Multi-object tracking in wide-area motion imagery (WAMI) is facilitating great interest in the field of image processing that leads to numerous real-world applications. Among them, aircraft and unmanned aerial vehicles (UAV) with real-time robust visual trackers for long-term aerial maneuvering are currently attracting attention and have remarkably broadened the scope of applications of object tracking. In this paper, we present a novel attention-based feature fusion strategy, which effectively combines the template and searching region features. Our results demonstrate the efficacy of the proposed system on CLIF and UNICORN datasets.

We propose a modified U-Net architecture incorporating the residues from successive layers for the extraction of features in subsequent layers. The new Residues in Succession U-Net model is evaluated for blood vessel segmentation in retinal images on a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE). The testing and evaluation results shows improved performance when compared to U-Net and R2U-Net in the same experimentation setup. A nonlinear image enhancement strategy is employed to improve the fine details in the images so that the network will be able to capture more information in further processing.

This Conference Presentation, “Towards a deep-learning aided point cloud labeling suite,” was recorded at SPIE Photonics West held in San Francisco, California, United States

This Conference Presentation, “Learning classical image registration features using a deep learning architecture,” was recorded at SPIE Photonics West held in San Francisco, California, United States

This Conference Presentation, “Deep neural machine for multimodal information fusion,” was recorded at SPIE Photonics West held in San Francisco, California, United States