Abstract: Systems and methods for a dense surface reconstruction of an object using graph signal processing is provided. None of the traditional systems and methods provide for a dense or three-dimensional surface reconstruction of objects by resolving ? ambiguity. The embodiments of the proposed disclosure provide for resolving ? ambiguity by identifying, from one or more sparse three-dimensional shapes extracted, a first set of azimuth values corresponding to a first region of the object; constructing, using a phase angle, a graph capturing a relational structure between the first set of azimuth values and a second set of azimuth values to be estimated; obtaining, a Graph Fourier Transform (GFT) matrix corresponding to the constructed graph; and estimating, from the GFT matrix and the first set of azimuth values, the second set of azimuth values corresponding to a second region of the object by the graph signal processing technique.

Abstract: Systems and methods for reconstructing super-resolution images under total aliasing based upon translation values. Traditional systems and methods provide for extracting super-resolution (SR) image/s from low-resolution (LR) image/s but include aliasing as a background noise which degrades the performance or include aliasing into an image model which leads to an enormously high computational complexity. Embodiments of the present disclosure provide for reconstructing super-resolution images based upon translation values by taking aliasing in consideration by capturing a set of LR images, estimating, using a Fast Fourier Transformation, a set of translation values based upon the set of LR images, obtaining, using a multi-signal classification technique, one or more optimized frequency spectrums based upon the set of translation values and reconstructing one or more SR images based upon the set of translation values.

Abstract: Detecting sound sources in a physical space-of-interest is challenging due to strong ego-noise from micro aerial vehicles (MAVs)? propeller units, which is both wideband and non-stationary. The present subject matter discloses a system and method for acoustic source localization with aerial drones. In an embodiment, a wideband acoustic signal is received from an aerial drone. Further, the wideband acoustic signal is splitted into multiple narrow sub-bands having cells. Moreover, from a measurement position corresponding to each of the multiple narrow sub-bands, power in each of the cells is measured by forming a beam to each of the cells. In addition, intra-band and inter measurement fusion of the measured power at each of the cells is performed. Also, geo-location of an acoustic source corresponding to the wideband acoustic signal is identified upon performing intra-band and inter measurement fusion of the measured power.

Abstract: This disclosure relates generally to image processing, and more particularly to generate a high resolution image from multiple low resolution images. In an embodiment, the system collects a plurality of low resolution images as input, processes the collected images, and generates a high resolution image as output. During this process, the system pre-processes the collected low resolution images, and during this process, at least one characteristic of each of the low resolution image is changed with respect to a reference image. After the pre-processing stage, the images are then enhanced based on a plurality of final enhancement factors that are dynamically determined. Further, the enhanced images are fused to generate the high resolution image.

Abstract: This disclosure relates generally to inventory management, and more particularly to system and method for airborne shelf inspection and inventor management. When a UAV has to be navigated based on navigation information embedded in visual markers on different items in the inventory, and if one or more of the visual markers are not completely visible due to occlusion, then the UAV automatically recovers data that is missing due to the occlusion, and accordingly navigates the UAV.