimensional (3-D) pose estimation framework for estimating the 3-D pose of an object present in an image used for the 3-dimensional (3D) model registration using deep learning, by training a composite network model with both shape features and image features of the object. The composite network model includes a graph neural network (GNN) for capturing the shape features of the object and a convolution neural network (CNN) for capturing the image features of the object. The graph neural network (GNN) utilizes the local neighbourhood information through the image features of the object and at the same time maintaining global shape property through the shape features of the object, to estimate the 3-D pose of the object.

Abstract: A system and a method for verification of a source code are provided. There as many techniques available that can be used for verification of software codes, however, it is difficult to determine appropriate technique that can be utilized for verification of a given software code. In an embodiment, the system receives a source code encoded with one or more specifications to be verified. A static analysis of the source code is performed to identify program features of the source code. The program features may include, but are not limited to, multiple return paths, loops with an unstructured control flow, loops with arrays, short ranges and numerical loops. Based on the identification of the program features, verification techniques are applied to the source code for the verification. Each verification technique of the one or more verification techniques is applied for a predetermined period of time and in a predefined order.

Abstract: This disclosure relates generally to ROI marking, and more particularly to system and method for marking ROI in a media stream using touchless hand gesture interface such as headmount devices. In one embodiment, the method includes recognizing a pointing object representative of a gesture in frames of the media stream while capturing the media stream. The media stream comprises a scene captured from a first person view (FPV) of a user. Locus of the pointing object is detected in subsequent frames subsequent of the media stream to select a ROI in the media stream. The locus of the pointing object configures a bounding box around the ROI. The ROI is tracked in frames of the media stream occurring subsequent to the subsequent frames in the media stream. The bounding box is updated around the ROI based on the tracking, wherein the updated bounding box encloses the ROI.

Abstract: A system and a method for verification of a source code are provided. There as many techniques available that can be used for verification of software codes, however, it is difficult to determine appropriate technique that can be utilized for verification of a given software code. In an embodiment, the system receives a source code encoded with one or more specifications to be verified. A static analysis of the source code is performed to identify program features of the source code. The program features may include, but are not limited to, multiple return paths, loops with an unstructured control flow, loops with arrays, short ranges and numerical loops. Based on the identification of the program features, verification techniques are applied to the source code for the verification. Each verification technique of the one or more verification techniques is applied for a predetermined period of time and in a predefined order.

Abstract: Hand gestures form an intuitive means of interaction in Augmented Reality/Mixed Reality (MR) applications. However, accurate gesture recognition can be achieved through deep learning models or with use of expensive sensors. Despite the robustness of these deep learning models, they are generally computationally expensive and obtaining real-time performance remains a challenge. Embodiments of the present disclosure provide systems and methods for classifying fingertip motion patterns into different hand gestures. Red Green Blue (RGB) images are fed as input to an object detector (MobileNetV2) for outputting hand candidate bounding box, which are then down-scaled to reduce processing time without compromising on the quality of image features.

Abstract: The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN).

Abstract: This disclosure relates generally to character detection and recognition, and more particularly to a method and system for container code recognition via Spatial Transformer Networks and Connected Component. The method comprises capturing an image of a container using an image capture device which is pre-processed using an image preprocessing module. The method further comprises extracting and filtering region proposals from the pre-processed image using a region extraction module to generate regrouped region proposals.

Abstract: This disclosure relates generally to hand-gesture recognition, and more particularly to system and method for detecting interaction of 3D dynamic hand gestures with frugal AR devices. In one embodiment, a method for hand-gesture recognition includes receiving frames of a media stream of a scene captured from a FPV of a user using RGB sensor communicably coupled to a wearable AR device. The media stream includes RGB image data associated with the frames of the scene. The scene comprises a dynamic hand gesture performed by the user. Temporal information associated with the dynamic hand gesture is estimated from the RGB image data by using a deep learning model. The estimated temporal information is associated with hand poses of the user and comprises key-points identified on user's hand in the frames. Based on said temporal information, the dynamic hand gesture is classified into predefined gesture classes by using multi-layered LSTM classification network.

Abstract: This disclosure relates generally to telecom inventory management, and more particularly to telecom inventory management via object recognition and localization using street-view images. In one embodiment, the method includes obtaining street-view images of a geographical area having telecom assets. The telecom assets are associated with corresponding GPS location coordinates. An object recognition model is applied to the street-view images to detect the telecom assets therein. Detecting the telecom assets includes associating the telecom assets with corresponding asset labels. A real-world location of the telecom assets is estimated in the geographical area by applying triangulation method on a set of multi-view images selected from the street-view images. The set of multi-view images are captured from a plurality of consecutive locations in vicinity of the telecom asset in the geographical area.

Abstract: This disclosure relates generally to hand-gesture recognition, and more particularly to system and method for detecting interaction of 3D dynamic hand gestures with frugal AR devices. In one embodiment, a method for hand-gesture recognition includes receiving frames of a media stream of a scene captured from a FPV of a user using RGB sensor communicably coupled to a wearable AR device. The media stream includes RGB image data associated with the frames of the scene. The scene comprises a dynamic hand gesture performed by the user. Temporal information associated with the dynamic hand gesture is estimated from the RGB image data by using a deep learning model. The estimated temporal information is associated with hand poses of the user and comprises key-points identified on user's hand in the frames. Based on said temporal information, the dynamic hand gesture is classified into predefined gesture classes by using multi-layered LSTM classification network.

Abstract: The most challenging problems in karyotyping are segmentation and classification of overlapping chromosomes in metaphase spread images. Often chromosomes are bent in different directions with varying degrees of bend. Tediousness and time consuming nature of the effort for ground truth creation makes it difficult to scale the ground truth for training phase. The present disclosure provides an end-to-end solution that reduces the cognitive burden of segmenting and karyotyping chromosomes. Dependency on experts is reduced by employing crowdsourcing while simultaneously addressing the issues associated with crowdsourcing. Identified segments through crowdsourcing are pre-processed to improve classification achieved by employing deep convolutional network (CNN).

Abstract: This disclosure relates generally to ROI marking, and more particularly to system and method for marking ROI in a media stream using touchless hand gesture interface such as headmount devices. In one embodiment, the method includes recognizing a pointing object representative of a gesture in frames of the media stream while capturing the media stream. The media stream comprises a scene captured from a first person view (FPV) of a user. Locus of the pointing object is detected in subsequent frames subsequent of the media stream to select a ROI in the media stream. The locus of the pointing object configures a bounding box around the ROI. The ROI is tracked in frames of the media stream occurring subsequent to the subsequent frames in the media stream. The bounding box is updated around the ROI based on the tracking, wherein the updated bounding box encloses the ROI.

Abstract: This disclosure relates generally to telecom inventory management, and more particularly to telecom inventory management via object recognition and localization using street-view images. In one embodiment, the method includes obtaining street-view images of a geographical area having telecom assets. The telecom assets are associated with corresponding GPS location coordinates. An object recognition model is applied to the street-view images to detect the telecom assets therein. Detecting the telecom assets includes associating the telecom assets with corresponding asset labels. A real-world location of the telecom assets is estimated in the geographical area by applying triangulation method on a set of multi-view images selected from the street-view images. The set of multi-view images are captured from a plurality of consecutive locations in vicinity of the telecom asset in the geographical area.