Abstract: The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.

Abstract: Automation is the key to build efficient workflows with minimum effort consumption. However, there is a large gap in workflow synthesis for automated AI application development. Computer vision workflow synthesis largely rely on domain expert due to lack of generalization over solution search space for given goal. This search space for creating suitable solution(s) using available algorithms is quite vast, which makes exploratory work of solution building a time-, effort- and intellect intensive endeavor. Embodiments of the present disclosure provide system and method for goal-driven algorithm selection approach for building computer vision workflows on the fly. The system generates one or more task workflows with associated success probability depending on initial conditions and input natural language goal query by combining various image processing algorithms. Symbolic AI planning is aided by Reinforcement Learning to recommend optimal workflows that are robust and adaptive to changes in the environment.

Abstract: The disclosure herein relates to methods and systems for localized smoke removal and color restoration of a real-time video. Conventional techniques apply the de-smoking process only on a single image, by finding the regions having the smoke, based on manual air-light estimation. In addition, regaining original colors of de-smoked image is quite challenging. The present disclosure herein solves the technical problems. In the first stage, video frames having the smoky and smoke-free video frames are identified, from the video received in the real-time. In the second stage, an air-light is estimated automatically using a combined feature map. An intermediate de-smoked video frame for each smoky video frame is generated based on the air-light using a de-smoking algorithm. In the third and the last stage, a smoke-free video reference frame is used to compensate for color distortions introduced by the de-smoking algorithm in the second stage.

Abstract: This disclosure relates to automatic creation of an image processing pipeline that has a dependency on image processing domain experts. Given the image processing problem has multiple viable solutions, search space of creating a solution for a given goal in a given constrained infrastructure is large and, choosing an optimal image processing solution is an effort, time and intellect intensive endeavor. The present disclosure models image processing goals in abstract task categories and externalizes domain knowledge as an object model and a set of rules defined over it. Rules codify task template selection criteria and algorithm selection criteria for implementing a task. Recommendations are provided to choose suitable algorithm (s) for carrying out image processing tasks. Effects of algorithm execution are recorded for subsequent tasks recommendations. The stored state of execution and trace information enables rerouting the execution and knowledge upgrade.

Abstract: Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.

Abstract: State of the art techniques in the domain of video analysis have limitations in terms of capability to capture spatio-temporal representation. This limitation in turn affects interpretation of video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to capture spatio-temporal representation for video reconstruction and analysis. The method presents different architecture variations using three main deep network components: 2D convolution units, 3D convolution units and long short-term memory (LSTM) units for video reconstruction and analysis. These variations are trained for learning the spatio-temporal representation of the videos in order to generate a pre-trained video analysis module. By understanding the advantages and disadvantages of different architectural configurations, a novel architecture is designed for video reconstruction.

Abstract: State of the art techniques in the domain of video analysis have limitations in terms of capability to extract the spatial and temporal information. This limitation in turn affects interpretation of the video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to extract spatio-temporal information from a video being analyzed. The system uses a neural network architecture which has multiple layers to extract spatial and temporal information from the video being analyzed. The method of training the neural network that extracts a micro-scale information from a latent representation of the video is presented. This is generated using an attention network, which is then used to extract spatio-temporal information corresponding to the collected video, which is then used in multiple video analysis applications such as searching actions in videos, action detection and localization.

Abstract: State of the art image processing techniques such as background subtraction, and Convolutional Neural Network based approaches, when used for change detection, fail to support certain datasets. The disclosure herein generally relates to semantic change detection, and, more particularly, to a method and system for semantic change detection using a deep neural network feature correlation approach. An adaptive correlation layer is used by the system, which determines extent of computation required at pixel level, based on amount of information at pixels, and uses this information in further computation done for the semantic change detection. Information on the determined extent of computation required is then used to extract semantic features, which is then used to compute one or more correlation maps between the at least one feature map of a test image and corresponding reference image. Further the semantic changes are determined from the one or more correlation maps.

Abstract: Conventionally, systems and methods have been provided for manual annotation of anatomical landmarks in digital radiography (DR) images. Embodiments of the present disclosure provides system and method for anatomical landmark detection and identification from DR images containing severe skeletal deformations. More specifically, motion artefacts and exposure are filtered from an input DR image to obtain a pre-processed DR image and probable/candidate anatomical landmarks comprised therein are identified. These probable candidate anatomical landmarks are assigned a score. A subset of the candidate anatomical landmarks (CALs) is selected as accurate anatomical landmarks based on comparison of the score with a pre-defined threshold performed by a trained classifier. Position of remaining CALs may be fine-tuned for classification thereof as accurate anatomical landmarks or missing anatomical landmarks.

Abstract: This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.

Abstract: This disclosure relates to automatic creation of an image processing pipeline that has a dependency on image processing domain experts. Given the image processing problem has multiple viable solutions, search space of creating a solution for a given goal in a given constrained infrastructure is large and, choosing an optimal image processing solution is an effort, time and intellect intensive endeavor. The present disclosure models image processing goals in abstract task categories and externalizes domain knowledge as an object model and a set of rules defined over it. Rules codify task template selection criteria and algorithm selection criteria for implementing a task. Recommendations are provided to choose suitable algorithm (s) for carrying out image processing tasks. Effects of algorithm execution are recorded for subsequent tasks recommendations. The stored state of execution and trace information enables rerouting the execution and knowledge upgrade.

Abstract: This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.

Abstract: State of the art image processing techniques such as background subtraction, and Convolutional Neural Network based approaches, when used for change detection, fail to support certain datasets. The disclosure herein generally relates to semantic change detection, and, more particularly, to a method and system for semantic change detection using a deep neural network feature correlation approach. An adaptive correlation layer is used by the system, which determines extent of computation required at pixel level, based on amount of information at pixels, and uses this information in further computation done for the semantic change detection. Information on the determined extent of computation required is then used to extract semantic features, which is then used to compute one or more correlation maps between the at least one feature map of a test image and corresponding reference image. Further the semantic changes are determined from the one or more correlation maps.

Abstract: This disclosure relates generally to a method and a system for frame stitching based image construction for an indoor environment. The method enables constructing an image of a scene by stitching a plurality of key frames identified from a plurality of image frames captured by a mobile imaging device. The method overcomes multiple challenges posed by the indoor environment, effectively providing clean stitching of the key frames to construct the image of the scene. The method provides image stitching approach that combines visual data from the mobile imaging device and an inertial sensor from an Inertial Measurement Unit (IMU) mounted on the mobile imaging device, with a feedback for error correction to generate robust stitching outputs in indoor scenario.

Abstract: The disclosure herein generally relate to scene change detection, and, more particularly, to use of Unmanned Vehicle (UV) to inspect a scene and perform a scene change detection using UVs. When a UV performs visual inspection of an area or an object, due to various factors, such as but not limited to environmental factors, and movement of object and/or the UV, image of the area/object captured by the drone lacks clarity, which in turn makes detection of any object a difficult task. The UV disclosed herein uses a multi scale super pixel technique for visual change detection, in order to solve the aforementioned issues. In an embodiment, the UV captures an image, identifies a reference image that matches the captured image, and generates a change map. The multi-scale super pixel analysis is then performed on this change map to detect changes between the captured image and the reference image.

Abstract: A system and method for identifying real time change in a scene of an unknown environment using an unmanned vehicle. In the context of unmanned vehicle navigation, it is critical to calculate the saliency map in real time and employ them in scene understanding. This will reduce the search space and ensure that the process is quicker. A domain specific ontology is created and a saliency model is developed. The saliency model detects key domain specific regions of interest in two consecutive images. The regions of interest is used for registration and change detection. The system is detecting the change by using visual saliency as an abstract feature that is developed in the environment. Probability of change is derived using the salient maps of the two images.

Abstract: This disclosure relates generally to image processing, and more particularly to method and system for image reconstruction using deep dictionary learning (DDL). The system collects the degraded image as test image and processes the test image to extract sparse features from the test image, at different levels, using dictionaries. The extracted sparse features and data from the dictionaries are used by the system to reconstruct the HR image corresponding to the test image.

Abstract: System and method for monitoring motor recovery in a post-stroke treatment is disclosed. Motor activity data corresponding to limbs movement is collected during an acute stroke period. The motor activity data is transmitted to at least one base station and an activity value for each arm of two arms is calculated in a predefined time window by using the motor activity data. The activity value for each arm is compared thus providing the monitoring of the motor activity.

Abstract: A system for identification of one or more neuro-cardiological disturbances of a subject in a post-acute stroke treatment and in the intensive care setting is provided. The system includes a wearable device configured for capturing data associated with a neural and heart activity. The wearable device includes a first electrode and a second electrode configured for measuring an electrical activity of a heart to generate a first signal. The wearable device also includes an accelerometer configured for detecting and acquiring neural accelerometry data to generate a second signal. Moreover, the system includes a processor with a memory.

Abstract: This disclosure relates generally to systems and methods for data acquisition and asset inspection in presence of magnetic interference. Data acquisition and assets inspection systems in many infrastructures such as railway, power line, and bridges provide inaccurate results in presence of magnetic interference. The proposed system and method proposes UAV based navigation through a dynamic correction path to inspect one or more assets in one or more infrastructures. A plurality of sensors are integrated with the UAV to acquire images of the one or more assets in presence of magnetic field. The acquired images are further processed to segment and detect anomalies in one or more parts of the one or more assets. The detected anomalies are further classified as potential anomalies and non-potential anomalies. The proposed method provides accurate results with reduced processing time.